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1084 products in 119 categories |
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NWS Alerts (Coastal Hazards)
[NWS-Alerts-Coastal-Hazards]
NWS Alerts (Coastal Hazards)
NWS Alerts (Coastal Hazards)
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NWS Alerts (Fire Weather)
[NWS-Alerts-Fire-Weather]
NWS Alerts (Fire Weather)
NWS Alerts (Fire Weather)
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NWS Alerts (Non-Weather Emergencies)
[NWS-Alerts-Non-Weather-Emergencies]
NWS Alerts (Non-Weather Emergencies)
NWS Alerts (Non-Weather Emergencies)
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NWS Alerts (Non Convective)
[NWS-Alerts-Non-Convective]
NWS Alerts (Non Convective)
NWS Alerts (Non Convective)
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NWS Alerts (Winter Weather)
[NWS-Alerts-Winter-Weather]
NWS Alerts (Winter Weather)
NWS Alerts (Winter Weather)
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Fire Radiative Power VIIRS I-band - GINA
[AFIMG-Points-GINA]
VIIRS 375m I-band high spatial resolution imagery provides a greaterresponse over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime...
VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software at GINA.
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MIRS RainRate - Alaska (GINA)
[MIRS-RainRate-AK]
With inputs from the ATMS (Advanced Technology Microwave Sounder) sensoraboard JPSS satellites, the rainfall rate product from the Microwave Integrated Retrieval System (MIRS) identifies the intensity of rain at the...
With inputs from the ATMS (Advanced Technology Microwave Sounder) sensor aboard JPSS satellites, the rainfall rate product from the Microwave Integrated Retrieval System (MIRS) identifies the intensity of rain at the instant the satellite is passing over the area. It is derived from three vertically integrated MIRS products: Cloud Liquid Water (CLW), Rain Water Path (RWP), and Ice Water Path (IWP), taking advantage of the physical relationship found between atmospheric hydrometeor amounts and surface rain rate.
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VIIRS Fire RGB (GINA) DB Alaska
[DayLandCloudFire-RGB-GINA]
This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87umchannel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic...
This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic Information Network of Alaska (GINA).
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VIIRS Fire Temp RGB (GINA) DB Alaska
[FireTemperature-RGB-GINA]
This RGB is created by assigning the VIIRS 3.74um channel to red, 2.25umchannel to green, and the 1.61um channel to blue. It is used to assess fire intensity and size, with fires ranging from red (lowest) to yellow to white...
This RGB is created by assigning the VIIRS 3.74um channel to red, 2.25um channel to green, and the 1.61um channel to blue. It is used to assess fire intensity and size, with fires ranging from red (lowest) to yellow to white (hottest or biggest). These data are produced by the Geographic Information Network of Alaska (GINA).
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2015 WI NAIP DOQQs
[NAIPWI2015fp]
This layer displays the coverage footprints for the 2015 Wisconsin NAIPaerial photography. Right-click probe allows downloads of source imagery.
This layer displays the coverage footprints for the 2015 Wisconsin NAIP aerial photography. Right-click probe allows downloads of source imagery.
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Infrared 6 inch Imagery of Madison
[madisonir]
Infrared 6 inch Imagery of Madison
Infrared 6 inch Imagery of Madison
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NAIP WI
[NAIPWI]
National Agricultural Imagery Program aerial photography from the WisconsinFarm Service Agency (WI-FSA) of the USDA.
National Agricultural Imagery Program aerial photography from the Wisconsin Farm Service Agency (WI-FSA) of the USDA.
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NAIP WI Color Infrared
[NAIPWICIR]
National Agricultural Imagery Program aerial photography from the WisconsinFarm Service Agency (WI-FSA) of the USDA (Color Infrared)
National Agricultural Imagery Program aerial photography from the Wisconsin Farm Service Agency (WI-FSA) of the USDA (Color Infrared)
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WI Lake Clarity
[LakesTSI]
These data represent the estimated clarity, or transparency, of the 8,000largest of those lakes as measured by satellite remote sensing (Landsat).
These data represent the estimated clarity, or transparency, of the 8,000 largest of those lakes as measured by satellite remote sensing (Landsat).
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WISCLAND 1993
[wiscland]
In 1993 a team of researchers from University of Wisconsin-Madison (ERSC)and the Wisconsin DNR developed WISCLAND, the first satellite-derived land cover map of Wisconsin. The UW-Madison (SCO) and the DNR partnered on a...
In 1993 a team of researchers from University of Wisconsin-Madison (ERSC) and the Wisconsin DNR developed WISCLAND, the first satellite-derived land cover map of Wisconsin. The UW-Madison (SCO) and the DNR partnered on a project to produce an updated land cover map of Wisconsin. The resulting dataset, known as Wiscland 2.0, was completed in August 2016.
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Wisconsin LIDAR Hillshade
[wi-hillshade]
WisconsinView is a remote sensing consortium and member of AmericaView.org.These Wisconsin lidar data sets were collected by aircraft and processed by state and county agencies. These data are hosted by WisconsinView and...
WisconsinView is a remote sensing consortium and member of AmericaView.org. These Wisconsin lidar data sets were collected by aircraft and processed by state and county agencies. These data are hosted by WisconsinView and visualized here with coordination and funding from the WI State Dept. of Administration, Geographic Information Office and NOAA"s coastal management program.
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WI USGS Landsat Poster
[wilandsat]
This is a georeferenced poster from the USGS. The original source is:http://eros.usgs.gov/imagegallery/landsat-state-mosaics unfortunately the original poster imagery without graphics burned-in is not available.
This is a georeferenced poster from the USGS. The original source is: http://eros.usgs.gov/imagegallery/landsat-state-mosaics unfortunately the original poster imagery without graphics burned-in is not available.
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Copernicus LS Lake - Surface Temperature
[global-lakes-temperature]
The Copernicus Global Land Service – Lake Water products include lakesurface water temperature (LSWT). It describes the temperature of the lake surface, one important indicator of lake hydrology and biogeochemistry....
The Copernicus Global Land Service – Lake Water products include lake surface water temperature (LSWT). It describes the temperature of the lake surface, one important indicator of lake hydrology and biogeochemistry. Temperature trends observed over many years can be an indicator of how climate change affects the lake. LSWT is recognized internationally as an Essential Climate Variable (ECV) and complements the water quality information, in environmental monitoring of a large number of lakes globally.
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Copernicus LS Lake - Trophic State
[global-lakes-trophic]
The Copernicus Global Land Service – Lake Water products include anoptical characterization of ~4000 of the world"s largest inland water bodies from observations by Sentinel-3 OLCI (Ocean and Land Color...
The Copernicus Global Land Service – Lake Water products include an optical characterization of ~4000 of the world"s largest inland water bodies from observations by Sentinel-3 OLCI (Ocean and Land Color Instrument). This product represents estimated trophic state index (derived from phytoplankton biomass by proxy of chlorophyll-a). Production and delivery of the parameters are over 10-day intervals starting the 1st, 11th and 21st day of each month and mapped to a common global grid at 300m resolution. Trophic State Index data were produced by the Plymouth Marine Laboratory and Brockmann Consult for the Copernicus Global Land Operations Service under Copernicus Global Land Operations Lot 2.
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Copernicus LS Lake - Turbidity
[global-lakes-turbidity]
The Copernicus Global Land Service – Lake Water products include anoptical characterization of ~4000 of the world"s largest inland water bodies from observations by Sentinel-3 OLCI (Ocean and Land Color...
The Copernicus Global Land Service – Lake Water products include an optical characterization of ~4000 of the world"s largest inland water bodies from observations by Sentinel-3 OLCI (Ocean and Land Color Instrument). Production and delivery of the parameters are over 10-day intervals starting the 1st, 11th and 21st day of each month and mapped to a common global grid at 300m resolution. The turbidity of a lake describes water clarity, or whether sunlight can penetrate deeper parts of the lake. Turbidity often varies seasonally, both with the discharge of rivers and growth of phytoplankton (algae and cyanobacteria)
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Great Lakes Surface Environmental Analysis
[GLERL-GLSEAimage]
Great Lakes Surface Environmental Analysis (GLSEA) from GLERL. For moreinfo see: http://coastwatch.glerl.noaa.gov/glsea/doc
Great Lakes Surface Environmental Analysis (GLSEA) from GLERL. For more info see:
http://coastwatch.glerl.noaa.gov/glsea/doc
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NOAA MSL12 DINEOF Gap-filled: Chlor-a
[CW-dineof-chlora]
This Chlorophyll-a product is part of the NOAA MSL12 multi-sensor DINEOFglobal gap-filled collection. Ocean Color satellite sensors measure visible light at specific wavelengths which leave the surface of the ocean...
This Chlorophyll-a product is part of the NOAA MSL12 multi-sensor DINEOF global gap-filled collection. Ocean Color satellite sensors measure visible light at specific wavelengths which leave the surface of the ocean and arrive at the top of the atmosphere where the sensor is located. From these visible, near-IR, and shortwave IR spectral radiance measurements are used to derive ocean properties such as the concentration of chlorophyll-a, which is the green pigment responsible for photosynthesis and therefore an indicator of the amount of phytoplankton biomass in the ocean water. These global gap-free data are generated using the data interpolating empirical orthogonal function (DINEOF) method (Liu and Wang, 2022). The data that go into this product currently come from 3 instruments: the VIIRS sensor aboard the SNPP satellite and the NOAA-20 satellite plus the Ocean and Land Colour Instrument (OLCI) on the Sentinel 3A satellite from the Copernicus program of the European Union.
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NOAA MSL12 DINEOF Gap-filled: Kd(490)
[CW-dineof-kd490]
This diffuse attenuation coefficient 490 nm Kd(490) product is part of theNOAA MSL12 multi-sensor DINEOF global gap-filled collection. Ocean Color satellite sensors measure visible light at specific wavelengths which leave...
This diffuse attenuation coefficient 490 nm Kd(490) product is part of the NOAA MSL12 multi-sensor DINEOF global gap-filled collection. Ocean Color satellite sensors measure visible light at specific wavelengths which leave the surface of the ocean and arrive at the top of the atmosphere where the sensor is located. From these visible, near-IR, and shortwave IR spectral radiance measurements are used to derive ocean properties that can be related to water turbidity and clarity. These global gap-free data are generated using the data interpolating empirical orthogonal function (DINEOF) method (Liu and Wang, 2022). The data that go into this product currently come from 3 instruments: the VIIRS sensor aboard the SNPP satellite and the NOAA-20 satellite plus the Ocean and Land Colour Instrument (OLCI) on the Sentinel 3A satellite from the Copernicus program of the European Union.
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NOAA MSL12 DINEOF Gap-filled: SPM
[CW-dineof-spm]
This suspended particulate matter (SPM) product is part of the NOAA MSL12multi-sensor DINEOF global gap-filled collection. Ocean Color satellite sensors measure visible light at specific wavelengths which leave the...
This suspended particulate matter (SPM) product is part of the NOAA MSL12 multi-sensor DINEOF global gap-filled collection. Ocean Color satellite sensors measure visible light at specific wavelengths which leave the surface of the ocean and arrive at the top of the atmosphere where the sensor is located. From these visible, near-IR, and shortwave IR spectral radiance measurements are used to derive ocean properties that can be related to water turbidity and clarity. These global gap-free data are generated using the data interpolating empirical orthogonal function (DINEOF) method (Liu and Wang, 2022). The data that go into this product currently come from 3 instruments: the VIIRS sensor aboard the SNPP satellite and the NOAA-20 satellite plus the Ocean and Land Colour Instrument (OLCI) on the Sentinel 3A satellite from the Copernicus program of the European Union.
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Ocean Color - CCI
[ESA-daily-chlora]
The Ocean Colour project is currently in its third phase which started inApril 2019 and has recently released the v5.0 dataset (November 2020) to the international science community following internal quality control and...
The Ocean Colour project is currently in its third phase which started in April 2019 and has recently released the v5.0 dataset (November 2020) to the international science community following internal quality control and analysis. This follows Phase 2 which ran from 2015-2018, and the original phase 1 project.
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UNEP GEMS - Global Freshwater Quality Database
[GEMS-daily]
The Global Freshwater Quality Database GEMStat providesscientifically-sound data and information on the state and trend of global inland water quality. As operational part of the GEMS/Water Programme of...
The Global Freshwater Quality Database GEMStat provides scientifically-sound data and information on the state and trend of global inland water quality. As operational part of the GEMS/Water Programme of the United Nations Environment Programme (UNEP), GEMStat is hosted by the GEMS/Water Data Centre (GWDC) within the International Centre for Water Resources and Global Change (ICWRGC) in Koblenz, Germany.
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Cloud Top Cooling targets
[CIMSS-CTCtargets]
CIMSS-Cloud Top Cooling targets
CIMSS-Cloud Top Cooling targets
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Mountains Obscured Advisory
[AIRMET-MTN]
AIRMET-Mountain Obscured Advisory
AIRMET-Mountain Obscured Advisory
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Volcanic Ash Adv plumes
[VAA]
Volcanic Ash Advisories: Ash Clouds
Volcanic Ash Advisories: Ash Clouds
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True Color Clear View
[BRDF]
MODIS Clear View ConUS Composite. BRDF (Bidirectional ReluctanceDistribution Function) is a 16-day cloud-free composite.
MODIS Clear View ConUS Composite. BRDF (Bidirectional Reluctance Distribution Function) is a 16-day cloud-free composite.
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Gridded NUCAPS Total Column Carbon Monoxide (CO)
[nucaps-grid-co-total]
Total Column Carbon Monoxide represents the amount of carbon monoxide inthe atmospheric column in units of 10e18 molecules/cm2. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition...
Total Column Carbon Monoxide represents the amount of carbon monoxide in the atmospheric column in units of 10e18 molecules/cm2. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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Gridded NUCAPS Cloud Top Fraction - Lower
[nucaps-grid-ctf-lower]
NUCAPS retrieves Cloud Top Fraction, or the portion of each field of viewcovered by cloud, on 2 layers (e.g., Upper and Lower). The upper top cloud fraction is the top-most retrievable layer of clouds. The lower top cloud...
NUCAPS retrieves Cloud Top Fraction, or the portion of each field of view covered by cloud, on 2 layers (e.g., Upper and Lower). The upper top cloud fraction is the top-most retrievable layer of clouds. The lower top cloud fraction represents the percentage of cloud cover that isn’t obscured by the upper cloud deck. The numbers therefore can exceed 100%. Profiles are typically rejected when the cloud top fraction on either layer exceeds 80%. Cloud top fraction can be used to assess confidence in the retrieval. Retrievals presented with a ‘yellow’ color Quality Flag with a cloud top fraction below 80% may still be usable. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020 or https://vlab.noaa.gov/web/nasa-sport/gridded-nucaps for more information.
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Gridded NUCAPS Cloud Top Fraction - Upper
[nucaps-grid-ctf-upper]
NUCAPS retrieves Cloud Top Fraction, or the portion of each field of viewcovered by cloud, on 2 layers (e.g., Upper and Lower). The upper cloud top fraction is the top-most retrievable layer of clouds. The lower cloud top...
NUCAPS retrieves Cloud Top Fraction, or the portion of each field of view covered by cloud, on 2 layers (e.g., Upper and Lower). The upper cloud top fraction is the top-most retrievable layer of clouds. The lower cloud top fraction represents the percentage of cloud cover that isn’t obscured by the upper cloud deck. The numbers therefore can exceed 100%. Profiles are typically rejected when the cloud top fraction on either layer exceed 80%. Cloud top fraction can be used to assess confidence in the retrieval. Retrievals presented with a ‘yellow’ color Quality Flag with a cloud top fraction below 80% may still be usable. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020 or https://vlab.noaa.gov/web/nasa-sport/gridded-nucaps for more information.
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Gridded NUCAPS Cloud Top Pressure - Lower
[nucaps-grid-ctp-lower]
NUCAPS retrieves Cloud Top Pressure, or the air pressure at the top of thecloud in mb, on 2 layers (e.g., Upper and Lower). Cloud Top Pressure is an indication of the height of the cloud top in each of the two layers NUCAPS...
NUCAPS retrieves Cloud Top Pressure, or the air pressure at the top of the cloud in mb, on 2 layers (e.g., Upper and Lower). Cloud Top Pressure is an indication of the height of the cloud top in each of the two layers NUCAPS retrieves. The upper cloud top pressure is the top-most retrievable layer of clouds. The lower cloud top pressure represents the percentage of cloud cover that isn’t obscured by the upper cloud deck. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020 or https://vlab.noaa.gov/web/nasa-sport/gridded-nucaps for more information.
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Gridded NUCAPS Cloud Top Pressure - Upper
[nucaps-grid-ctp-upper]
NUCAPS retrieves Cloud Top Pressure, or the air pressure at the top of thecloud in mb, on 2 layers (e.g., Upper and Lower). Cloud Top Pressure is an indication of the height of the cloud top in each of the two layers NUCAPS...
NUCAPS retrieves Cloud Top Pressure, or the air pressure at the top of the cloud in mb, on 2 layers (e.g., Upper and Lower). Cloud Top Pressure is an indication of the height of the cloud top in each of the two layers NUCAPS retrieves. The upper cloud top pressure is the top-most retrievable layer of clouds. The lower cloud top pressure represents the percentage of cloud cover that isn’t obscured by the upper cloud deck. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020 or https://vlab.noaa.gov/web/nasa-sport/gridded-nucaps for more information.
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Mean Snow Duration 1988-2017
[mean-snow-cover-1988-2017]
Global 4 km Multisensor Automated Snow and Ice Maps (GMASI) developed atthe NOAA/NESDIS Center for Satellite Applications and Research (STAR). The main function of the GMASI is to routinely generate global continuous maps...
Global 4 km Multisensor Automated Snow and Ice Maps (GMASI) developed at the NOAA/NESDIS Center for Satellite Applications and Research (STAR). The main function of the GMASI is to routinely generate global continuous maps of snow and ice cover distribution from combined observations in the visible/infrared and in the microwave spectral bands from operational meteorological polar orbiting and geostationary satellites.
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NOAA20 VIIRS Sea Ice Concentration Global
[j01-sic]
The Sea Ice Concentration products uses threshold reflectance(temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km...
The Sea Ice Concentration products uses threshold reflectance (temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km resolution covered by ice. The product is available over oceans, seas and lakes only under clear-sky conditions that is determined by VIIRS cloud mask.
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NOAA20 VIIRS Sea Ice Temperature Global
[j01-ist]
The Sea Ice Temperature product uses a split window algorithm that isdependent on bands M15 (~11 um) and M16 (~12 um) along with satellite scan angle to come up with an atmospheric correction term that adjusts clear...
The Sea Ice Temperature product uses a split window algorithm that is dependent on bands M15 (~11 um) and M16 (~12 um) along with satellite scan angle to come up with an atmospheric correction term that adjusts clear window Brightness Temperature to come up with a final IST value that is at 750 m resolution and has been shown to be within 1.5 K of validation measurements. The product is available over all water bodies, including rivers under clear-sky conditions that is determined by VIIRS cloud mask.
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NOAA20 VIIRS Sea Ice Thickness Global
[j01-ithk]
The Sea Ice Thickness product uses a one-dimensional thermodynamic icemodel (OTIM) The OTIM is based on the surface energy balance but does not directly use any channel data. Instead, it takes into account variables...
The Sea Ice Thickness product uses a one-dimensional thermodynamic ice model (OTIM) The OTIM is based on the surface energy balance but does not directly use any channel data. Instead, it takes into account variables such as VIIRS ice surface temperature and the VIIRS cloud mask to determine sea and lake ice thickness. The product is at 750 m resolution and available over all water bodies, including rivers under clear sky conditions that is determined by VIIRS cloud mask.
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SNPP VIIRS SEA Ice Concentration Global
[snpp-sic]
The Sea Ice Concentration products uses threshold reflectance(temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km...
The Sea Ice Concentration products uses threshold reflectance (temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km resolution covered by ice. The product is available over oceans, seas and lakes only under clear-sky conditions that is determined by VIIRS cloud mask.
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SNPP VIIRS Sea Ice Temperature Global
[snpp-ist]
The Sea Ice Temperature product uses a split window algorithm that isdependent on bands M15 (~11 um) and M16 (~12 um) along with satellite scan angle to come up with an atmospheric correction term that adjusts clear...
The Sea Ice Temperature product uses a split window algorithm that is dependent on bands M15 (~11 um) and M16 (~12 um) along with satellite scan angle to come up with an atmospheric correction term that adjusts clear window Brightness Temperature to come up with a final IST value that is at 750 m resolution and has been shown to be within 1.5 K of validation measurements. The product is available over all water bodies, including rivers under clear-sky conditions that is determined by VIIRS cloud mask.
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SNPP VIIRS Sea Ice Thickness Global
[snpp-ithk]
The Sea Ice Thickness product uses a one-dimensional thermodynamic icemodel (OTIM) The OTIM is based on the surface energy balance but does not directly use any channel data. Instead, it takes into account variables...
The Sea Ice Thickness product uses a one-dimensional thermodynamic ice model (OTIM) The OTIM is based on the surface energy balance but does not directly use any channel data. Instead, it takes into account variables such as VIIRS ice surface temperature and the VIIRS cloud mask to determine sea and lake ice thickness. The product is at 750 m resolution and available over all water bodies, including rivers under clear sky conditions that is determined by VIIRS cloud mask.
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FireWork Smoke Forecast - PM2.5 Column Diff
[RAQDPS-COL-Diff-25]
Canada’s Wildfire Smoke Prediction System (FireWork) produces daily smokeforecast maps. This map represents small particles (PM2.5), the whole atmosphere compressed (column), and the smoke-only after subtracting the...
Canada’s Wildfire Smoke Prediction System (FireWork) produces daily smoke forecast maps. This map represents small particles (PM2.5), the whole atmosphere compressed (column), and the smoke-only after subtracting the atmospheric background (diff). Smoke from wildfires in forests and grasslands can be a major source of air pollution for Canadians. The fine particles in the smoke can be a serious risk to health, particularly for children, seniors and those with heart or lung disease. Because smoke may be carried thousands of kilometers downwind, distant locations can be affected almost as severely as areas close to the fire. To help Canadians be better prepared, wildfire smoke forecast maps are available through the Government of Canada’s FireWork system. FireWork is an air quality prediction system that indicates how smoke from wildfires is expected to move across North America over the next 72 hours.
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HRRR CONUS/AK Near Surface Smoke
[HRRR-smoke-surface]
Operational model output from NECP. Developed at the NOAA Earth SystemResearch Laboratory High Resolution Rapid Refresh (HRRR) Surface Smoke forecast model, uses VIIRS inputs.
Operational model output from NECP. Developed at the NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Surface Smoke forecast model, uses VIIRS inputs.
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HRRR CONUS/AK Vertically Integrated Smoke
[HRRR-smoke-column]
Operational smoke model output from NCEP developed at the NOAA Earth SystemResearch Laboratory High Resolution Rapid Refresh (HRRR) Vertically Integrated Smoke forecast model, uses VIIRS inputs.
Operational smoke model output from NCEP developed at the NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Vertically Integrated Smoke forecast model, uses VIIRS inputs.
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RAP North America Near Surface Smoke
[RAP-smoke-surface]
The Rapid Refresh (RAP) is the continental-scale NOAA hourly-updatedassimilation/modeling system operational at NCEP. RAP covers North America and is comprised primarily of a numerical forecast model and an...
The Rapid Refresh (RAP) is the continental-scale NOAA hourly-updated assimilation/modeling system operational at NCEP. RAP covers North America and is comprised primarily of a numerical forecast model and an analysis/assimilation system to initialize that model. The RAP has a resolution of 13.5km and includes smoke forecast variables derived in part from VIIRS satellite inputs. RAP is complemented by the higher-resolution 3km High-Resolution Rapid Refresh (HRRR) model, which is also updated hourly and covering a smaller geographic domain.
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RAP North America Vertically Integrated Smoke
[RAP-smoke-column]
The Rapid Refresh (RAP) is the continental-scale NOAA hourly-updatedassimilation/modeling system operational at NCEP. RAP covers North America and is comprised primarily of a numerical forecast model and an...
The Rapid Refresh (RAP) is the continental-scale NOAA hourly-updated assimilation/modeling system operational at NCEP. RAP covers North America and is comprised primarily of a numerical forecast model and an analysis/assimilation system to initialize that model. The RAP has a resolution of 13.5km and includes smoke forecast variables derived in part from VIIRS satellite inputs. RAP is complemented by the higher-resolution 3km High-Resolution Rapid Refresh (HRRR) model, which is also updated hourly and covering a smaller geographic domain.
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Fire Hazards (Valid)
[XREDFLAG]
The National Weather Service issues a variety of Weather warnings, watchesand advisories. The event type is indicated on the map by different colors. This product contains Wildland Fire Weather Hazards VALID for a 48hr Window...
The National Weather Service issues a variety of Weather warnings, watches and advisories. The event type is indicated on the map by different colors. This product contains Wildland Fire Weather Hazards VALID for a 48hr Window spanning from the previous 24hrs to 24hrs in the future at 1hr increments
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Fire Weather Outlook - Categorical (color map)
[SPC-FireOutlook-CATG-cmap]
The Fire Weather Outlooks are intended to delineate areas of thecontinental U.S. where pre-existing fuel conditions, combined with forecast weather conditions, will result in a significant threat for the ignition...
The Fire Weather Outlooks are intended to delineate areas of the continental U.S. where pre-existing fuel conditions, combined with forecast weather conditions, will result in a significant threat for the ignition and/or spread of wildfires. This product is designed for use in the NWS, as well as other federal, state, and local government agencies.
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Fire Weather Outlook Day1
[SPCfwday1]
Fire Weather Outlook Day1 (Category)
Fire Weather Outlook Day1 (Category)
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Fire Weather Outlook Day2
[SPCfwday2]
Fire Weather Outlook Day2 (Category)
Fire Weather Outlook Day2 (Category)
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Fire Radiative Power VIIRS I-band - GINA
[AFIMG-Points-GINA]
VIIRS 375m I-band high spatial resolution imagery provides a greaterresponse over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime...
VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software at GINA.
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VIIRS Fire Radiative Power I-band DB ConUS
[AFIMG-Points]
VIIRS 375m I-band high spatial resolution imagery provides a greaterresponse over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime...
VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software.
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VIIRS Fire RGB (GINA) DB Alaska
[DayLandCloudFire-RGB-GINA]
This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87umchannel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic...
This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic Information Network of Alaska (GINA).
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VIIRS Fire Temp RGB (GINA) DB Alaska
[FireTemperature-RGB-GINA]
This RGB is created by assigning the VIIRS 3.74um channel to red, 2.25umchannel to green, and the 1.61um channel to blue. It is used to assess fire intensity and size, with fires ranging from red (lowest) to yellow to white...
This RGB is created by assigning the VIIRS 3.74um channel to red, 2.25um channel to green, and the 1.61um channel to blue. It is used to assess fire intensity and size, with fires ranging from red (lowest) to yellow to white (hottest or biggest). These data are produced by the Geographic Information Network of Alaska (GINA).
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NOAA-20 VIIRS Daily Fires - NASA (Image)
[FIRMS-Fire-Image-j01-daily]
NASA"s Fire Information for Resource Management System (FIRMS) distributesNear Real-Time (NRT) fire/thermal anomaly data within 3 hours of satellite observation from the Visible Infrared Imaging Radiometer Suite (VIIRS)...
NASA"s Fire Information for Resource Management System (FIRMS) distributes Near Real-Time (NRT) fire/thermal anomaly data within 3 hours of satellite observation from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the joint NASA/NOAA Suomi National Polar orbiting Partnership (Suomi NPP) and NOAA-20 satellites. FIRMS is part of NASA"s Land, Atmosphere Near real-time Capability for EOS (LANCE).
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NOAA-20 VIIRS Daily Fires - NASA (Points)
[FIRMS-Fire-Points-j01-daily]
NASA"s Fire Information for Resource Management System (FIRMS) distributesNear Real-Time (NRT) fire/thermal anomaly data within 3 hours of satellite observation from the Visible Infrared Imaging Radiometer Suite (VIIRS)...
NASA"s Fire Information for Resource Management System (FIRMS) distributes Near Real-Time (NRT) fire/thermal anomaly data within 3 hours of satellite observation from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the joint NASA/NOAA Suomi National Polar orbiting Partnership (Suomi NPP) and NOAA-20 satellites. FIRMS is part of NASA"s Land, Atmosphere Near real-time Capability for EOS (LANCE).
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NOAA-20 VIIRS Daily Fires - NOAA (Image)
[NOAA-Fire-Image-j01-daily]
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data setproduced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System...
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.
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NOAA-20 VIIRS Daily Fires - NOAA (Points)
[NOAA-Fire-Points-j01-daily]
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data setproduced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System...
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.
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NOAA-20 VIIRS Swath Fires - NOAA (Image)
[NOAA-Fire-Image-j01]
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data setproduced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System...
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.
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NOAA-20 VIIRS Swath Fires - NOAA (Points)
[NOAA-Fire-Points-j01]
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data setproduced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System...
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.
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SNPP VIIRS Daily Fires - NASA (Image)
[FIRMS-Fire-Image-npp-daily]
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data setproduced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System...
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.
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SNPP VIIRS Daily Fires - NASA (Points)
[FIRMS-Fire-Points-npp-daily]
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data setproduced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System...
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.
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SNPP VIIRS Daily Fires - NOAA (Image)
[NOAA-Fire-Image-npp-daily]
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data setproduced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System...
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.
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SNPP VIIRS Daily Fires - NOAA (Points)
[NOAA-Fire-Points-npp-daily]
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data setproduced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System...
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.
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SNPP VIIRS Swath Fires - NOAA (Image)
[NOAA-Fire-Image-npp]
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data setproduced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System...
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.
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SNPP VIIRS Swath Fires - NOAA (Points)
[NOAA-Fire-Points-npp]
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data setproduced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System...
The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.
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VIIRS Fire Radiative Power I-band DB ConUS
[AFIMG-Points]
VIIRS 375m I-band high spatial resolution imagery provides a greaterresponse over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime...
VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software.
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FireWork Smoke Forecast - PM2.5 Column Diff
[RAQDPS-COL-Diff-25]
Canada’s Wildfire Smoke Prediction System (FireWork) produces daily smokeforecast maps. This map represents small particles (PM2.5), the whole atmosphere compressed (column), and the smoke-only after subtracting the...
Canada’s Wildfire Smoke Prediction System (FireWork) produces daily smoke forecast maps. This map represents small particles (PM2.5), the whole atmosphere compressed (column), and the smoke-only after subtracting the atmospheric background (diff). Smoke from wildfires in forests and grasslands can be a major source of air pollution for Canadians. The fine particles in the smoke can be a serious risk to health, particularly for children, seniors and those with heart or lung disease. Because smoke may be carried thousands of kilometers downwind, distant locations can be affected almost as severely as areas close to the fire. To help Canadians be better prepared, wildfire smoke forecast maps are available through the Government of Canada’s FireWork system. FireWork is an air quality prediction system that indicates how smoke from wildfires is expected to move across North America over the next 72 hours.
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HRRR CONUS/AK Near Surface Smoke
[HRRR-smoke-surface]
Operational model output from NECP. Developed at the NOAA Earth SystemResearch Laboratory High Resolution Rapid Refresh (HRRR) Surface Smoke forecast model, uses VIIRS inputs.
Operational model output from NECP. Developed at the NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Surface Smoke forecast model, uses VIIRS inputs.
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HRRR CONUS/AK Vertically Integrated Smoke
[HRRR-smoke-column]
Operational smoke model output from NCEP developed at the NOAA Earth SystemResearch Laboratory High Resolution Rapid Refresh (HRRR) Vertically Integrated Smoke forecast model, uses VIIRS inputs.
Operational smoke model output from NCEP developed at the NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Vertically Integrated Smoke forecast model, uses VIIRS inputs.
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RAP North America Near Surface Smoke
[RAP-smoke-surface]
The Rapid Refresh (RAP) is the continental-scale NOAA hourly-updatedassimilation/modeling system operational at NCEP. RAP covers North America and is comprised primarily of a numerical forecast model and an...
The Rapid Refresh (RAP) is the continental-scale NOAA hourly-updated assimilation/modeling system operational at NCEP. RAP covers North America and is comprised primarily of a numerical forecast model and an analysis/assimilation system to initialize that model. The RAP has a resolution of 13.5km and includes smoke forecast variables derived in part from VIIRS satellite inputs. RAP is complemented by the higher-resolution 3km High-Resolution Rapid Refresh (HRRR) model, which is also updated hourly and covering a smaller geographic domain.
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RAP North America Vertically Integrated Smoke
[RAP-smoke-column]
The Rapid Refresh (RAP) is the continental-scale NOAA hourly-updatedassimilation/modeling system operational at NCEP. RAP covers North America and is comprised primarily of a numerical forecast model and an...
The Rapid Refresh (RAP) is the continental-scale NOAA hourly-updated assimilation/modeling system operational at NCEP. RAP covers North America and is comprised primarily of a numerical forecast model and an analysis/assimilation system to initialize that model. The RAP has a resolution of 13.5km and includes smoke forecast variables derived in part from VIIRS satellite inputs. RAP is complemented by the higher-resolution 3km High-Resolution Rapid Refresh (HRRR) model, which is also updated hourly and covering a smaller geographic domain.
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Wildland Fire Perimeters - Current
[WFIGS-Perimeters]
This product represents the best available perimeters for recent andongoing wildland fires in the United States. It is produced by the Wildland Fire Interagency Geospatial Services (WFIGS) Group and provides...
This product represents the best available perimeters for recent and ongoing wildland fires in the United States.
It is produced by the Wildland Fire Interagency Geospatial Services (WFIGS) Group and provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.
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Excessive Rainfall Outlook
[ERTA-NCEP]
Excessive Rainfall Outlook:
In the Excessive Rainfall Outlooks, theWeather Prediction Center (WPC) forecasts the probability that rainfall will exceed flash flood guidance at a point. Gridded FFG is provided by the...
Excessive Rainfall Outlook:
In the Excessive Rainfall Outlooks, the Weather Prediction Center (WPC) forecasts the probability that rainfall will exceed flash flood guidance at a point. Gridded FFG is provided by the twelve NWS River Forecast Centers (RFCs) whose service areas cover the lower 48 states. NCEP creates a national mosaic of FFG, whose 1, 3, and 6-hour values represent the amount of rainfall over those short durations which it is estimated would bring rivers and streams up to bankfull conditions. WPC estimates the likelihood that FFG will be exceeded by assessing environmental conditions (e.g. moisture content and steering winds), recognizing weather patterns commonly associated with heavy rainfall, and using a variety of deterministic and ensemble-based numerical model tools
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Flood Warnings Hydrological-VTEC (Issued)
[HVTEC]
For Flood Warnings (FLW) and follow up Flood Statements (FLS) at specificriver forecast points, the H-VTEC specifies the flood severity; immediate cause, timing of flood beginning, crest, and end; and how the flood...
For Flood Warnings (FLW) and follow up Flood Statements (FLS) at specific river forecast points, the H-VTEC specifies the flood severity; immediate cause,
timing of flood beginning, crest, and end; and how the flood compares to the flood of record.
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River Flood: ABI-daily
[River-Flood-ABI]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 5-min CONUS imagery since sunrise on the given day. These products are expected to be most useful in mid- and low-latitude locations.
CONUS region
Quick guide
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River Flood: ABI-daily (tsp)
[River-Flood-ABItsp]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 5-min CONUS imagery since sunrize through the given hour. These products are expected to be most useful in mid- and low-latitude locations.
CONUS region
Quick guide
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River Flood: ABI-hourly
[River-Flood-ABI-hourly]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 5-min CONUS imagery since sunrize through the given hour. These products are expected to be most useful in mid- and low-latitude locations.
CONUS region
Quick guide
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River Flood: ABI-hourly (tsp)
[River-Flood-ABItsp-hourly]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 5-min CONUS imagery since sunrize through the given hour. These products are expected to be most useful in mid- and low-latitude locations.
CONUS region
Quick guide
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River Flood: AHI
[RIVER-FLD-AHI]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 10-min imagery since sunrise on the given day. These products are expected to be most useful in mid- and low-latitude locations.
For more information visit: Here
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River Flood: Joint ABI/VIIRS
[RIVER-FLD-joint-ABI]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available ABI-full disk imagery and VIIRS imagery since sunrise on the given day.
For more information visit: Here
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River Flood: Joint AHI/VIIRS
[RIVER-FLD-joint-AHI]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available AHI-full disk imagery and VIIRS imagery since sunrise on the given day.
For more information visit: Here
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River Flood: 1 day VIIRS composite
[RIVER-FLDglobal-composite1]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 1 day.
For more information visit: Here
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River Flood: 5 day VIIRS composite
[RIVER-FLDglobal-composite]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 5 days.
For more information visit: Here
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River Flood: Alaska
[RIVER-FLDall-AP]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Alaska region
Quick guide
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River Flood: Alaska (transparent)
[RIVER-FLDtsp-AP]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Alaska region(Transparent flood-free land)
Quick guide
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River Flood: Global
[RIVER-FLDglobal]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Global(CSPP product)
Quick guide
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River Flood: Missouri Basin
[RIVER-FLDall-MB]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Missouri Basin
Quick guide
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River Flood: Missouri Basin (transparent)
[RIVER-FLDtsp-MB]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Missouri Basin(Transparent flood-free land)
Quick guide
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River Flood: North Central Basin
[RIVER-FLDall-NC]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North Central Basin
Quick guide
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River Flood: North Central Basin (transparent)
[RIVER-FLDtsp-NC]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North Central Basin(Transparent flood-free land)
Quick guide
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River Flood: North East Basin
[RIVER-FLDall-NE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North East Basin
Quick guide
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River Flood: North East Basin (transparent)
[RIVER-FLDtsp-NE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North East Basin(Transparent flood-free land)
Quick guide
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River Flood: North West
[RIVER-FLDall-NW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Northwest Region
Quick guide
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River Flood: North West (transparent)
[RIVER-FLDtsp-NW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Northwest Region(Transparent flood-free land)
Quick guide
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River Flood: South East
[RIVER-FLDall-SE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southeast Region
Quick guide
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River Flood: South East (transparent)
[RIVER-FLDtsp-SE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southeast Region(Transparent flood-free land)
Quick guide
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River Flood: South West
[RIVER-FLDall-SW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southwest Region
Quick guide
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River Flood: South West (tsp)
[RIVER-FLDtsp-SW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southwest Region(Transparent flood-free land)
Quick guide
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River Flood: US
[RIVER-FLDall-US]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
US
Quick guide
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River Flood: West Gulf Basin
[RIVER-FLDall-WG]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
West Gulf Basin
Quick guide
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River Flood: West Gulf Basin (transparent)
[RIVER-FLDtsp-WG]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
West Gulf Basin(Transparent flood-free land)
Quick guide
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VIIRS Floodwater Depth
[VIIRS-3Dflood]
VIIRS downscaling software is designed to downscale the VIIRS 375-m floodproducts to 30-m flood products. The software uses VIIRS daily composite flood product as a basis for the downscaling.
VIIRS downscaling software is designed to downscale the VIIRS 375-m flood products to 30-m flood products. The software uses VIIRS daily composite flood product as a basis for the downscaling.
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Landsat 8 Look Natural Color (Swaths)
[lsat8-llook-fc]
View of lsat8-llook-fc-scenes
View of lsat8-llook-fc-scenes
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Landsat 8 Look Thermal IR (Swaths)
[lsat8-llook-tir]
View of lsat8-llook-tir-scenes
View of lsat8-llook-tir-scenes
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Landsat Footprints (WRS-2)
[wrs2-land]
The Worldwide Reference System (WRS) is a global notation used incataloging Landsat data. Landsat 8 and Landsat 7 follow the WRS-2, as did Landsat 5 and Landsat 4.
The Worldwide Reference System (WRS) is a global notation used in cataloging Landsat data. Landsat 8 and Landsat 7 follow the WRS-2, as did Landsat 5 and Landsat 4.
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Landsat 9 Look Natural Color (Swaths)
[lsat9-llook-fc]
View of lsat9-llook-fc-scenes
View of lsat9-llook-fc-scenes
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Landsat 9 Look Thermal IR (Swaths)
[lsat9-llook-tir]
View of lsat9-llook-tir-scenes
View of lsat9-llook-tir-scenes
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Flood Warnings Hydrological-VTEC (Issued)
[HVTEC]
For Flood Warnings (FLW) and follow up Flood Statements (FLS) at specificriver forecast points, the H-VTEC specifies the flood severity; immediate cause, timing of flood beginning, crest, and end; and how the flood...
For Flood Warnings (FLW) and follow up Flood Statements (FLS) at specific river forecast points, the H-VTEC specifies the flood severity; immediate cause,
timing of flood beginning, crest, and end; and how the flood compares to the flood of record.
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Excessive Rainfall Outlook
[ERTA-NCEP]
Excessive Rainfall Outlook:
In the Excessive Rainfall Outlooks, theWeather Prediction Center (WPC) forecasts the probability that rainfall will exceed flash flood guidance at a point. Gridded FFG is provided by the...
Excessive Rainfall Outlook:
In the Excessive Rainfall Outlooks, the Weather Prediction Center (WPC) forecasts the probability that rainfall will exceed flash flood guidance at a point. Gridded FFG is provided by the twelve NWS River Forecast Centers (RFCs) whose service areas cover the lower 48 states. NCEP creates a national mosaic of FFG, whose 1, 3, and 6-hour values represent the amount of rainfall over those short durations which it is estimated would bring rivers and streams up to bankfull conditions. WPC estimates the likelihood that FFG will be exceeded by assessing environmental conditions (e.g. moisture content and steering winds), recognizing weather patterns commonly associated with heavy rainfall, and using a variety of deterministic and ensemble-based numerical model tools
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HRRR ConUS Latest Freezing MASK
[HRR-CONUS-FZRN-SFC]
HRRR ConUS Latest Freezing MASK
HRRR ConUS Latest Freezing MASK
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HRRR ConUS Latest Ice Mask
[HRR-CONUS-ICEP-SFC]
HRRR ConUS Latest Ice Mask
HRRR ConUS Latest Ice Mask
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HRRR ConUS Latest Precipitation Rate
[HRR-CONUS-PCP-LATEST]
View of HRR-CONUS-PCP-SFC
View of HRR-CONUS-PCP-SFC
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HRRR ConUS Latest Rain Mask
[HRR-CONUS-RAIN-SFC]
HRRR ConUS Latest Rain Mask
HRRR ConUS Latest Rain Mask
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HRRR ConUS Latest Simulated Radar
[HRR-CONUS-RADAR-LATEST]
View of HRR-CONUS-PCP-SFC
View of HRR-CONUS-PCP-SFC
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HRRR ConUS Latest Snow Mask
[HRR-CONUS-SNOW-SFC]
HRRR ConUS Latest Snow Mask
HRRR ConUS Latest Snow Mask
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RAP ConUS Latest Simulated Radar
[RAP-CONUS-PRAT-SFC-DBZ]
View of RAP-CONUS-PRAT-SFC
View of RAP-CONUS-PRAT-SFC
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GOES-16 ABI Band 01 "Blue"
[GNCA-G16-ABI-L2-CMIPF-C01]
The 0.47 µm, or “Blue” visible band, is one of two visible bands onthe ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established...
The 0.47 µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47 µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47µm band is more sensitive to aerosols / dust / smoke because it samples a part of the electromagnetic spectrum where clear-skyatmospheric scattering is important.
Freq: 00, 10, 30 and 40 min each hour
Units: Reflectance
Res: 1 km
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GOES-16 ABI Band 02 "Red"
[GNCA-G16-ABI-L2-CMIPF-C02]
The ‘Red’ Visible band – 0.64 µm – has the finest spatialresolution (0.5 km at the sub-satellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air...
The ‘Red’ Visible band – 0.64 µm – has the finest spatial resolution (0.5 km at the sub-satellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for
creation of “true color” imagery.
Freq: 00, 10, 20, 30, 40 and 50 min each hour
Units: Reflectance
Res: 1 km
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GOES-16 ABI Band 03 "Veggie"
[GNCA-G16-ABI-L2-CMIPF-C03]
The 0.86 μm band (a reflective band) detects daytime clouds, fog, andaerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86...
The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.
Freq: 00, 10, 30 and 40 min each hour
Units: Reflectance
Res: 1 km
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GOES-16 ABI Band 04 "Cirrus"
[GNCA-G16-ABI-L2-CMIPF-C04]
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABIin that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during...
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABI in that it
occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.
Freq: 00, 10, 30 and 40 min each hour
Units: Reflectance
Res: 2 km
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GOES-16 ABI Band 05 "Snow / Ice"
[GNCA-G16-ABI-L2-CMIPF-C05]
The Snow/Ice band takes advantage of the
difference between the refractioncomponents of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather...
The Snow/Ice band takes advantage of the
difference between the refraction components
of water and ice at 1.61 µm. Liquid water
clouds are bright in this channel; ice clouds are
darker because ice absorbs (rather than
reflects) radiation at 1.61 µm. Thus you can
infer cloud phase: compare at right the darker
region of the cirrus anvil to the more reflective
water-based cumulus clouds to the right of the
storm. Land/water contrast is great at 1.61
µm (lakes are readily apparent in the image)
and shadows can be particularly striking. Fires
can also be detected at night using this band.
Freq: 00, 10, 30 and 40 min each hour
Units: Reflectance
Res: 1 km
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GOES-16 ABI Band 06 "Cloud Particle Size"
[GNCA-G16-ABI-L2-CMIPF-C06]
The 2.24 μm band, in conjunction with other bands, enables cloud particlesize estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate...
The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free background over land), to create cloud masking and to detect hot spots.
Freq: 00, 10, 30 and 40 min each hour
Units: Reflectance
Res: 2 km
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GOES-16 ABI Band 07 "Shortwave Window"
[GNCA-G16-ABI-L2-CMIPF-C07]
The 3.9 μm band can be used to identify fog and low clouds at night,identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day....
The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both
emitted terrestrial radiation as well as significant reflected solar radiation during the day.
Freq: 00, 10, 20. 30. 40 and 50 min each hour
Units: Brightness Temperature (K)
Res: 1km
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GOES-16 ABI Band 08 "Upper-Level WV"
[GNCA-G16-ABI-L2-CMIPF-C08]
The 6.2 µm “Upper-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating upper/mid-level moisture (for legacy vertical moisture profiles) and identifying regions where the potential for turbulence exists. Further, it can be used to validate numerical model initialization and warming/cooling with time can reveal vertical motions at mid- and upper levels.
Freq: 00, 10, 20, 30, 40 and 50 min every hour
Units: Brightness Temperature (K)
Res: 2 km
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GOES-16 ABI Band 09 "Mid-Level WV"
[GNCA-G16-ABI-L2-CMIPF-C09]
The 6.9 µm “Mid-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9µm by water vapor.
Freq: 00, 10, 20, 30, 40 and 50 min every hour
Units: Brightness Temperature (K)
Res: 2 km
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GOES-16 ABI Band 10 "Lower-Level WV"
[GNCA-G16-ABI-L2-CMIPF-C10]
The 7.3 µm “Lower-level water vapor” band is one of three water vaporbands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track...
The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect snow bands.
Freq: 00, 10, 20, 30, 40 and 50 min every hour
Units: Brightness Temperature (K)
Res: 2 km
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GOES-16 ABI Band 11 "Cloud-Top Phase"
[GNCA-G16-ABI-L2-CMIPF-C11]
The infrared 8.5 μm band is a window channel; there is little atmosphericabsorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is...
The infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived brightness temperature. Water droplets also have different emissivity properties for 8.5μm radiation compared to other wavelengths. The 8.5μm band was not available on either the Legacy GOES Imager or GOES Sounder.
Freq: 00, 10, 30 and 40 min every hour
Units: Brightness Temperature (K)
Res: 2 km
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GOES-16 ABI Band 12 "Ozone"
[GNCA-G16-ABI-L2-CMIPF-C12]
The 9.6 μm band gives information both
day and night about the dynamicsof the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor...
The 9.6 μm band gives information both
day and night about the dynamics of
the atmosphere near the tropopause.
This band shows cooler temperatures
than the clean window band because
both ozone and water vapor absorb 9.6
μm atmospheric energy. The cooling
effect is especially apparent at large
zenith angles. This band alone cannot
diagnose total column ozone: product
generation using other bands will be
necessary for that.
Freq: 00,10, 30 and 40 min every hour
Units: Brightness Temperature (K)
Res: 2 km
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GOES-16 ABI Band 13 "Clean" IR Longwave
[GNCA-G16-ABI-L2-CMIPF-C13]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
Freq: 00, 10, 20, 30, 40 and 50 min every hour
Units: Brightness Temperature (K)
Res: 2 km
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GOES-16 ABI Band 14 IR Longwave
[GNCA-G16-ABI-L2-CMIPF-C14]
The infrared 11.2 μm band is a window channel; however, there isabsorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will...
The infrared 11.2 μm band is a window channel; however, there is absorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7 μm.
Freq: 00, 10, 20, 30, 40 and 50 min every hour
Units: Brightness Temperature (K)
Res: 2 km
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GOES-16 ABI Band 15 "Dirty" IR Longwave
[GNCA-G16-ABI-L2-CMIPF-C15]
Absorption and re-emission of water vapor, particularly in the lowertroposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more...
Absorption and re-emission of water vapor, particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3
μm) for the monitoring of simple atmospheric
phenomena.
Freq: 00, 10, 20, 30, 40 and 50 min every hour
Units: Brightness Temperature (K)
Res: 2 km
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GOES-16 ABI Band 16 "CO2" Longwave
[GNCA-G16-ABI-L2-CMIPF-C16]
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band canbe used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface...
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band can be used to
delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.
Freq: 00, 10 30 and 40 min every hour
Units: Brightness Temperature (K)
Res: 2 km
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GOES-16 Aerosol Detection Smoke and Dust
[GNCA-G16-ABI-L2-ADPF]
The aerosol detection product will use several spectral bands madeavailable on the GOES-R Series imager. The algorithm will use known spectral absorption and scattering properties of different aerosols to...
The aerosol detection product will use several spectral bands made available on the GOES-R Series imager. The algorithm will use known spectral absorption and scattering properties of different aerosols to detect their presence in the atmosphere. The aerosol detection product enables forecasters to better monitor areas of smoke and dust, which can be critical factors in visibility and air quality forecasts. In addition to short-term prediction, this product also enables better monitoring of the long-term trends in aerosol quantities and distribution throughout the atmosphere.
Res: 2km
Rate: 20min Valid: 30min
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GOES-16 Aerosol Optical Depth
[GNCA-G16-ABI-L2-AODF]
The aerosol optical depth (AOD) product utilizes several spectralwavelengths of the ABI (Advanced Baseline Imager) to measure the reflectance properties of cloud-free pixels at the top of the atmosphere...
The aerosol optical depth (AOD) product utilizes several spectral wavelengths of the ABI (Advanced Baseline Imager) to measure the reflectance properties of cloud-free pixels at the top of the atmosphere (TOA). These reflectance properties at the TOA are then fed into aerosol models to compute the surface reflectance and aerosol properties at the surface. The information provided by the AOD algorithm aids meteorologists and others in making critical air quality, visibility, and aviation forecasts. In addition, AOD product provides valuable data for climate models and helps climate scientists monitor and predict climate change.
Res: 2km
Rate:10min Valid: 30min
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GOES-16 Clear Sky Masks
[GNCA-G16-ABI-L2-ACMF]
The clear sky mask algorithm takes advantage of the high spatial andtemporal resolution of the GOES-R ABI visible, near-infrared, and infrared bands to automatically produce a cloud classification for each pixel:...
The clear sky mask algorithm takes advantage of the high spatial and temporal resolution of the GOES-R ABI visible, near-infrared, and infrared bands to automatically produce a cloud classification for each pixel: cloudy, probably cloudy, clear, or probably clear. This information is used extensively by downstream level-2 product algorithms that require the state of cloudiness in each pixel. Products such as land surface temperature (LST) and sea surface temperature (SST), for example, can only be reliably computed for pixels that are totally cloud free. The clear sky mask product can be used by the numerical weather prediction (NWP) community to identify which ABI pixel information should be assimilated for use in NWP models.
Res: 2km
Rate: 20min Valid: 30min
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GOES-16 Cloud Optical Depth
[GNCA-G16-ABI-L2-CODF]
Cloud optical depth uses both the visible and the near-infrared ABI bandsduring the daytime and a combination of infrared bands for nighttime detection. This product, together with the cloud particle size distribution...
Cloud optical depth uses both the visible and the near-infrared ABI bands during the daytime and a combination of infrared bands for nighttime detection. This product, together with the cloud particle size distribution product, provides valuable information about the radiative properties of clouds. These two properties enhance climate prediction, as they provide global climate models with higher quality data regarding the Earth’s energy and radiation budget.
Res: 2km
Rate:20min Valid: 30min
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GOES-16 Cloud Particle Size Distribution
[GNCA-G16-ABI-L2-CPSF]
The cloud effective particle size is computed using the same algorithm thatestimates cloud optical depth (COD). Using both the visible and near-infrared ABI bands during the day and the infrared bands during the...
The cloud effective particle size is computed using the same algorithm that estimates cloud optical depth (COD). Using both the visible and near-infrared ABI bands during the day and the infrared bands during the night, the GOES-R cloud optical and microphysical properties algorithm retrieves, simultaneously with COD, the cloud particle size. Cloud particle size provides valuable information about the radiative properties of clouds. This information combined with the information provided by the COD product provides very accurate information about the Earth’s radiation budget, yielding more accurate climate prediction possibilities.
Res: 2km
Rate: 20min Valid: 30min
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GOES-16 Cloud Top Height
[GNCA-G16-ABI-L2-ACHAF]
The cloud top height algorithm uses ABI infrared bands to simultaneouslyretrieve cloud top height, cloud top temperature, and cloud top pressure for each cloudy pixel. These cloud products are a prerequisite for...
The cloud top height algorithm uses ABI infrared bands to simultaneously retrieve cloud top height, cloud top temperature, and cloud top pressure for each cloudy pixel. These cloud products are a prerequisite for generating other downstream products that include cloud layer, cloud optical/microphysical products, and derived motion winds. Forecasters can use this information to determine areas of cloud growth and likelihood of precipitation. Other operational applications of this product include its use in aviation Terminal Aerodrome Forecasts (TAFs), supplementing upper-level cloud information to the ground-based Automated Surface Observing System (ASOS), and initialization of clouds in numerical weather prediction models.
Res: 10km
Rate: 20min Valid: 30min
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GOES-16 Cloud Top Phase
[GNCA-G16-ABI-L2-ACTPF]
The cloud type algorithm uses four GOES-R ABI infrared spectral bands todetermine four different cloud phases: warm (>0C) liquid water, supercooled liquid water, mixed, and ice. The cloud phase product is a prerequisite for...
The cloud type algorithm uses four GOES-R ABI infrared spectral bands to determine four different cloud phases: warm (>0C) liquid water, supercooled liquid water, mixed, and ice. The cloud phase product is a prerequisite for generating other downstream products that include cloud height, cloud optical properties, fog detection/depth, and aircraft icing. The cloud top phase product enables meteorologists to better monitor and track changes in the water properties of clouds, improve icing forecasts for the aviation community, and aid in improving warnings for severe weather. Cloud phase product information can also be used in advanced ABI applications such as severe weather prediction and tropical cyclone intensity estimation.
Res: 2km
Rate: 20min Valid: 30min
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GOES-16 Cloud Top Pressure
[GNCA-G16-ABI-L2-CTPF]
The cloud top height algorithm uses ABI infrared bands to simultaneouslyretrieve cloud top height, cloud top temperature, and cloud top pressure for each cloudy pixel. These cloud products are a prerequisite for...
The cloud top height algorithm uses ABI infrared bands to simultaneously retrieve cloud top height, cloud top temperature, and cloud top pressure for each cloudy pixel. These cloud products are a prerequisite for generating other downstream products that include cloud layer, cloud optical/microphysical products, and derived motion winds. Forecasters can use this information to determine areas of cloud growth and likelihood of precipitation. Other operational applications of this product include its use in aviation Terminal Aerodrome Forecasts (TAFs), supplementing upper-level cloud information to the ground-based Automated Surface Observing System (ASOS), and initialization of clouds in numerical weather prediction models.
Res: 2km
Freq: 30min Valid: 30min
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GOES-16 Cloud Top Temperature
[GNCA-G16-ABI-L2-ACHTF]
The cloud top height algorithm uses ABI infrared bands to simultaneouslyretrieve cloud top height, cloud top temperature, and cloud top pressure for each cloudy pixel. These cloud products are a prerequisite for...
The cloud top height algorithm uses ABI infrared bands to simultaneously retrieve cloud top height, cloud top temperature, and cloud top pressure for each cloudy pixel. These cloud products are a prerequisite for generating other downstream products that include cloud layer, cloud optical/microphysical products, and derived motion winds. Forecasters can use this information to determine areas of cloud growth and likelihood of precipitation. Other operational applications of this product include its use in aviation Terminal Aerodrome Forecasts (TAFs), supplementing upper-level cloud information to the ground-based Automated Surface Observing System (ASOS), and initialization of clouds in numerical weather prediction models.
Res: 10km
Rate: 20min Valid: 30min
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GOES-16 Derived Clear Sky Winds Band 08
[GNCA-G16-ABI-L2-DMWVF-C08]
The derived motion winds product is derived from using a sequence ofvisible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion...
The derived motion winds product is derived from using a sequence of visible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion are assigned heights by using the cloud height product. The derived motion winds product provides vital tropospheric wind information over expansive regions of the earth devoid of in-situ wind observations that include oceans and Southern Hemisphere land masses. This product provides key wind observations to operational numerical weather prediction (NWP) data assimilation systems where their use has been demonstrated to improved NWP forecasts including tropical cyclones. In addition, this product provides improved guidance for National Weather Service field forecasters.
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GOES-16 Derived Motion Winds Band 02
[GNCA-G16-ABI-L2-DMWF-C02]
The derived motion winds product is derived from using a sequence ofvisible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion...
The derived motion winds product is derived from using a sequence of visible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion are assigned heights by using the cloud height product. The derived motion winds product provides vital tropospheric wind information over expansive regions of the earth devoid of in-situ wind observations that include oceans and Southern Hemisphere land masses. This product provides key wind observations to operational numerical weather prediction (NWP) data assimilation systems where their use has been demonstrated to improved NWP forecasts including tropical cyclones. In addition, this product provides improved guidance for National Weather Service field forecasters.
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GOES-16 Derived Motion Winds Band 07
[GNCA-G16-ABI-L2-DMWF-C07]
The derived motion winds product is derived from using a sequence ofvisible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion...
The derived motion winds product is derived from using a sequence of visible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion are assigned heights by using the cloud height product. The derived motion winds product provides vital tropospheric wind information over expansive regions of the earth devoid of in-situ wind observations that include oceans and Southern Hemisphere land masses. This product provides key wind observations to operational numerical weather prediction (NWP) data assimilation systems where their use has been demonstrated to improved NWP forecasts including tropical cyclones. In addition, this product provides improved guidance for National Weather Service field forecasters.
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GOES-16 Derived Motion Winds Band 08
[GNCA-G16-ABI-L2-DMWF-C08]
The derived motion winds product is derived from using a sequence ofvisible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion...
The derived motion winds product is derived from using a sequence of visible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion are assigned heights by using the cloud height product. The derived motion winds product provides vital tropospheric wind information over expansive regions of the earth devoid of in-situ wind observations that include oceans and Southern Hemisphere land masses. This product provides key wind observations to operational numerical weather prediction (NWP) data assimilation systems where their use has been demonstrated to improved NWP forecasts including tropical cyclones. In addition, this product provides improved guidance for National Weather Service field forecasters.
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GOES-16 Derived Motion Winds Band 09
[GNCA-G16-ABI-L2-DMWF-C09]
The derived motion winds product is derived from using a sequence ofvisible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion...
The derived motion winds product is derived from using a sequence of visible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion are assigned heights by using the cloud height product. The derived motion winds product provides vital tropospheric wind information over expansive regions of the earth devoid of in-situ wind observations that include oceans and Southern Hemisphere land masses. This product provides key wind observations to operational numerical weather prediction (NWP) data assimilation systems where their use has been demonstrated to improved NWP forecasts including tropical cyclones. In addition, this product provides improved guidance for National Weather Service field forecasters.
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GOES-16 Derived Motion Winds Band 10
[GNCA-G16-ABI-L2-DMWF-C10]
The derived motion winds product is derived from using a sequence ofvisible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion...
The derived motion winds product is derived from using a sequence of visible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion are assigned heights by using the cloud height product. The derived motion winds product provides vital tropospheric wind information over expansive regions of the earth devoid of in-situ wind observations that include oceans and Southern Hemisphere land masses. This product provides key wind observations to operational numerical weather prediction (NWP) data assimilation systems where their use has been demonstrated to improved NWP forecasts including tropical cyclones. In addition, this product provides improved guidance for National Weather Service field forecasters.
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GOES-16 Derived Motion Winds Band 14
[GNCA-G16-ABI-L2-DMWF-C14]
The derived motion winds product is derived from using a sequence ofvisible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion...
The derived motion winds product is derived from using a sequence of visible or infrared spectral bands to track the motion of cloud features and water vapor gradients. The resulting estimates of atmospheric motion are assigned heights by using the cloud height product. The derived motion winds product provides vital tropospheric wind information over expansive regions of the earth devoid of in-situ wind observations that include oceans and Southern Hemisphere land masses. This product provides key wind observations to operational numerical weather prediction (NWP) data assimilation systems where their use has been demonstrated to improved NWP forecasts including tropical cyclones. In addition, this product provides improved guidance for National Weather Service field forecasters.
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GOES-16 Derived Stability Indices
[GNCA-G16-ABI-L2-DSIF]
The derived stability indices such as convective available potential energy(CAPE), lifted index (LI), total totals (TT), Showalter index (SI), and the K-index (KI) are computed from the retrieved atmospheric moisture and...
The derived stability indices such as convective available potential energy (CAPE), lifted index (LI), total totals (TT), Showalter index (SI), and the K-index (KI) are computed from the retrieved atmospheric moisture and temperature profiles. These indices aid forecasters in nowcasting severe weather by providing them with a plan view of these atmospheric stability parameters. Forecasters use this information to monitor rapid changes in atmospheric stability over time at various geographic locations, thus improving their situational awareness in pre-convective environments for potential watch/warning scenarios.
Res: 2km
Freq: 20min Valid: 30min
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GOES-16 Downward Shortwave Radiation Surface
[GNCA-G16-ABI-L2-DSRF]
The downward shortwave radiation (DSR) product is an estimate of the totalamount of shortwave radiation that reaches the Earth’s surface. The product algorithm uses spectral channels in both the visible and the...
The downward shortwave radiation (DSR) product is an estimate of the total amount of shortwave radiation that reaches the Earth’s surface. The product algorithm uses spectral channels in both the visible and the infrared in addition to data regarding albedo and atmospheric composition to compute the downward shortwave radiation at the Earth’s surface. It is also used in surface energy budget models, land surface assimilation models and ocean assimilation models either as an input (providing observationally-based forcing term), or as an independent data source to evaluate model performance. DSR data are also employed in estimating heat flux components over the coastal ocean to drive ocean circulation models. In agriculture, DSR is used as input in crop modeling. In hydrology, it is used in watershed and run-off analysis, which is important for determining flood risks and dam monitoring.
Res: 2km
Rate: 10min Valid: 30min
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GOES-16 Fire / Hot Spot Characterization
[GNCA-G16-ABI-L2-FDCF]
The fire/hot spot characterization product makes use of both visible andinfrared ABI spectral bands to locate fires and retrieve sub-pixel fire characteristics. The product greatly improves upon the previous fire...
The fire/hot spot characterization product makes use of both visible and infrared ABI spectral bands to locate fires and retrieve sub-pixel fire characteristics. The product greatly improves upon the previous fire detection product by taking advantage of the higher spatial and temporal resolution available with the GOES-R Series ABI. Forecasters use this product to monitor wildfires, and more importantly, rapid changes in individual fires. Forecasters use this product as part of an arsenal of forecasting tools aimed at helping firefighting efforts.
Res: 2km
Rate: 10min Valid: 30min
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GOES-16 Land Surface Temperture Skin
[GNCA-G16-ABI-L2-LST2KMF]
The land surface temperature (LST2km) product is derived from GOES-R ABIlongwave infrared spectral channels and is expected to be used in a number of applications in hydrology, meteorology, and climatology. Forecasters use...
The land surface temperature (LST2km) product is derived from GOES-R ABI longwave infrared spectral channels and is expected to be used in a number of applications in hydrology, meteorology, and climatology. Forecasters use it to forecast the occurrence of fog and frost. The land surface product is of fundamental importance to the net radiation budget at the Earth’s surface and to monitoring the state of crops and vegetation. It is an important indicator of both the greenhouse effect and the energy flux between the atmosphere and ground. Furthermore, it can be assimilated into climate, atmospheric, and land surface models to estimate sensible heat flux and latent heat flux.
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GOES-16 Rainfall Rate QPE
[GNCA-G16-ABI-L2-RRQPEF]
The ABI rainfall rate algorithm generates the baseline rainfall rateproduct from ABI infrared brightness temperatures and is calibrated in real time against microwave-derived rain rates to enhance accuracy. The...
The ABI rainfall rate algorithm generates the baseline rainfall rate product from ABI infrared brightness temperatures and is calibrated in real time against microwave-derived rain rates to enhance accuracy. The algorithm generates estimates of the instantaneous rainfall rate at each ABI IR pixel. The information provided by the quantitative precipitation estimation is used by forecasters and hydrologists in flood forecasting. Much of the flooding that occurs is related to some form of convective weather. The higher spatial and temporal resolution available on the GOES-R Series ABI is able to automatically resolve rainfall rates on a much finer scale, enabling weather forecasters to produce more timely and accurate flood advisories and warnings.
Res: 2km
Freq: 20min Valid: 30min
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GOES-16 Reflected Shortwave Radiation TOA
[GNCA-G16-ABI-L2-RSRF]
The reflected shortwave radiation product measures the total amount ofshortwave radiation that exits the Earth through the top of the atmosphere. The algorithm uses several spectral channels in both the visible and...
The reflected shortwave radiation product measures the total amount of shortwave radiation that exits the Earth through the top of the atmosphere. The algorithm uses several spectral channels in both the visible and infrared spectrum to measure the reflected shortwave radiation. Information from this product provides an integral piece of the Earth’s radiation budget, aiding in climate modeling and prediction.
Res 25km
Freq: 60min Valid: 180min
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GOES-16 Sea Surface Temperature Skin
[GNCA-G16-ABI-L2-SSTF]
The GOES-R Series provides forecasters with a sea surface temperature (SST)for each cloud-free pixel over water identified by the ABI. The SST algorithm employed on the GOES-R Series uses hybrid physical-regression...
The GOES-R Series provides forecasters with a sea surface temperature (SST) for each cloud-free pixel over water identified by the ABI. The SST algorithm employed on the GOES-R Series uses hybrid physical-regression retrieval in order to produce a more accurate product. Knowledge of SST can be beneficial for a large spectrum of operational applications that include: climate monitoring/forecasting, seasonal forecasting, operational weather and ocean forecasting, military and defense operations, validating or forcing ocean and atmospheric models, sea turtle tracking, coral bleach warnings and assessment, tourism, and commercial fisheries management.
Res: 2km
Freq: 60min Valid: 180min
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GOES-16 Total Precipitable Water
[GNCA-G16-ABI-L2-TPWF]
The total precipitable water (TPW) product is computed from the retrievedatmospheric moisture profiles and represents the total integrated moisture in the atmospheric column from the surface to the top of the atmosphere....
The total precipitable water (TPW) product is computed from the retrieved atmospheric moisture profiles and represents the total integrated moisture in the atmospheric column from the surface to the top of the atmosphere. This product provides useful information to weather forecasters and hydrologists to improve their situational awareness for a number of situations that require forecasting of events, such as heavy rain, flash flooding, onset of Gulf of Mexico return flow, and the onset of the Southwest United States monsoon. The TPW product also serves to initialize the moisture field used in numerical weather prediction models.
Res: 2km
Freq: 60min Valid:180min
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GOES-16 GLM Flash Density Full Disk
[GNCA-G16-GLM-FlashDensity]
The GLM detects changes in brightness every
~2 ms relative to acontinuously updating background image Individual pixels illuminated above the background threshold during 2 ms frames are termed GLM events ...
The GLM detects changes in brightness every
~2 ms relative to a continuously updating
background image
Individual pixels illuminated above the
background threshold during 2 ms frames are
termed GLM events
Filters then remove non-lightning events leaving
only those most likely to be lightning
Lightning Cluster Filter Algorithm combines
events into groups and groups into flashes
Res: 8x8km
Freq: 5min Valid:30min
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GOES-17 ABI Band 02 "Red"
[GNCA-G17-ABI-L2-CMIPF-C02]
The ‘Red’ Visible band – 0.64 µm – has the finest spatialresolution (0.5 km at the sub-satellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air...
The ‘Red’ Visible band – 0.64 µm – has the finest spatial resolution (0.5 km at the sub-satellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for
creation of “true color” imagery.
Res: 1km
Rate:10min Valid:30
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GOES-17 ABI Band 09 "Mid-Level WV"
[GNCA-G17-ABI-L2-CMIPF-C09]
The 6.9 µm “Mid-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9µm by water vapor.
Res:1km
Rate: 10min Valid: 30min
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GOES-17 ABI Band 13 "Clean" IR Longwave
[GNCA-G17-ABI-L2-CMIPF-C13]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
Res: 1km
Rate: 10min Valid: 30min
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VIIRS Fire Radiative Power I-band DB ConUS
[AFIMG-Points]
VIIRS 375m I-band high spatial resolution imagery provides a greaterresponse over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime...
VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software.
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Flood Mapping Product - Joint VIIRS / ABI comp
[GNCA-RIVER-FLD-joint-ABI]
The River Flood product, developed at George Mason University (GMU), wasderived from VIIRS and adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice,...
The River Flood product, developed at George Mason University (GMU), was derived from VIIRS and adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available ABI/AHI Full Disk imagery and VIIRS imagery since sunrise on the given day. The joint VIIRS/ABI flood product combines the clear sky VIIRS and ABI daily composites and represents the best daily cloud-free flood extent. Available at ~0800 UTC daily and is useful for flood extent from the previous day.
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Flood Mapping Product - River Flood Global comp
[GNCA-RIVER-FLDglobal-composite]
The 5-day VIIRS River Flood composite has the best spatial resolution andprovides the maximal cloud-free flood extent during the latest five days. Useful for flood investigation during a major flood event. The VIIRS 375-m...
The 5-day VIIRS River Flood composite has the best spatial resolution and provides the maximal cloud-free flood extent during the latest five days. Useful for flood investigation during a major flood event. The VIIRS 375-m Flood Product, is a near real-time product derived from daytime VIIRS imagery from S-NPP and NOAA20. The product reflects the current flood status at the time of the overpass along with additional information on the weather and land conditions. S-NPP and NOAA-20 are Low Earth Orbiting (LEO) satellites, which means only two daytime observations can be derived per day over a given Region of Interest (ROI) with a ~50 min interval. Observations are taken ~2-3pm local solar time.
Res: 375m
Rate: ~100min Valid: 1day
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Flood Mapping Product - River Flood GOES-16 ABI
[GNCA-River-Flood-ABI-hourly]
The VIIRS River Flood product is adapted to the GOES-ABI. The productprovides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. This product represents a composite of all...
The VIIRS River Flood product is adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. This product represents a composite of all available imagery since sunrise through the given hour. These products are expected to be most useful in mid- and low-latitude locations. The ABI hourly composite has the best time manner in detecting floods as it is updated every hour under cloud free conditions. Information composited hourly for clouds to provide the best and latest cloud free information of flood extent.This can be very helpful for emergency response providing a low resolution (1-2km) initial flood extent estimate.
Res: 1km
Rate: 1hr Valid: 1day
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GOES 18 - RGB
[G18-daynight]
True color - day
Cloud microphysics - night
True color - day
Cloud microphysics - night
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GOES East ABI ConUS B01 "Blue" Visible
[G19-ABI-CONUS-BAND01]
The 0.47 µm, or “Blue” visible band, is one of two visible bands onthe ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established...
The 0.47 µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47 µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47 µm
band is more sensitive to aerosols / dust /
smoke because it samples a part of the
electromagnetic spectrum where clear-sky
atmospheric scattering is important
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GOES East ABI ConUS B02 Hi-Res "Red" Visible
[G19-ABI-CONUS-BAND02]
The ‘Red’ Visible band – 0.64 µm – has the
finest spatialresolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air...
The ‘Red’ Visible band – 0.64 µm – has the
finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow
and ice cover, diagnose low-level cloud-drift
winds, assist with detection of volcanic ash
and analysis of hurricanes and winter storms.
The ‘Red’ Visible band is also essential for
creation of “true color” imagery.
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GOES East ABI ConUS B03 "Veggie"
[G19-ABI-CONUS-BAND03]
The 0.86 μm band (a reflective band) detects daytime clouds, fog, andaerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86...
The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.
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GOES East ABI ConUS B04 Cirrus
[G19-ABI-CONUS-BAND04]
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABIin that it occupies a region of very strong absorption bywater vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the...
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABI in that it
occupies a region of very strong absorption bywater vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.
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GOES East ABI ConUS B05 Snow/Ice
[G19-ABI-CONUS-BAND05]
The Snow/Ice band takes advantage of the
difference between the refractioncomponents of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than...
The Snow/Ice band takes advantage of the
difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.
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GOES East ABI ConUS B06 Cloud Particle Size
[G19-ABI-CONUS-BAND06]
The 2.24 μm band, in conjunction with other bands, enables cloud particlesize estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate...
The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free background over land), to create cloud masking and to detect hot spots.
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GOES East ABI ConUS B07 "Fire"
[G19-ABI-CONUS-BAND07]
The 3.9 μm band can be used to identify fog and low clouds at night,identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day....
The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.
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GOES East ABI ConUS B07 "Fire" enhanced
[G19-ABI-CONUS-BAND07-FIRE]
The 3.9 μm band can be used to identify fog and low clouds at night,identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day....
The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.
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GOES East ABI ConUS B08 Upper-level Water Vapor
[G19-ABI-CONUS-BAND08]
The 6.2 µm “Upper-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating upper/ mid-level moisture (for legacy vertical moisture
profiles) and identifying regions where the
potential for turbulence exists. Further, it can be used to validate numerical model initialization and warming/cooling with time can reveal vertical motions at mid- and upper levels.
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GOES East ABI ConUS B08 Upper-level Water Vapor enhanced
[G19-ABI-CONUS-BAND08-VAPR]
The 6.2 µm “Upper-level water vapor” band is one of three water vaporbands on the ABI, andis used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, andis used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating upper/ mid-level moisture (for legacy vertical moisture
profiles) and identifying regions where the
potential for turbulence exists. Further, it can be used to validate numerical model initialization and warming/cooling with time can reveal vertical motions at mid- and upper levels.
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GOES East ABI ConUS B09 Mid-level Water Vapor
[G19-ABI-CONUS-BAND09]
The 6.9 µm “Mid-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might
exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9µm by water vapor.
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GOES East ABI ConUS B09 Mid-level Water Vapor enhanced
[G19-ABI-CONUS-BAND09-VAPR]
The 6.9 µm “Mid-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level
moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9µm by water vapor.
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GOES East ABI ConUS B10 Lower-level Water Vapor
[G19-ABI-CONUS-BAND10]
The 7.3 µm “Lower-level water vapor” band is one of three water vaporbands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track...
The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect
snow bands.
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GOES East ABI ConUS B10 Lower-level Water Vapor enhanced
[G19-ABI-CONUS-BAND10-VAPR]
The 7.3 µm “Lower-level water vapor” band is one of three water vaporbands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track...
The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect
snow bands.
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GOES East ABI ConUS B11 Cloud Phase
[G19-ABI-CONUS-BAND11]
The infrared 8.5 μm band is a window channel; there is little atmosphericabsorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is...
The infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived
brightness temperature. Water droplets also
have different emissivity properties for 8.5μm radiation compared to other wavelengths. The 8.5μm band was not available on either the Legacy GOES Imager or GOES Sounder.
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GOES East ABI ConUS B12 Ozone
[G19-ABI-CONUS-BAND12]
The 9.6 μm band gives information both
day and night about the dynamicsof the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor...
The 9.6 μm band gives information both
day and night about the dynamics of
the atmosphere near the tropopause.
This band shows cooler temperatures
than the clean window band because
both ozone and water vapor absorb 9.6
μm atmospheric energy. The cooling
effect is especially apparent at large
zenith angles. This band alone cannot
diagnose total column ozone: product
generation using other bands will be
necessary for that.
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GOES East ABI ConUS B13 "Clean" Infrared
[G19-ABI-CONUS-BAND13]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES East ABI ConUS B13 "Clean" Infrared enhanced
[G19-ABI-CONUS-BAND13-GRAD]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES East ABI ConUS B14 Infrared
[G19-ABI-CONUS-BAND14]
The infrared 11.2 μm band is a window
channel; however, there isabsorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will...
The infrared 11.2 μm band is a window
channel; however, there is absorption of
energy by water vapor at this wavelength.
Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7μm.
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GOES East ABI ConUS B15 "Dirty" Infrared
[G19-ABI-CONUS-BAND15]
Absorption and re-emission of water vapor,
particularly in the lowertroposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more...
Absorption and re-emission of water vapor,
particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3μm) for the monitoring of simple atmospheric phenomena.
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GOES East ABI ConUS B16 Carbon Dioxide
[G19-ABI-CONUS-BAND16]
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band canbe used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface...
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band can be used to
delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.
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GOES East ABI ConUS RGB Air Mass
[G19-ABI-CONUS-airmass]
The Air Mass RGB is used to diagnose the
environment surrounding synopticsystems by enhancing temperature and moisture characteristics of air masses. Cyclogenesis can be inferred by the identification of warm,...
The Air Mass RGB is used to diagnose the
environment surrounding synoptic systems by enhancing temperature and moisture
characteristics of air masses. Cyclogenesis can
be inferred by the identification of warm, dry,
ozone-rich descending stratospheric air
associated with jet streams and potential
vorticity (PV) anomalies. The RGB can be used to
validate the location of PV anomalies in model
data. Additionally, this RGB can distinguish
between polar and tropical air masses, especially along upper-level frontal boundaries and identify high-, mid-, and low-level clouds.
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GOES East ABI ConUS RGB Cloud Phase
[G19-ABI-CONUS-cloud-phase]
The Baseline Cloud Phase product describes the cloud-top composition.There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase...
The Baseline Cloud Phase product describes the cloud-top composition. There are four phase
categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid
water and ice) clouds and glaciated (ice) clouds.
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GOES East ABI ConUS RGB Day Convection
[G19-ABI-CONUS-convection]
The Day Convection RGB was designed to
emphasize convection with strongupdrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the...
The Day Convection RGB was designed to
emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the
mature storm stage.
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GOES East ABI ConUS RGB Day Microphysics
[G19-ABI-CONUS-day-microphysics-abi]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
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GOES East ABI ConUS RGB Differential Water Vapor
[G19-ABI-CONUS-water-vapors2]
The Differential Water Vapor RGB was designed to analyze water vapordistribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and...
The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries,
trough/ridge patterns, potential vorticity (PV)
anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and
tropopause fold-driven high-impact wind events.
Analysis of moist/dry layers is also important for
predicting changes in hurricane intensity and
extratropical transition.
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GOES East ABI ConUS RGB Dust
[G19-ABI-CONUS-dust]
Dust can be hard to see in VIS and IR imagery because it is optically thin,or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band...
Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the
IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in
color at night depending on height. Dust is also
distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud
phase and thicknesses.
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GOES East ABI ConUS RGB Fire Temperature
[G19-ABI-CONUS-fire-temperature-awips]
This RGB allows the user to identify where the most intense fires areoccurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background...
This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar
radiation and surface reflectance increases. This means
that fires need to be more intense in order to be
detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.
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GOES East ABI ConUS RGB Geo Color
[G19-ABI-CONUS-geo-color]
GeoColor imagery provides as close an
approximation to daytime True Colorimagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark...
GeoColor imagery provides as close an
approximation to daytime True Color imagery
as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights
database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.
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GOES East ABI ConUS RGB IR Sandwich
[G19-ABI-CONUS-ir-sandwich]
With this product(s) it is possible to monitor those cloud top features ofmature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and...
With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature.
Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.
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GOES East ABI ConUS RGB Night Microphysics
[G19-ABI-CONUS-night-microphysics]
The distinction between low clouds and fog in
satellite imagery is often achallenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the...
The distinction between low clouds and fog in
satellite imagery is often a challenge. While the
difference in the 10.4 and 3.9 µm channels has
been a regularly applied product to meet
aviation forecast needs, the Nighttime
Microphysics (NtMicro) RGB adds another
channel difference (12.4- 10.4 µm) as a proxy to
cloud thickness and repeats the use of the 10.4
µm thermal channel to enhance areas of warm
(i.e. low) clouds where fog is more likely. The
NtMicro RGB is also an efficient tool to quickly
identify other cloud types in the mid and upper
atmosphere.
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GOES East ABI ConUS RGB Snow-Fog
[G19-ABI-CONUS-snow-fog]
On heritage GOES, it was difficult to distinguish white “reflective”snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the...
On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds
varies across the visible, near infrared, and
infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.
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GOES East ABI ConUS RGB SO2
[G19-ABI-CONUS-so2]
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere duringvolcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and...
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.
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GOES East ABI ConUS RGB True Color
[G19-ABI-CONUS-true-color]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
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GOES East ABI ConUS RGB Volcanic Ash
[G19-ABI-CONUS-ash]
The Volcanic Ash False Color product determines the location for satellitepixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and...
The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.
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GOES East ABI Full Disk B01 "Blue" Visible
[G19-ABI-FD-BAND01]
The 0.47 µm, or “Blue” visible band, is one of two visible bands onthe ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established...
The 0.47 µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47 µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47µm band is more sensitive to aerosols / dust / smoke because it samples a part of the
electromagnetic spectrum where clear-sky
atmospheric scattering is important.
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GOES East ABI Full Disk B02 Hi-Res "Red" Visible
[G19-ABI-FD-BAND02]
The ‘Red’ Visible band – 0.64 µm – has the
finest spatialresolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air...
The ‘Red’ Visible band – 0.64 µm – has the
finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color imagery.
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GOES East ABI Full Disk B03 "Veggie"
[G19-ABI-FD-BAND03]
The 0.86 μm band (a reflective band) detects daytime clouds, fog, andaerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86...
The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.
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GOES East ABI Full Disk B04 Cirrus
[G19-ABI-FD-BAND04]
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABIin that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during...
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABI in that it
occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.
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GOES East ABI Full Disk B05 Snow/Ice
[G19-ABI-FD-BAND05]
The Snow/Ice band takes advantage of the
difference between the refractioncomponents of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than...
The Snow/Ice band takes advantage of the
difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.
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GOES East ABI Full Disk B06 Cloud Particle Size
[G19-ABI-FD-BAND06]
The 2.24 μm band, in conjunction with other bands, enables cloud particlesize estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate...
The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free
background over land), to create cloud
masking and to detect hot spots.
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GOES East ABI Full Disk B07 "Fire"
[G19-ABI-FD-BAND07]
The 3.9 μm band can be used to identify fog and low clouds at night,identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day....
The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.
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GOES East ABI Full Disk B08 Upper-level Water Vapor
[G19-ABI-FD-BAND08]
The 6.2 µm “Upper-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking upper tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating upper/ mid-level moisture (for legacy vertical moisture
profiles) and identifying regions where the
potential for turbulence exists. Further, it can be used to validate numerical model initialization and warming/cooling with time can reveal vertical motions at mid- and upper levels.
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GOES East ABI Full Disk B09 Mid-level Water Vapor
[G19-ABI-FD-BAND09]
The 6.9 µm “Mid-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level
moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9µm by water vapor.
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GOES East ABI Full Disk B10 Lower-level Water Vapor
[G19-ABI-FD-BAND10]
The 7.3 µm “Lower-level water vapor” band is one of three water vaporbands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track...
The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect
snow bands.
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GOES East ABI Full Disk B11 Cloud Phase
[G19-ABI-FD-BAND11]
The infrared 8.5 μm band is a window channel; there is little atmosphericabsorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is...
The infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceive brightness temperature. Water droplets also have different emissivity properties for 8.5 μm
radiation compared to other wavelengths. The 8.5μm band was not available on either the Legacy GOES Imager or GOES Sounder.
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GOES East ABI Full Disk B12 Ozone
[G19-ABI-FD-BAND12]
The 9.6 μm band gives information both
day and night about the dynamicsof the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor...
The 9.6 μm band gives information both
day and night about the dynamics of
the atmosphere near the tropopause.
This band shows cooler temperatures
than the clean window band because
both ozone and water vapor absorb 9.6
μm atmospheric energy. The cooling
effect is especially apparent at large
zenith angles. This band alone cannot
diagnose total column ozone: product
generation using other bands will be
necessary for that.
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GOES East ABI Full Disk B13 "Clean" Infrared
[G19-ABI-FD-BAND13]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES East ABI Full Disk B13 "Clean" Infrared enhanced
[G19-ABI-FD-BAND13-GRAD]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES East ABI Full Disk B14 Infrared
[G19-ABI-FD-BAND14]
The infrared 11.2 μm band is a window
channel; however, there isabsorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will...
The infrared 11.2 μm band is a window
channel; however, there is absorption of
energy by water vapor at this wavelength.
Brightness Temperatures (BTs) are affected by
this absorption, and 11.2 μm BTs will be cooler
than clean window (10.3 μm) BTs – by an
amount that is a function of the amount of
moisture in the atmosphere. This band has
similarities to the legacy infrared channel at
10.7 μm.
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GOES East ABI Full Disk B15 "Dirty" Infrared
[G19-ABI-FD-BAND15]
Absorption and re-emission of water vapor,
particularly in the lowertroposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more...
Absorption and re-emission of water vapor,
particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3μm band and the 10.3μm are used to compute the ‘split window difference’. The 10.3μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3μm) for the monitoring of simple atmospheric phenomena.
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GOES East ABI Full Disk B16 Carbon Dioxide
[G19-ABI-FD-BAND16]
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band canbe used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface...
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band can be used to
delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.
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GOES East ABI Full Disk RGB Air Mass
[G19-ABI-FD-airmass]
The Air Mass RGB is used to diagnose the
environment surrounding synopticsystems by enhancing temperature and moisture characteristics of air masses. Cyclogenesis can be inferred by the identification of warm,...
The Air Mass RGB is used to diagnose the
environment surrounding synoptic systems by enhancing temperature and moisture
characteristics of air masses. Cyclogenesis can
be inferred by the identification of warm, dry,
ozone-rich descending stratospheric air
associated with jet streams and potential
vorticity (PV) anomalies. The RGB can be used to
validate the location of PV anomalies in model
data. Additionally, this RGB can distinguish
between polar and tropical air masses, especially along upper-level frontal boundaries and identify high-, mid-, and low-level clouds.
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GOES East ABI Full Disk RGB Cloud Phase
[G19-ABI-FD-cloud-phase]
The Baseline Cloud Phase product describes the cloud-top composition. Thereare four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid...
The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.
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GOES East ABI Full Disk RGB Day Convection
[G19-ABI-FD-convection]
The Day Convection RGB was designed to
emphasize convection with strongupdrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the...
The Day Convection RGB was designed to
emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.
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GOES East ABI Full Disk RGB Day Microphysics
[G19-ABI-FD-day-microphysics-abi]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
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GOES East ABI Full Disk RGB Differential Water Vapor
[G19-ABI-FD-water-vapors2]
The Differential Water Vapor RGB was designed to analyze water vapordistribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and...
The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries,
trough/ridge patterns, potential vorticity (PV)
anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and
tropopause fold-driven high-impact wind events.
Analysis of moist/dry layers is also important for
predicting changes in hurricane intensity and
extratropical transition.
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GOES East ABI Full Disk RGB Dust
[G19-ABI-FD-dust]
Dust can be hard to see in VIS and IR imagery because it is optically thin,or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band...
Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.
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GOES East ABI Full Disk RGB Fire Temperature
[G19-ABI-FD-fire-temperature-awips]
This RGB allows the user to identify where the most intense fires areoccurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background...
This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.
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GOES East ABI Full Disk RGB Geo Color
[G19-ABI-FD-geo-color]
GeoColor imagery provides as close an
approximation to daytime True Colorimagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark...
GeoColor imagery provides as close an
approximation to daytime True Color imagery
as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights
database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.
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GOES East ABI Full Disk RGB IR Sandwich
[G19-ABI-FD-ir-sandwich]
With this product(s) it is possible to monitor those cloud top features ofmature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and...
With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature.
Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.
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GOES East ABI Full Disk RGB Night Microphysics
[G19-ABI-FD-night-microphysics]
The distinction between low clouds and fog in
satellite imagery is often achallenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the...
The distinction between low clouds and fog in
satellite imagery is often a challenge. While the
difference in the 10.4 and 3.9 µm channels has
been a regularly applied product to meet
aviation forecast needs, the Nighttime
Microphysics (NtMicro) RGB adds another
channel difference (12.4- 10.4 µm) as a proxy to
cloud thickness and repeats the use of the 10.4
µm thermal channel to enhance areas of warm
(i.e. low) clouds where fog is more likely. The
NtMicro RGB is also an efficient tool to quickly
identify other cloud types in the mid and upper
atmosphere.
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GOES East ABI Full Disk RGB Snow-Fog
[G19-ABI-FD-snow-fog]
On heritage GOES, it was difficult to distinguish white “reflective”snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the...
On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.
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GOES East ABI Full Disk RGB SO2
[G19-ABI-FD-so2]
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere duringvolcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and...
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.
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GOES East ABI Full Disk RGB True Color
[G19-ABI-FD-true-color]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
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GOES East ABI Full Disk RGB Volcanic Ash
[G19-ABI-FD-ash]
The Volcanic Ash False Color product determines the location for satellitepixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and...
The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.
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GOES East CONUS FLS Cloud Thickness
[G19-L2-CONUS-FLS-Thickness]
Cloud thickness: Estimate of the geometric thickness (cloud top - cloudbase) of a single layer liquid water stratus cloud.
Cloud thickness: Estimate of the geometric thickness (cloud top - cloud base) of a single layer liquid water stratus cloud.
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GOES East CONUS FLS IFR Fog Probability
[G19-L2-CONUS-FLS-IFR]
IFR probability: Probability that IFR (or lower) flight rule category ispresent for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
IFR probability: Probability that IFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
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GOES East CONUS FLS LIFR Fog Probability
[G19-L2-CONUS-FLS-LIFR]
LIFR probability: Probability that LIFR (or lower) flight rule category ispresent for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
LIFR probability: Probability that LIFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
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GOES East CONUS FLS MVFR Fog Probability
[G19-L2-CONUS-FLS-MVFR]
MVFR probability: Probability that MVFR (or lower) flight rule category ispresent for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
MVFR probability: Probability that MVFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
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GOES East ABI Meso1 B01 "Blue" Visible
[G19-ABI-MESO1-BAND01]
The 0.47 µm, or “Blue” visible band, is one of two visible bands onthe ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides...
The 0.47 µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data
for monitoring aerosols. Included on NASA’s
MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47 µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47 µm band is more sensitive to aerosols / dust / smoke because it samples a part of the electromagnetic spectrum where clear-sky atmospheric scattering is important.
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GOES East ABI Meso1 B02 Hi-Res "Red" Visible
[G19-ABI-MESO1-BAND02]
The ‘Red’ Visible band – 0.64 µm – has the
finest spatialresolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air...
The ‘Red’ Visible band – 0.64 µm – has the
finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color” imagery.
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GOES East ABI Meso1 B03 "Veggie"
[G19-ABI-MESO1-BAND03]
The 0.86 μm band (a reflective band) detects daytime clouds, fog, andaerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86...
The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.
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GOES East ABI Meso1 B04 Cirrus
[G19-ABI-MESO1-BAND04]
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABIin that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during...
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABI in that it
occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.
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GOES East ABI Meso1 B05 Snow/Ice
[G19-ABI-MESO1-BAND05]
The Snow/Ice band takes advantage of the
difference between the refractioncomponents of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than...
The Snow/Ice band takes advantage of the
difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.
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GOES East ABI Meso1 B06 Cloud Particle Size
[G19-ABI-MESO1-BAND06]
The 2.24 μm band, in conjunction with other bands, enables cloud particlesize estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate...
The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free background over land), to create cloud masking and to detect hot spots.
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GOES East ABI Meso1 B07 "Fire"
[G19-ABI-MESO1-BAND07]
The 3.9 μm band can be used to identify fog and low clouds at night,identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day....
The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.
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GOES East ABI Meso1 B08 Upper-level Water Vapor
[G19-ABI-MESO1-BAND08]
The 6.2 µm “Upper-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating upper/ mid-level moisture (for legacy vertical moisture
profiles) and identifying regions where the
potential for turbulence exists. Further, it can be used to validate numerical model initialization and warming/cooling with time can reveal vertical motions at mid- and upper levels.
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GOES East ABI Meso1 B09 Mid-level Water Vapor
[G19-ABI-MESO1-BAND09]
The 6.9 µm “Mid-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level
moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show
cooling because of absorption of energy at 6.9µm by water vapor.
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GOES East ABI Meso1 B10 Lower-level Water Vapor
[G19-ABI-MESO1-BAND10]
The 7.3 µm “Lower-level water vapor” band is one of three water vaporbands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track...
The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes thatare rich in sulphur dioxide (SO2) and track LakeEffect
snow bands.
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GOES East ABI Meso1 B11 Cloud Phase
[G19-ABI-MESO1-BAND11]
The infrared 8.5 μm band is a window channel; there is little atmosphericabsorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is...
The infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived
brightness temperature. Water droplets also
have different emissivity properties for 8.5μm radiation compared to other wavelengths. The 8.5μm band was not available on either the Legacy GOES Imager or GOES Sounder.
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GOES East ABI Meso1 B12 Ozone
[G19-ABI-MESO1-BAND12]
The 9.6 μm band gives information both
day and night about the dynamicsof the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor...
The 9.6 μm band gives information both
day and night about the dynamics of
the atmosphere near the tropopause.
This band shows cooler temperatures
than the clean window band because
both ozone and water vapor absorb 9.6
μm atmospheric energy. The cooling
effect is especially apparent at large
zenith angles. This band alone cannot
diagnose total column ozone: product
generation using other bands will be
necessary for that.
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GOES East ABI Meso1 B13 "Clean" Infrared
[G19-ABI-MESO1-BAND13]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES East ABI Meso1 B13 "Clean" Infrared enhanced
[G19-ABI-MESO1-BAND13-GRAD]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES East ABI Meso1 B14 Infrared
[G19-ABI-MESO1-BAND14]
The infrared 11.2 μm band is a window
channel; however, there isabsorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will...
The infrared 11.2 μm band is a window
channel; however, there is absorption of
energy by water vapor at this wavelength.
Brightness Temperatures (BTs) are affected by
this absorption, and 11.2 μm BTs will be cooler
than clean window (10.3 μm) BTs – by an
amount that is a function of the amount of
moisture in the atmosphere. This band has
similarities to the legacy infrared channel at
10.7μm.
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GOES East ABI Meso1 B15 "Dirty" Infrared
[G19-ABI-MESO1-BAND15]
Absorption and re-emission of water vapor,
particularly in the lowertroposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more...
Absorption and re-emission of water vapor,
particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3μm) for the monitoring of simple atmospheric phenomena.
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GOES East ABI Meso1 B16 Carbon Dioxide
[G19-ABI-MESO1-BAND16]
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band canbe used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface...
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band can be used to
delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of
weather events.
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GOES East ABI Meso1 RGB Air Mass
[G19-ABI-MESO1-airmass]
The Air Mass RGB is used to diagnose the
environment surrounding synopticsystems by enhancing temperature and moisture characteristics of air masses. Cyclogenesis can be inferred by the identification of warm,...
The Air Mass RGB is used to diagnose the
environment surrounding synoptic systems by enhancing temperature and moisture
characteristics of air masses. Cyclogenesis can
be inferred by the identification of warm, dry,
ozone-rich descending stratospheric air
associated with jet streams and potential
vorticity (PV) anomalies. The RGB can be used to
validate the location of PV anomalies in model
data. Additionally, this RGB can distinguish
between polar and tropical air masses, especially along upper-level frontal boundaries and identify high-, mid-, and low-level clouds.
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GOES East ABI Meso1 RGB Cloud Phase
[G19-ABI-MESO1-cloud-phase]
The Baseline Cloud Phase product describes the cloud-top composition. Thereare four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid...
The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.
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GOES East ABI Meso1 RGB Day Convection
[G19-ABI-MESO1-convection]
The Day Convection RGB was designed to
emphasize convection with strongupdrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the...
The Day Convection RGB was designed to
emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.
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GOES East ABI Meso1 RGB Day Microphysics
[G19-ABI-MESO1-day-microphysics-abi]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
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GOES East ABI Meso1 RGB Differential Water Vapor
[G19-ABI-MESO1-water-vapors2]
The Differential Water Vapor RGB was designed to analyze water vapordistribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and...
The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries,
trough/ridge patterns, potential vorticity (PV)
anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and
tropopause fold-driven high-impact wind events.
Analysis of moist/dry layers is also important for
predicting changes in hurricane intensity and
extratropical transition.
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GOES East ABI Meso1 RGB Dust
[G19-ABI-MESO1-dust]
Dust can be hard to see in VIS and IR imagery because it is optically thin,or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band...
Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.
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GOES East ABI Meso1 RGB Fire Temperature
[G19-ABI-MESO1-fire-temperature-awips]
This RGB allows the user to identify where the most intense fires areoccurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background...
This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.
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GOES East ABI Meso1 RGB Geo Color
[G19-ABI-MESO1-geo-color]
GeoColor imagery provides as close an
approximation to daytime True Colorimagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark...
GeoColor imagery provides as close an
approximation to daytime True Color imagery
as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights
database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.
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GOES East ABI Meso1 RGB IR Sandwich
[G19-ABI-MESO1-ir-sandwich]
With this product(s) it is possible to monitor those cloud top features ofmature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and...
With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature.
Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.
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GOES East ABI Meso1 RGB Night Microphysics
[G19-ABI-MESO1-night-microphysics]
The distinction between low clouds and fog in
satellite imagery is often achallenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the...
The distinction between low clouds and fog in
satellite imagery is often a challenge. While the
difference in the 10.4 and 3.9 µm channels has
been a regularly applied product to meet
aviation forecast needs, the Nighttime
Microphysics (NtMicro) RGB adds another
channel difference (12.4- 10.4 µm) as a proxy to
cloud thickness and repeats the use of the 10.4
µm thermal channel to enhance areas of warm
(i.e. low) clouds where fog is more likely. The
NtMicro RGB is also an efficient tool to quickly
identify other cloud types in the mid and upper
atmosphere.
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GOES East ABI Meso1 RGB Snow-Fog
[G19-ABI-MESO1-snow-fog]
On heritage GOES, it was difficult to distinguish white “reflective”snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the...
On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.
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GOES East ABI Meso1 RGB SO2
[G19-ABI-MESO1-so2]
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere duringvolcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and...
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.
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GOES East ABI Meso1 RGB True Color
[G19-ABI-MESO1-true-color]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
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GOES East ABI Meso1 RGB Volcanic Ash
[G19-ABI-MESO1-ash]
The Volcanic Ash False Color product determines the location for satellitepixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and...
The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.
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GOES East ABI Meso2 B01 "Blue" Visible
[G19-ABI-MESO2-BAND01]
The 0.47 µm, or “Blue” visible band, is one of two visible bands onthe ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established...
The 0.47 µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47 µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47 µm
band is more sensitive to aerosols / dust /
smoke because it samples a part of the
electromagnetic spectrum where clear-sky
atmospheric scattering is important.
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GOES East ABI Meso2 B02 Hi-Res "Red" Visible
[G19-ABI-MESO2-BAND02]
The ‘Red’ Visible band – 0.64 µm – has the
finest spatialresolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air...
The ‘Red’ Visible band – 0.64 µm – has the
finest spatial resolution (0.5 km at the subsatellite
point) of all ABI bands. Thus it is ideal
to identify small-scale features such as river
fogs and fog/clear air boundaries, or
overshooting tops or cumulus clouds. It has
also been used to document daytime snow
and ice cover, diagnose low-level cloud-drift
winds, assist with detection of volcanic ash
and analysis of hurricanes and winter storms.
The ‘Red’ Visible band is also essential for
creation of “true color” imagery.
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GOES East ABI Meso2 B03 "Veggie"
[G19-ABI-MESO2-BAND03]
The 0.86 μm band (a reflective band) detects daytime clouds, fog, andaerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86...
The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.
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GOES East ABI Meso2 B04 Cirrus
[G19-ABI-MESO2-BAND04]
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABIin that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during...
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABI in that it
occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.
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GOES East ABI Meso2 B05 Snow/Ice
[G19-ABI-MESO2-BAND05]
The Snow/Ice band takes advantage of the
difference between the refractioncomponents of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than...
The Snow/Ice band takes advantage of the
difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.
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GOES East ABI Meso2 B06 Cloud Particle Size
[G19-ABI-MESO2-BAND06]
IThe 2.24 μm band, in conjunction with other bands, enables cloud particlesize estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate...
IThe 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free
background over land), to create cloud
masking and to detect hot spots.
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GOES East ABI Meso2 B07 "Fire"
[G19-ABI-MESO2-BAND07]
The 3.9 μm band can be used to identify fog and low clouds at night,identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day....
The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.
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GOES East ABI Meso2 B08 Upper-level Water Vapor
[G19-ABI-MESO2-BAND08]
The 6.2 µm “Upper-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating upper/ mid-level moisture (for legacy vertical moisture
profiles) and identifying regions where the
potential for turbulence exists. Further, it can be used to validate numerical model initialization and warming/cooling with time can reveal vertical motions at mid- and upper levels.
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GOES East ABI Meso2 B09 Mid-level Water Vapor
[G19-ABI-MESO2-BAND09]
The 6.9 µm “Mid-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles)
and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9µm by water vapor.
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GOES East ABI Meso2 B10 Lower-level Water Vapor
[G19-ABI-MESO2-BAND10]
The 7.3 µm “Lower-level water vapor” band is one of three water vaporbands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track...
The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect
snow bands.
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GOES East ABI Meso2 B11 Cloud Phase
[G19-ABI-MESO2-BAND11]
The infrared 8.5 μm band is a window channel; there is little atmosphericabsorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is...
The infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived
brightness temperature. Water droplets also
have different emissivity properties for 8.5μm radiation compared to other wavelengths. The 8.5μm band was not available on either the Legacy GOES Imager or GOES Sounder.
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GOES East ABI Meso2 B12 Ozone
[G19-ABI-MESO2-BAND12]
The 9.6 μm band gives information both
day and night about the dynamicsof the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor...
The 9.6 μm band gives information both
day and night about the dynamics of
the atmosphere near the tropopause.
This band shows cooler temperatures
than the clean window band because
both ozone and water vapor absorb 9.6
μm atmospheric energy. The cooling
effect is especially apparent at large
zenith angles. This band alone cannot
diagnose total column ozone: product
generation using other bands will be
necessary for that.
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GOES East ABI Meso2 B13 "Clean" Infrared
[G19-ABI-MESO2-BAND13]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES East ABI Meso2 B13 "Clean" Infrared enhanced
[G19-ABI-MESO2-BAND13-GRAD]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES East ABI Meso2 B14 Infrared
[G19-ABI-MESO2-BAND14]
The infrared 11.2 μm band is a window
channel; however, there isabsorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will...
The infrared 11.2 μm band is a window
channel; however, there is absorption of
energy by water vapor at this wavelength.
Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7μm.
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GOES East ABI Meso2 B15 "Dirty" Infrared
[G19-ABI-MESO2-BAND15]
Absorption and re-emission of water vapor,
particularly in the lowertroposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more...
Absorption and re-emission of water vapor,
particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3μm) for the monitoring of simple atmospheric phenomena.
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GOES East ABI Meso2 B16 Carbon Dioxide
[G19-ABI-MESO2-BAND16]
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band canbe used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface...
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band can be used to
delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.
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GOES East ABI Meso2 RGB Air Mass
[G19-ABI-MESO2-airmass]
The Air Mass RGB is used to diagnose the
environment surrounding synopticsystems by enhancing temperature and moisture characteristics of air masses. Cyclogenesis can be inferred by the identification of warm,...
The Air Mass RGB is used to diagnose the
environment surrounding synoptic systems by enhancing temperature and moisture
characteristics of air masses. Cyclogenesis can
be inferred by the identification of warm, dry,
ozone-rich descending stratospheric air
associated with jet streams and potential
vorticity (PV) anomalies. The RGB can be used to
validate the location of PV anomalies in model
data. Additionally, this RGB can distinguish
between polar and tropical air masses, especially along upper-level frontal boundaries and identify high-, mid-, and low-level clouds.
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GOES East ABI Meso2 RGB Cloud Phase
[G19-ABI-MESO2-cloud-phase]
The Baseline Cloud Phase product describes the cloud-top composition. Thereare four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid...
The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.
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GOES East ABI Meso2 RGB Day Convection
[G19-ABI-MESO2-convection]
The Day Convection RGB was designed to
emphasize convection with strongupdrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the...
The Day Convection RGB was designed to
emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.
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GOES East ABI Meso2 RGB Day Microphysics
[G19-ABI-MESO2-day-microphysics-abi]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
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GOES East ABI Meso2 RGB Differential Water Vapor
[G19-ABI-MESO2-water-vapors2]
The Differential Water Vapor RGB was designed to analyze water vapordistribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and...
The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries,
trough/ridge patterns, potential vorticity (PV)
anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and
tropopause fold-driven high-impact wind events.
Analysis of moist/dry layers is also important for
predicting changes in hurricane intensity and
extratropical transition.
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GOES East ABI Meso2 RGB Dust
[G19-ABI-MESO2-dust]
Dust can be hard to see in VIS and IR imagery because it is optically thin,or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band...
Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.
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GOES East ABI Meso2 RGB Fire Temperature
[G19-ABI-MESO2-fire-temperature-awips]
This RGB allows the user to identify where the most intense fires areoccurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background...
This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.
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GOES East ABI Meso2 RGB Geo Color
[G19-ABI-MESO2-geo-color]
GeoColor imagery provides as close an
approximation to daytime True Colorimagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark...
GeoColor imagery provides as close an
approximation to daytime True Color imagery
as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights
database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.
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GOES East ABI Meso2 RGB IR Sandwich
[G19-ABI-MESO2-ir-sandwich]
With this product(s) it is possible to monitor those cloud top features ofmature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and...
With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature.
Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.
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GOES East ABI Meso2 RGB Night Microphysics
[G19-ABI-MESO2-night-microphysics]
The distinction between low clouds and fog in
satellite imagery is often achallenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the...
The distinction between low clouds and fog in
satellite imagery is often a challenge. While the
difference in the 10.4 and 3.9 µm channels has
been a regularly applied product to meet
aviation forecast needs, the Nighttime
Microphysics (NtMicro) RGB adds another
channel difference (12.4- 10.4 µm) as a proxy to
cloud thickness and repeats the use of the 10.4
µm thermal channel to enhance areas of warm
(i.e. low) clouds where fog is more likely. The
NtMicro RGB is also an efficient tool to quickly
identify other cloud types in the mid and upper
atmosphere.
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GOES East ABI Meso2 RGB Snow-Fog
[G19-ABI-MESO2-snow-fog]
On heritage GOES, it was difficult to distinguish white “reflective”snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the...
On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.
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GOES East ABI Meso2 RGB SO2
[G19-ABI-MESO2-so2]
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere duringvolcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and...
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.
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GOES East ABI Meso2 RGB True Color
[G19-ABI-MESO2-true-color]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
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GOES East ABI Meso2 RGB Volcanic Ash
[G19-ABI-MESO2-ash]
The Volcanic Ash False Color product determines the location for satellitepixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and...
The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.
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GOES East ABI ConUS RGB Air Mass
[G19-ABI-CONUS-airmass]
The Air Mass RGB is used to diagnose the
environment surrounding synopticsystems by enhancing temperature and moisture characteristics of air masses. Cyclogenesis can be inferred by the identification of warm,...
The Air Mass RGB is used to diagnose the
environment surrounding synoptic systems by enhancing temperature and moisture
characteristics of air masses. Cyclogenesis can
be inferred by the identification of warm, dry,
ozone-rich descending stratospheric air
associated with jet streams and potential
vorticity (PV) anomalies. The RGB can be used to
validate the location of PV anomalies in model
data. Additionally, this RGB can distinguish
between polar and tropical air masses, especially along upper-level frontal boundaries and identify high-, mid-, and low-level clouds.
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GOES East ABI ConUS RGB Cloud Phase
[G19-ABI-CONUS-cloud-phase]
The Baseline Cloud Phase product describes the cloud-top composition.There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase...
The Baseline Cloud Phase product describes the cloud-top composition. There are four phase
categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid
water and ice) clouds and glaciated (ice) clouds.
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GOES East ABI ConUS RGB Day Convection
[G19-ABI-CONUS-convection]
The Day Convection RGB was designed to
emphasize convection with strongupdrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the...
The Day Convection RGB was designed to
emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the
mature storm stage.
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GOES East ABI ConUS RGB Day Microphysics
[G19-ABI-CONUS-day-microphysics-abi]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
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GOES East ABI ConUS RGB Differential Water Vapor
[G19-ABI-CONUS-water-vapors2]
The Differential Water Vapor RGB was designed to analyze water vapordistribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and...
The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries,
trough/ridge patterns, potential vorticity (PV)
anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and
tropopause fold-driven high-impact wind events.
Analysis of moist/dry layers is also important for
predicting changes in hurricane intensity and
extratropical transition.
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GOES East ABI ConUS RGB Dust
[G19-ABI-CONUS-dust]
Dust can be hard to see in VIS and IR imagery because it is optically thin,or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band...
Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the
IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in
color at night depending on height. Dust is also
distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud
phase and thicknesses.
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GOES East ABI ConUS RGB Fire Temperature
[G19-ABI-CONUS-fire-temperature-awips]
This RGB allows the user to identify where the most intense fires areoccurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background...
This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar
radiation and surface reflectance increases. This means
that fires need to be more intense in order to be
detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.
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GOES East ABI ConUS RGB Geo Color
[G19-ABI-CONUS-geo-color]
GeoColor imagery provides as close an
approximation to daytime True Colorimagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark...
GeoColor imagery provides as close an
approximation to daytime True Color imagery
as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights
database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.
|
|
GOES East ABI ConUS RGB IR Sandwich
[G19-ABI-CONUS-ir-sandwich]
With this product(s) it is possible to monitor those cloud top features ofmature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and...
With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature.
Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.
|
|
GOES East ABI ConUS RGB Night Microphysics
[G19-ABI-CONUS-night-microphysics]
The distinction between low clouds and fog in
satellite imagery is often achallenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the...
The distinction between low clouds and fog in
satellite imagery is often a challenge. While the
difference in the 10.4 and 3.9 µm channels has
been a regularly applied product to meet
aviation forecast needs, the Nighttime
Microphysics (NtMicro) RGB adds another
channel difference (12.4- 10.4 µm) as a proxy to
cloud thickness and repeats the use of the 10.4
µm thermal channel to enhance areas of warm
(i.e. low) clouds where fog is more likely. The
NtMicro RGB is also an efficient tool to quickly
identify other cloud types in the mid and upper
atmosphere.
|
|
GOES East ABI ConUS RGB Snow-Fog
[G19-ABI-CONUS-snow-fog]
On heritage GOES, it was difficult to distinguish white “reflective”snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the...
On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds
varies across the visible, near infrared, and
infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.
|
|
GOES East ABI ConUS RGB SO2
[G19-ABI-CONUS-so2]
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere duringvolcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and...
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.
|
|
GOES East ABI ConUS RGB True Color
[G19-ABI-CONUS-true-color]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
|
|
GOES East ABI ConUS RGB Volcanic Ash
[G19-ABI-CONUS-ash]
The Volcanic Ash False Color product determines the location for satellitepixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and...
The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.
|
|
GOES East ABI Full Disk RGB Air Mass
[G19-ABI-FD-airmass]
The Air Mass RGB is used to diagnose the
environment surrounding synopticsystems by enhancing temperature and moisture characteristics of air masses. Cyclogenesis can be inferred by the identification of warm,...
The Air Mass RGB is used to diagnose the
environment surrounding synoptic systems by enhancing temperature and moisture
characteristics of air masses. Cyclogenesis can
be inferred by the identification of warm, dry,
ozone-rich descending stratospheric air
associated with jet streams and potential
vorticity (PV) anomalies. The RGB can be used to
validate the location of PV anomalies in model
data. Additionally, this RGB can distinguish
between polar and tropical air masses, especially along upper-level frontal boundaries and identify high-, mid-, and low-level clouds.
|
|
GOES East ABI Full Disk RGB Cloud Phase
[G19-ABI-FD-cloud-phase]
The Baseline Cloud Phase product describes the cloud-top composition. Thereare four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid...
The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.
|
|
GOES East ABI Full Disk RGB Day Convection
[G19-ABI-FD-convection]
The Day Convection RGB was designed to
emphasize convection with strongupdrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the...
The Day Convection RGB was designed to
emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.
|
|
GOES East ABI Full Disk RGB Day Microphysics
[G19-ABI-FD-day-microphysics-abi]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
|
|
GOES East ABI Full Disk RGB Differential Water Vapor
[G19-ABI-FD-water-vapors2]
The Differential Water Vapor RGB was designed to analyze water vapordistribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and...
The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries,
trough/ridge patterns, potential vorticity (PV)
anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and
tropopause fold-driven high-impact wind events.
Analysis of moist/dry layers is also important for
predicting changes in hurricane intensity and
extratropical transition.
|
|
GOES East ABI Full Disk RGB Dust
[G19-ABI-FD-dust]
Dust can be hard to see in VIS and IR imagery because it is optically thin,or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band...
Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.
|
|
GOES East ABI Full Disk RGB Fire Temperature
[G19-ABI-FD-fire-temperature-awips]
This RGB allows the user to identify where the most intense fires areoccurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background...
This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.
|
|
|
|
GOES East ABI Full Disk RGB Geo Color
[G19-ABI-FD-geo-color]
GeoColor imagery provides as close an
approximation to daytime True Colorimagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark...
GeoColor imagery provides as close an
approximation to daytime True Color imagery
as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights
database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.
|
|
GOES East ABI Full Disk RGB IR Sandwich
[G19-ABI-FD-ir-sandwich]
With this product(s) it is possible to monitor those cloud top features ofmature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and...
With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature.
Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.
|
|
GOES East ABI Full Disk RGB Night Microphysics
[G19-ABI-FD-night-microphysics]
The distinction between low clouds and fog in
satellite imagery is often achallenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the...
The distinction between low clouds and fog in
satellite imagery is often a challenge. While the
difference in the 10.4 and 3.9 µm channels has
been a regularly applied product to meet
aviation forecast needs, the Nighttime
Microphysics (NtMicro) RGB adds another
channel difference (12.4- 10.4 µm) as a proxy to
cloud thickness and repeats the use of the 10.4
µm thermal channel to enhance areas of warm
(i.e. low) clouds where fog is more likely. The
NtMicro RGB is also an efficient tool to quickly
identify other cloud types in the mid and upper
atmosphere.
|
|
GOES East ABI Full Disk RGB Snow-Fog
[G19-ABI-FD-snow-fog]
On heritage GOES, it was difficult to distinguish white “reflective”snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the...
On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.
|
|
GOES East ABI Full Disk RGB SO2
[G19-ABI-FD-so2]
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere duringvolcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and...
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.
|
|
GOES East ABI Full Disk RGB True Color
[G19-ABI-FD-true-color]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
|
|
GOES East ABI Full Disk RGB Volcanic Ash
[G19-ABI-FD-ash]
The Volcanic Ash False Color product determines the location for satellitepixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and...
The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.
|
|
GOES East ABI Meso1 RGB Air Mass
[G19-ABI-MESO1-airmass]
The Air Mass RGB is used to diagnose the
environment surrounding synopticsystems by enhancing temperature and moisture characteristics of air masses. Cyclogenesis can be inferred by the identification of warm,...
The Air Mass RGB is used to diagnose the
environment surrounding synoptic systems by enhancing temperature and moisture
characteristics of air masses. Cyclogenesis can
be inferred by the identification of warm, dry,
ozone-rich descending stratospheric air
associated with jet streams and potential
vorticity (PV) anomalies. The RGB can be used to
validate the location of PV anomalies in model
data. Additionally, this RGB can distinguish
between polar and tropical air masses, especially along upper-level frontal boundaries and identify high-, mid-, and low-level clouds.
|
|
GOES East ABI Meso1 RGB Cloud Phase
[G19-ABI-MESO1-cloud-phase]
The Baseline Cloud Phase product describes the cloud-top composition. Thereare four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid...
The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.
|
|
GOES East ABI Meso1 RGB Day Convection
[G19-ABI-MESO1-convection]
The Day Convection RGB was designed to
emphasize convection with strongupdrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the...
The Day Convection RGB was designed to
emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.
|
|
GOES East ABI Meso1 RGB Day Microphysics
[G19-ABI-MESO1-day-microphysics-abi]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
|
|
GOES East ABI Meso1 RGB Differential Water Vapor
[G19-ABI-MESO1-water-vapors2]
The Differential Water Vapor RGB was designed to analyze water vapordistribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and...
The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries,
trough/ridge patterns, potential vorticity (PV)
anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and
tropopause fold-driven high-impact wind events.
Analysis of moist/dry layers is also important for
predicting changes in hurricane intensity and
extratropical transition.
|
|
GOES East ABI Meso1 RGB Dust
[G19-ABI-MESO1-dust]
Dust can be hard to see in VIS and IR imagery because it is optically thin,or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band...
Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.
|
|
GOES East ABI Meso1 RGB Fire Temperature
[G19-ABI-MESO1-fire-temperature-awips]
This RGB allows the user to identify where the most intense fires areoccurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background...
This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.
|
|
|
|
GOES East ABI Meso1 RGB Geo Color
[G19-ABI-MESO1-geo-color]
GeoColor imagery provides as close an
approximation to daytime True Colorimagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark...
GeoColor imagery provides as close an
approximation to daytime True Color imagery
as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights
database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.
|
|
GOES East ABI Meso1 RGB IR Sandwich
[G19-ABI-MESO1-ir-sandwich]
With this product(s) it is possible to monitor those cloud top features ofmature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and...
With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature.
Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.
|
|
GOES East ABI Meso1 RGB Night Microphysics
[G19-ABI-MESO1-night-microphysics]
The distinction between low clouds and fog in
satellite imagery is often achallenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the...
The distinction between low clouds and fog in
satellite imagery is often a challenge. While the
difference in the 10.4 and 3.9 µm channels has
been a regularly applied product to meet
aviation forecast needs, the Nighttime
Microphysics (NtMicro) RGB adds another
channel difference (12.4- 10.4 µm) as a proxy to
cloud thickness and repeats the use of the 10.4
µm thermal channel to enhance areas of warm
(i.e. low) clouds where fog is more likely. The
NtMicro RGB is also an efficient tool to quickly
identify other cloud types in the mid and upper
atmosphere.
|
|
GOES East ABI Meso1 RGB Snow-Fog
[G19-ABI-MESO1-snow-fog]
On heritage GOES, it was difficult to distinguish white “reflective”snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the...
On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.
|
|
GOES East ABI Meso1 RGB SO2
[G19-ABI-MESO1-so2]
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere duringvolcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and...
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.
|
|
GOES East ABI Meso1 RGB True Color
[G19-ABI-MESO1-true-color]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
|
|
GOES East ABI Meso1 RGB Volcanic Ash
[G19-ABI-MESO1-ash]
The Volcanic Ash False Color product determines the location for satellitepixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and...
The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.
|
|
GOES East ABI Meso2 RGB Air Mass
[G19-ABI-MESO2-airmass]
The Air Mass RGB is used to diagnose the
environment surrounding synopticsystems by enhancing temperature and moisture characteristics of air masses. Cyclogenesis can be inferred by the identification of warm,...
The Air Mass RGB is used to diagnose the
environment surrounding synoptic systems by enhancing temperature and moisture
characteristics of air masses. Cyclogenesis can
be inferred by the identification of warm, dry,
ozone-rich descending stratospheric air
associated with jet streams and potential
vorticity (PV) anomalies. The RGB can be used to
validate the location of PV anomalies in model
data. Additionally, this RGB can distinguish
between polar and tropical air masses, especially along upper-level frontal boundaries and identify high-, mid-, and low-level clouds.
|
|
GOES East ABI Meso2 RGB Cloud Phase
[G19-ABI-MESO2-cloud-phase]
The Baseline Cloud Phase product describes the cloud-top composition. Thereare four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid...
The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.
|
|
GOES East ABI Meso2 RGB Day Convection
[G19-ABI-MESO2-convection]
The Day Convection RGB was designed to
emphasize convection with strongupdrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the...
The Day Convection RGB was designed to
emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.
|
|
GOES East ABI Meso2 RGB Day Microphysics
[G19-ABI-MESO2-day-microphysics-abi]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
|
|
GOES East ABI Meso2 RGB Differential Water Vapor
[G19-ABI-MESO2-water-vapors2]
The Differential Water Vapor RGB was designed to analyze water vapordistribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and...
The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries,
trough/ridge patterns, potential vorticity (PV)
anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and
tropopause fold-driven high-impact wind events.
Analysis of moist/dry layers is also important for
predicting changes in hurricane intensity and
extratropical transition.
|
|
GOES East ABI Meso2 RGB Dust
[G19-ABI-MESO2-dust]
Dust can be hard to see in VIS and IR imagery because it is optically thin,or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band...
Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.
|
|
GOES East ABI Meso2 RGB Fire Temperature
[G19-ABI-MESO2-fire-temperature-awips]
This RGB allows the user to identify where the most intense fires areoccurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background...
This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.
|
|
|
|
GOES East ABI Meso2 RGB Geo Color
[G19-ABI-MESO2-geo-color]
GeoColor imagery provides as close an
approximation to daytime True Colorimagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark...
GeoColor imagery provides as close an
approximation to daytime True Color imagery
as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights
database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.
|
|
GOES East ABI Meso2 RGB IR Sandwich
[G19-ABI-MESO2-ir-sandwich]
With this product(s) it is possible to monitor those cloud top features ofmature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and...
With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature.
Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.
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GOES East ABI Meso2 RGB Night Microphysics
[G19-ABI-MESO2-night-microphysics]
The distinction between low clouds and fog in
satellite imagery is often achallenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the...
The distinction between low clouds and fog in
satellite imagery is often a challenge. While the
difference in the 10.4 and 3.9 µm channels has
been a regularly applied product to meet
aviation forecast needs, the Nighttime
Microphysics (NtMicro) RGB adds another
channel difference (12.4- 10.4 µm) as a proxy to
cloud thickness and repeats the use of the 10.4
µm thermal channel to enhance areas of warm
(i.e. low) clouds where fog is more likely. The
NtMicro RGB is also an efficient tool to quickly
identify other cloud types in the mid and upper
atmosphere.
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GOES East ABI Meso2 RGB Snow-Fog
[G19-ABI-MESO2-snow-fog]
On heritage GOES, it was difficult to distinguish white “reflective”snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the...
On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.
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GOES East ABI Meso2 RGB SO2
[G19-ABI-MESO2-so2]
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere duringvolcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and...
Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.
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GOES East ABI Meso2 RGB True Color
[G19-ABI-MESO2-true-color]
As with the Day Cloud Phase Distinction RGB, this RGB allows a user todiscern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3...
As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day
Cloud Phase Distinction RGB in regions of clear skies.
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GOES East ABI Meso2 RGB Volcanic Ash
[G19-ABI-MESO2-ash]
The Volcanic Ash False Color product determines the location for satellitepixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and...
The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.
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G18-ABI-CONUS-cloud-phase
[G18-ABI-CONUS-cloud-phase]
G18-ABI-CONUS-cloud-phase
G18-ABI-CONUS-cloud-phase
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G18-ABI-CONUS-convection
[G18-ABI-CONUS-convection]
G18-ABI-CONUS-convection
G18-ABI-CONUS-convection
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G18-ABI-CONUS-day-microphysics-abi
[G18-ABI-CONUS-day-microphysics-abi]
G18-ABI-CONUS-day-microphysics-abi
G18-ABI-CONUS-day-microphysics-abi
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G18-ABI-CONUS-fire-temperature-awips
[G18-ABI-CONUS-fire-temperature-awips]
G18-ABI-CONUS-fire-temperature-awips
G18-ABI-CONUS-fire-temperature-awips
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G18-ABI-CONUS-ir-sandwich
[G18-ABI-CONUS-ir-sandwich]
G18-ABI-CONUS-ir-sandwich
G18-ABI-CONUS-ir-sandwich
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G18-ABI-CONUS-night-microphysics
[G18-ABI-CONUS-night-microphysics]
G18-ABI-CONUS-night-microphysics
G18-ABI-CONUS-night-microphysics
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G18-ABI-CONUS-true-color
[G18-ABI-CONUS-true-color]
View of G18-ABI-CONUS-geo-color
View of G18-ABI-CONUS-geo-color
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G18-ABI-CONUS-water-vapors2
[G18-ABI-CONUS-water-vapors2]
G18-ABI-CONUS-water-vapors2
G18-ABI-CONUS-water-vapors2
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GOES West ABI ConUS B01 "Blue" Visible
[G18-ABI-CONUS-BAND01]
The 0.47µm, or “Blue” visible band, is one of two visible bands on theABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established...
The 0.47µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47 µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47 µm band is more sensitive to aerosols / dust /smoke because it samples a part of the electromagnetic spectrum where clear-sky
atmospheric scattering is important
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GOES West ABI ConUS B02 Hi-Res "Red" Visible
[G18-ABI-CONUS-BAND02]
he ‘Red’ Visible band – 0.64 µm – has the
finest spatialresolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air...
he ‘Red’ Visible band – 0.64 µm – has the
finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color” imagery.
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GOES West ABI ConUS B03 "Veggie"
[G18-ABI-CONUS-BAND03]
The 0.86 μm band (a reflective band) detects daytime clouds, fog, andaerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86...
The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.
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GOES West ABI ConUS B04 Cirrus
[G18-ABI-CONUS-BAND04]
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABIin that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during...
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABI in that it
occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.
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GOES West ABI ConUS B05 Snow/Ice
[G18-ABI-CONUS-BAND05]
The Snow/Ice band takes advantage of the
difference between the refractioncomponents of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than...
The Snow/Ice band takes advantage of the
difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.
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GOES West ABI ConUS B06 Cloud Particle Size
[G18-ABI-CONUS-BAND06]
The 2.24 μm band, in conjunction with other bands, enables cloud particlesize estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate...
The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free background over land), to create cloud masking and to detect hot spots.
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GOES West ABI ConUS B07 "Fire"
[G18-ABI-CONUS-BAND07]
The 3.9 μm band can be used to identify fog and low clouds at night,identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day....
The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.
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GOES West ABI ConUS B07 "Fire" enhanced
[G18-ABI-CONUS-BAND07-FIRE]
The 3.9 μm band can be used to identify fog and low clouds at night,identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day....
The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.
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GOES West ABI ConUS B08 Upper-level Water Vapor
[G18-ABI-CONUS-BAND08]
http://cimss.ssec.wisc.edu/goes/OCLOFactSheetPDFs/ABIQuickGuide_Band08.pdfThe6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds,...
http://cimss.ssec.wisc.edu/goes/OCLOFactSheetPDFs/ABIQuickGuide_Band08.pdfThe 6.2 µm “Upper-level water vapor” band is
one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring.
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GOES West ABI ConUS B08 Upper-level Water Vapor enhanced
[G18-ABI-CONUS-BAND08-VAPR]
The 6.2 µm “Upper-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring.
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GOES West ABI ConUS B09 Mid-level Water Vapor
[G18-ABI-CONUS-BAND09]
The 6.9 µm “Mid-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles)
and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9 µm by water vapor.
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GOES West ABI ConUS B09 Mid-level Water Vapor enhanced
[G18-ABI-CONUS-BAND09-VAPR]
The 6.9 µm “Mid-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles)
and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9 µm by water vapor.
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GOES West ABI ConUS B10 Lower-level Water Vapor
[G18-ABI-CONUS-BAND10]
The 7.3 µm “Lower-level water vapor” band is one of three water vaporbands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track...
The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that
are rich in sulphur dioxide (SO2) and track LakeEffect snow bands.
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GOES West ABI ConUS B10 Lower-level Water Vapor enhanced
[G18-ABI-CONUS-BAND10-VAPR]
The 7.3 µm “Lower-level water vapor” band is one of three water vaporbands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track...
The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect
snow bands.
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GOES West ABI ConUS B11 Cloud Phase
[G18-ABI-CONUS-BAND11]
he infrared 8.5 μm band is a window channel; there is little atmosphericabsorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is...
he infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived brightness temperature. Water droplets also
have different emissivity properties for 8.5 μm radiation compared to other wavelengths. The 8.5 μm band was not available on either the Legacy GOES Imager or GOES Sounder.
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GOES West ABI ConUS B12 Ozone
[G18-ABI-CONUS-BAND12]
The 9.6 μm band gives information both
day and night about the dynamicsof the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor...
The 9.6 μm band gives information both
day and night about the dynamics of
the atmosphere near the tropopause.
This band shows cooler temperatures
than the clean window band because
both ozone and water vapor absorb 9.6
μm atmospheric energy. The cooling
effect is especially apparent at large
zenith angles. This band alone cannot
diagnose total column ozone: product
generation using other bands will be
necessary for that.
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GOES West ABI ConUS B13 "Clean" Infrared
[G18-ABI-CONUS-BAND13]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES West ABI ConUS B13 "Clean" Infrared enhanced
[G18-ABI-CONUS-BAND13-GRAD]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature
identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES West ABI ConUS B14 Infrared
[G18-ABI-CONUS-BAND14]
http://cimss.ssec.wisc.edu/goes/OCLOFactSheetPDFs/ABIQuickGuide_Band14.pdfTheinfrared 11.2 μm band is a window channel; however, there is absorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs)...
http://cimss.ssec.wisc.edu/goes/OCLOFactSheetPDFs/ABIQuickGuide_Band14.pdfThe infrared 11.2 μm band is a window
channel; however, there is absorption of
energy by water vapor at this wavelength.
Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7 μm.
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GOES West ABI ConUS B15 "Dirty" Infrared
[G18-ABI-CONUS-BAND15]
Absorption and re-emission of water vapor,
particularly in the lowertroposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more...
Absorption and re-emission of water vapor,
particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3 μm) for the monitoring of simple atmospheric phenomena.
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GOES West ABI ConUS B16 Carbon Dioxide
[G18-ABI-CONUS-BAND16]
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band canbe used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface...
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band can be used to
delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3 μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.
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G18-ABI-FD-day-microphysics-abi
[G18-ABI-FD-day-microphysics-abi]
G18-ABI-FD-day-microphysics-abi
G18-ABI-FD-day-microphysics-abi
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G18-ABI-FD-fire-temperature-awips
[G18-ABI-FD-fire-temperature-awips]
G18-ABI-FD-fire-temperature-awips
G18-ABI-FD-fire-temperature-awips
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G18-ABI-FD-night-microphysics
[G18-ABI-FD-night-microphysics]
G18-ABI-FD-night-microphysics
G18-ABI-FD-night-microphysics
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G18-ABI-FD-true-color
[G18-ABI-FD-true-color]
View of G18-ABI-FD-geo-color
View of G18-ABI-FD-geo-color
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G18-ABI-FD-water-vapors2
[G18-ABI-FD-water-vapors2]
G18-ABI-FD-water-vapors2
G18-ABI-FD-water-vapors2
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GOES West ABI ConUS B13 "Clean" Infrared enhanced
[G18-ABI-FD-BAND13-GRAD]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES West ABI Full Disk B01 "Blue" Visible
[G18-ABI-FD-BAND01]
The 0.47µm, or “Blue” visible band, is one of two visible bands on theABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established...
The 0.47µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47µm band is more sensitive to aerosols / dust /smoke because it samples a part of the electromagnetic spectrum where clear-sky atmospheric scattering is important.
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GOES West ABI Full Disk B02 Hi-Res "Red" Visible
[G18-ABI-FD-BAND02]
he ‘Red’ Visible band – 0.64 µm – has the
finest spatialresolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air...
he ‘Red’ Visible band – 0.64 µm – has the
finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color” imagery.
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GOES West ABI Full Disk B03 "Veggie"
[G18-ABI-FD-BAND03]
The 0.86 μm band (a reflective band) detects daytime clouds, fog, andaerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86...
The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.
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GOES West ABI Full Disk B04 Cirrus
[G18-ABI-FD-BAND04]
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABIin that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during...
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABI in that it
occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.
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GOES West ABI Full Disk B05 Snow/Ice
[G18-ABI-FD-BAND05]
The Snow/Ice band takes advantage of the
difference between the refractioncomponents of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than...
The Snow/Ice band takes advantage of the
difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.
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GOES West ABI Full Disk B06 Cloud Particle Size
[G18-ABI-FD-BAND06]
The 2.24 μm band, in conjunction with other bands, enables cloud particlesize estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate...
The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free
background over land), to create cloud
masking and to detect hot spots.
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GOES West ABI Full Disk B07 "Fire"
[G18-ABI-FD-BAND07]
The 3.9 μm band can be used to identify fog and low clouds at night,identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day....
The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well assignificant reflected solar radiation during the day.
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GOES West ABI Full Disk B08 Upper-level Water Vapor
[G18-ABI-FD-BAND08]
The 6.2 µm “Upper-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring.
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GOES West ABI Full Disk B09 Mid-level Water Vapor
[G18-ABI-FD-BAND09]
The 6.9 µm “Mid-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level
moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9 µm by water vapor.
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GOES West ABI Full Disk B10 Lower-level Water Vapor
[G18-ABI-FD-BAND10]
The 7.3 µm “Lower-level water vapor” band is one of three water vaporbands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track...
The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles),identify regions where the potential forturbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect
snow bands.
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GOES West ABI Full Disk B11 Cloud Phase
[G18-ABI-FD-BAND11]
he infrared 8.5 μm band is a window channel; there is little atmosphericabsorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is...
he infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived
brightness temperature. Water droplets also
have different emissivity properties for 8.5 μm radiation compared to other wavelengths. The 8.5 μm band was not available on either the Legacy GOES Imager or GOES Sounder.
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GOES West ABI Full Disk B12 Ozone
[G18-ABI-FD-BAND12]
The 9.6 μm band gives information both
day and night about the dynamicsof the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor...
The 9.6 μm band gives information both
day and night about the dynamics of
the atmosphere near the tropopause.
This band shows cooler temperatures
than the clean window band because
both ozone and water vapor absorb 9.6
μm atmospheric energy. The cooling
effect is especially apparent at large
zenith angles. This band alone cannot
diagnose total column ozone: product
generation using other bands will be
necessary for that.
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GOES West ABI Full Disk B13 "Clean" Infrared
[G18-ABI-FD-BAND13]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES West ABI Full Disk B14 Infrared
[G18-ABI-FD-BAND14]
The infrared 11.2 μm band is a window
channel; however, there isabsorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will...
The infrared 11.2 μm band is a window
channel; however, there is absorption of
energy by water vapor at this wavelength.
Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7 μm.
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GOES West ABI Full Disk B15 "Dirty" Infrared
[G18-ABI-FD-BAND15]
Absorption and re-emission of water vapor,
particularly in the lowertroposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more...
Absorption and re-emission of water vapor,
particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3 μm) for the monitoring of simple atmospheric phenomena.
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GOES West ABI Full Disk B16 Carbon Dioxide
[G18-ABI-FD-BAND16]
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band canbe used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface...
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band can be used to
delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3 μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of
weather events.
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GOES West CONUS FLS Cloud Thickness
[G18-L2-CONUS-FLS-Thickness]
Cloud thickness: Estimate of the geometric thickness (cloud top - cloudbase) of a single layer liquid water stratus cloud.
Cloud thickness: Estimate of the geometric thickness (cloud top - cloud base) of a single layer liquid water stratus cloud.
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GOES West CONUS FLS IFR Fog Probability
[G18-L2-CONUS-FLS-IFR]
IFR probability: Probability that IFR (or lower) flight rule category ispresent for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
IFR probability: Probability that IFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
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GOES West CONUS FLS LIFR Fog Probability
[G18-L2-CONUS-FLS-LIFR]
LIFR probability: Probability that LIFR (or lower) flight rule category ispresent for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
LIFR probability: Probability that LIFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
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GOES West CONUS FLS MVFR Fog Probability
[G18-L2-CONUS-FLS-MVFR]
MVFR probability: Probability that MVFR (or lower) flight rule category ispresent for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
MVFR probability: Probability that MVFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
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GOES West FD FLS Cloud Thickness
[G18-L2-FD-FLS-Thickness]
Cloud thickness: Estimate of the geometric thickness (cloud top - cloudbase) of a single layer liquid water stratus cloud.
Cloud thickness: Estimate of the geometric thickness (cloud top - cloud base) of a single layer liquid water stratus cloud.
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GOES West FD FLS IFR Fog Probability
[G18-L2-FD-FLS-IFR]
IFR probability: Probability that IFR (or lower) flight rule category ispresent for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
IFR probability: Probability that IFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
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GOES West FD FLS LIFR Fog Probability
[G18-L2-FD-FLS-LIFR]
LIFR probability: Probability that LIFR (or lower) flight rule category ispresent for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
LIFR probability: Probability that LIFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
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GOES West FD FLS MVFR Fog Probability
[G18-L2-FD-FLS-MVFR]
MVFR probability: Probability that MVFR (or lower) flight rule category ispresent for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
MVFR probability: Probability that MVFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
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G18-ABI-MESO1-cloud-phase
[G18-ABI-MESO1-cloud-phase]
G18-ABI-MESO1-cloud-phase
G18-ABI-MESO1-cloud-phase
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G18-ABI-MESO1-convection
[G18-ABI-MESO1-convection]
G18-ABI-MESO1-convection
G18-ABI-MESO1-convection
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G18-ABI-MESO1-day-microphysics-abi
[G18-ABI-MESO1-day-microphysics-abi]
G18-ABI-MESO1-day-microphysics-abi
G18-ABI-MESO1-day-microphysics-abi
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G18-ABI-MESO1-fire-temperature-awips
[G18-ABI-MESO1-fire-temperature-awips]
G18-ABI-MESO1-fire-temperature-awips
G18-ABI-MESO1-fire-temperature-awips
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G18-ABI-MESO1-ir-sandwich
[G18-ABI-MESO1-ir-sandwich]
G18-ABI-MESO1-ir-sandwich
G18-ABI-MESO1-ir-sandwich
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G18-ABI-MESO1-night-microphysics
[G18-ABI-MESO1-night-microphysics]
G18-ABI-MESO1-night-microphysics
G18-ABI-MESO1-night-microphysics
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G18-ABI-MESO1-true-color
[G18-ABI-MESO1-true-color]
View of G18-ABI-MESO1-geo-color
View of G18-ABI-MESO1-geo-color
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G18-ABI-MESO1-water-vapors2
[G18-ABI-MESO1-water-vapors2]
G18-ABI-MESO1-water-vapors2
G18-ABI-MESO1-water-vapors2
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GOES West ABI Meso1 B01 "Blue" Visible
[G18-ABI-MESO1-BAND01]
The 0.47µm, or “Blue” visible band, is one of two visible bands on theABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established...
The 0.47µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47µm band is more sensitive to aerosols / dust / smoke because it samples a part of the electromagnetic spectrum where clear-sky
atmospheric scattering is important
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GOES West ABI Meso1 B02 Hi-Res "Red" Visible
[G18-ABI-MESO1-BAND02]
he ‘Red’ Visible band – 0.64 µm – has the
finest spatialresolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air...
he ‘Red’ Visible band – 0.64 µm – has the
finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color” imagery.
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GOES West ABI Meso1 B03 "Veggie"
[G18-ABI-MESO1-BAND03]
The 0.86 μm band (a reflective band) detects daytime clouds, fog, andaerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86...
The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.
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GOES West ABI Meso1 B04 Cirrus
[G18-ABI-MESO1-BAND04]
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABIin that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during...
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABI in that it
occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.
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GOES West ABI Meso1 B05 Snow/Ice
[G18-ABI-MESO1-BAND05]
The Snow/Ice band takes advantage of the
difference between the refractioncomponents of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than...
The Snow/Ice band takes advantage of the
difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.
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GOES West ABI Meso1 B06 Cloud Particle Size
[G18-ABI-MESO1-BAND06]
The 2.24 μm band, in conjunction with other bands, enables cloud particlesize estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate...
The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free background over land), to create cloud masking and to detect hot spots.
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GOES West ABI Meso1 B07 "Fire"
[G18-ABI-MESO1-BAND07]
The 3.9 μm band can be used to identify fog and low clouds at night,identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day....
The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.
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GOES West ABI Meso1 B08 Upper-level Water Vapor
[G18-ABI-MESO1-BAND08]
The 6.2 µm “Upper-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring
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GOES West ABI Meso1 B09 Mid-level Water Vapor
[G18-ABI-MESO1-BAND09]
The 6.9 µm “Mid-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles)
and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9 µm by water vapor.
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GOES West ABI Meso1 B10 Lower-level Water Vapor
[G18-ABI-MESO1-BAND10]
The 7.3 µm “Lower-level water vapor” band is one of three water vaporbands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lower...
The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lower tropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect
snow bands.
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GOES West ABI Meso1 B11 Cloud Phase
[G18-ABI-MESO1-BAND11]
he infrared 8.5 μm band is a window channel; there is little atmosphericabsorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is...
he infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived brightness temperature. Water droplets also
have different emissivity properties for 8.5 μm radiation compared to other wavelengths. The 8.5 μm band was not available on either theLegacy GOES Imager or GOES Sounder.
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GOES West ABI Meso1 B12 Ozone
[G18-ABI-MESO1-BAND12]
The 9.6 μm band gives information both
day and night about the dynamicsof the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor...
The 9.6 μm band gives information both
day and night about the dynamics of
the atmosphere near the tropopause.
This band shows cooler temperatures
than the clean window band because
both ozone and water vapor absorb 9.6
μm atmospheric energy. The cooling
effect is especially apparent at large
zenith angles. This band alone cannot
diagnose total column ozone: product
generation using other bands will be
necessary for that.
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GOES West ABI Meso1 B13 "Clean" Infrared
[G18-ABI-MESO1-BAND13]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES West ABI Meso1 B13 "Clean" Infrared enhanced
[G18-ABI-MESO1-BAND13-GRAD]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES West ABI Meso1 B14 Infrared
[G18-ABI-MESO1-BAND14]
The infrared 11.2 μm band is a window
channel; however, there isabsorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will...
The infrared 11.2 μm band is a window
channel; however, there is absorption of
energy by water vapor at this wavelength.
Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7 μm.
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GOES West ABI Meso1 B15 "Dirty" Infrared
[G18-ABI-MESO1-BAND15]
Absorption and re-emission of water vapor,
particularly in the lowertroposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more...
Absorption and re-emission of water vapor,
particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3 μm) for the monitoring of simple atmospheric phenomena.
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GOES West ABI Meso1 B16 Carbon Dioxide
[G18-ABI-MESO1-BAND16]
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band canbe used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface...
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band can be used to
delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3 μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.
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G18-ABI-MESO2-cloud-phase
[G18-ABI-MESO2-cloud-phase]
G18-ABI-MESO2-cloud-phase
G18-ABI-MESO2-cloud-phase
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G18-ABI-MESO2-convection
[G18-ABI-MESO2-convection]
G18-ABI-MESO2-convection
G18-ABI-MESO2-convection
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G18-ABI-MESO2-day-microphysics-abi
[G18-ABI-MESO2-day-microphysics-abi]
G18-ABI-MESO2-day-microphysics-abi
G18-ABI-MESO2-day-microphysics-abi
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G18-ABI-MESO2-fire-temperature-awips
[G18-ABI-MESO2-fire-temperature-awips]
G18-ABI-MESO2-fire-temperature-awips
G18-ABI-MESO2-fire-temperature-awips
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G18-ABI-MESO2-ir-sandwich
[G18-ABI-MESO2-ir-sandwich]
G18-ABI-MESO2-ir-sandwich
G18-ABI-MESO2-ir-sandwich
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G18-ABI-MESO2-night-microphysics
[G18-ABI-MESO2-night-microphysics]
G18-ABI-MESO2-night-microphysics
G18-ABI-MESO2-night-microphysics
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G18-ABI-MESO2-true-color
[G18-ABI-MESO2-true-color]
View of G18-ABI-MESO2-geo-color
View of G18-ABI-MESO2-geo-color
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G18-ABI-MESO2-water-vapors2
[G18-ABI-MESO2-water-vapors2]
G18-ABI-MESO2-water-vapors2
G18-ABI-MESO2-water-vapors2
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GOES West ABI Meso2 B01 "Blue" Visible
[G18-ABI-MESO2-BAND01]
The 0.47µm, or “Blue” visible band, is one of two visible bands on theABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established...
The 0.47µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47µm band is more sensitive to aerosols / dust / smoke because it samples a part of the
electromagnetic spectrum where clear-sky
atmospheric scattering is important
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GOES West ABI Meso2 B02 Hi-Res "Red" Visible
[G18-ABI-MESO2-BAND02]
he ‘Red’ Visible band – 0.64 µm – has the
finest spatialresolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air...
he ‘Red’ Visible band – 0.64 µm – has the
finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color” imagery.
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GOES West ABI Meso2 B03 "Veggie"
[G18-ABI-MESO2-BAND03]
The 0.86 μm band (a reflective band) detects daytime clouds, fog, andaerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86...
The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.
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GOES West ABI Meso2 B04 Cirrus
[G18-ABI-MESO2-BAND04]
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABIin that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during...
The Cirrus Band (1.37 µm) is unique among
the reflective bands on the ABI in that it
occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.
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GOES West ABI Meso2 B05 Snow/Ice
[G18-ABI-MESO2-BAND05]
The Snow/Ice band takes advantage of the
difference between the refractioncomponents of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than...
The Snow/Ice band takes advantage of the
difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.
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GOES West ABI Meso2 B06 Cloud Particle Size
[G18-ABI-MESO2-BAND06]
The 2.24 μm band, in conjunction with other bands, enables cloud particlesize estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate...
The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free
background over land), to create cloud masking and to detect hot spots.
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GOES West ABI Meso2 B07 "Fire"
[G18-ABI-MESO2-BAND07]
The 3.9 μm band can be used to identify fog and low clouds at night,identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day....
The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.
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GOES West ABI Meso2 B08 Upper-level Water Vapor
[G18-ABI-MESO2-BAND08]
The 6.2 µm “Upper-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring
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GOES West ABI Meso2 B09 Mid-level Water Vapor
[G18-ABI-MESO2-BAND09]
The 6.9 µm “Mid-level water vapor” band is one of three water vaporbands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm...
The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might
exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9 µm by water vapor.
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GOES West ABI Meso2 B10 Lower-level Water Vapor
[G18-ABI-MESO2-BAND10]
The 7.3 µm “Lower-level water vapor” band is one of three water vaporbands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track...
The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles),
identify regions where the potential for
turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect snow bands.
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GOES West ABI Meso2 B11 Cloud Phase
[G18-ABI-MESO2-BAND11]
he infrared 8.5 μm band is a window channel; there is little atmosphericabsorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is...
he infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over
different soil types, affecting the perceived
brightness temperature. Water droplets also
have different emissivity properties for 8.5 μm radiation compared to other wavelengths. The 8.5 μm band was not available on either the Legacy GOES Imager or GOES Sounder.
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GOES West ABI Meso2 B12 Ozone
[G18-ABI-MESO2-BAND12]
The 9.6 μm band gives information both
day and night about the dynamicsof the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor...
The 9.6 μm band gives information both
day and night about the dynamics of
the atmosphere near the tropopause.
This band shows cooler temperatures
than the clean window band because
both ozone and water vapor absorb 9.6
μm atmospheric energy. The cooling
effect is especially apparent at large
zenith angles. This band alone cannot
diagnose total column ozone: product
generation using other bands will be
necessary for that.
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GOES West ABI Meso2 B13 "Clean" Infrared
[G18-ABI-MESO2-BAND13]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES West ABI Meso2 B13 "Clean" Infrared enhanced
[G18-ABI-MESO2-BAND13-GRAD]
The 10.3 μm “clean” infrared window band is less sensitive than otherinfrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric...
The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.
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GOES West ABI Meso2 B14 Infrared
[G18-ABI-MESO2-BAND14]
The infrared 11.2 μm band is a window
channel; however, there isabsorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will...
The infrared 11.2 μm band is a window
channel; however, there is absorption of
energy by water vapor at this wavelength.
Brightness Temperatures (BTs) are affected by
this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7 μm.
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GOES West ABI Meso2 B15 "Dirty" Infrared
[G18-ABI-MESO2-BAND15]
Absorption and re-emission of water vapor,
particularly in the lowertroposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more...
Absorption and re-emission of water vapor,
particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’.
The 10.3 μm “Clean Window” channel is a
better choice than the “Dirty Window” (12.3
μm) for the monitoring of simple atmospheric
phenomena.
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GOES West ABI Meso2 B16 Carbon Dioxide
[G18-ABI-MESO2-BAND16]
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band canbe used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface...
Products derived using the infrared 13.3 μm
“Carbon Dioxide” band can be used to
delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) skyobservations and to identify Volcanic Ash. The 13.3 μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.
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G18-ABI-CONUS-cloud-phase
[G18-ABI-CONUS-cloud-phase]
G18-ABI-CONUS-cloud-phase
G18-ABI-CONUS-cloud-phase
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G18-ABI-CONUS-convection
[G18-ABI-CONUS-convection]
G18-ABI-CONUS-convection
G18-ABI-CONUS-convection
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G18-ABI-CONUS-day-microphysics-abi
[G18-ABI-CONUS-day-microphysics-abi]
G18-ABI-CONUS-day-microphysics-abi
G18-ABI-CONUS-day-microphysics-abi
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G18-ABI-CONUS-fire-temperature-awips
[G18-ABI-CONUS-fire-temperature-awips]
G18-ABI-CONUS-fire-temperature-awips
G18-ABI-CONUS-fire-temperature-awips
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G18-ABI-CONUS-ir-sandwich
[G18-ABI-CONUS-ir-sandwich]
G18-ABI-CONUS-ir-sandwich
G18-ABI-CONUS-ir-sandwich
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G18-ABI-CONUS-night-microphysics
[G18-ABI-CONUS-night-microphysics]
G18-ABI-CONUS-night-microphysics
G18-ABI-CONUS-night-microphysics
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G18-ABI-CONUS-true-color
[G18-ABI-CONUS-true-color]
View of G18-ABI-CONUS-geo-color
View of G18-ABI-CONUS-geo-color
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G18-ABI-CONUS-water-vapors2
[G18-ABI-CONUS-water-vapors2]
G18-ABI-CONUS-water-vapors2
G18-ABI-CONUS-water-vapors2
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G18-ABI-FD-day-microphysics-abi
[G18-ABI-FD-day-microphysics-abi]
G18-ABI-FD-day-microphysics-abi
G18-ABI-FD-day-microphysics-abi
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G18-ABI-FD-fire-temperature-awips
[G18-ABI-FD-fire-temperature-awips]
G18-ABI-FD-fire-temperature-awips
G18-ABI-FD-fire-temperature-awips
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G18-ABI-FD-night-microphysics
[G18-ABI-FD-night-microphysics]
G18-ABI-FD-night-microphysics
G18-ABI-FD-night-microphysics
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G18-ABI-FD-true-color
[G18-ABI-FD-true-color]
View of G18-ABI-FD-geo-color
View of G18-ABI-FD-geo-color
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G18-ABI-FD-water-vapors2
[G18-ABI-FD-water-vapors2]
G18-ABI-FD-water-vapors2
G18-ABI-FD-water-vapors2
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G18-ABI-MESO1-cloud-phase
[G18-ABI-MESO1-cloud-phase]
G18-ABI-MESO1-cloud-phase
G18-ABI-MESO1-cloud-phase
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G18-ABI-MESO1-convection
[G18-ABI-MESO1-convection]
G18-ABI-MESO1-convection
G18-ABI-MESO1-convection
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G18-ABI-MESO1-day-microphysics-abi
[G18-ABI-MESO1-day-microphysics-abi]
G18-ABI-MESO1-day-microphysics-abi
G18-ABI-MESO1-day-microphysics-abi
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G18-ABI-MESO1-fire-temperature-awips
[G18-ABI-MESO1-fire-temperature-awips]
G18-ABI-MESO1-fire-temperature-awips
G18-ABI-MESO1-fire-temperature-awips
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G18-ABI-MESO1-ir-sandwich
[G18-ABI-MESO1-ir-sandwich]
G18-ABI-MESO1-ir-sandwich
G18-ABI-MESO1-ir-sandwich
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G18-ABI-MESO1-night-microphysics
[G18-ABI-MESO1-night-microphysics]
G18-ABI-MESO1-night-microphysics
G18-ABI-MESO1-night-microphysics
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G18-ABI-MESO1-true-color
[G18-ABI-MESO1-true-color]
View of G18-ABI-MESO1-geo-color
View of G18-ABI-MESO1-geo-color
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G18-ABI-MESO1-water-vapors2
[G18-ABI-MESO1-water-vapors2]
G18-ABI-MESO1-water-vapors2
G18-ABI-MESO1-water-vapors2
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G18-ABI-MESO2-cloud-phase
[G18-ABI-MESO2-cloud-phase]
G18-ABI-MESO2-cloud-phase
G18-ABI-MESO2-cloud-phase
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G18-ABI-MESO2-convection
[G18-ABI-MESO2-convection]
G18-ABI-MESO2-convection
G18-ABI-MESO2-convection
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G18-ABI-MESO2-day-microphysics-abi
[G18-ABI-MESO2-day-microphysics-abi]
G18-ABI-MESO2-day-microphysics-abi
G18-ABI-MESO2-day-microphysics-abi
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G18-ABI-MESO2-fire-temperature-awips
[G18-ABI-MESO2-fire-temperature-awips]
G18-ABI-MESO2-fire-temperature-awips
G18-ABI-MESO2-fire-temperature-awips
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G18-ABI-MESO2-ir-sandwich
[G18-ABI-MESO2-ir-sandwich]
G18-ABI-MESO2-ir-sandwich
G18-ABI-MESO2-ir-sandwich
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G18-ABI-MESO2-night-microphysics
[G18-ABI-MESO2-night-microphysics]
G18-ABI-MESO2-night-microphysics
G18-ABI-MESO2-night-microphysics
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G18-ABI-MESO2-true-color
[G18-ABI-MESO2-true-color]
View of G18-ABI-MESO2-geo-color
View of G18-ABI-MESO2-geo-color
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G18-ABI-MESO2-water-vapors2
[G18-ABI-MESO2-water-vapors2]
G18-ABI-MESO2-water-vapors2
G18-ABI-MESO2-water-vapors2
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Gridded NUCAPS Geopotential Height 300mb
[nucaps-grid-ght-300]
Geopotential height approximates the actual height of a pressure surfaceabove mean sea-level. A geopotential height observation represents the height of the pressure surface on which the observation was taken....
Geopotential height approximates the actual height of a pressure surface above mean sea-level. A geopotential height observation represents the height of the pressure surface on which the observation was taken. Geopotential height is valuable for locating troughs and ridges which are the upper level counterparts of surface cyclones and anticyclones.
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Gridded NUCAPS Geopotential Height 500mb
[nucaps-grid-ght-500]
Geopotential height approximates the actual height of a pressure surfaceabove mean sea-level. A geopotential height observation represents the height of the pressure surface on which the observation was taken....
Geopotential height approximates the actual height of a pressure surface above mean sea-level. A geopotential height observation represents the height of the pressure surface on which the observation was taken. Geopotential height is valuable for locating troughs and ridges which are the upper level counterparts of surface cyclones and anticyclones.
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Gridded NUCAPS Geopotential Height 700mb
[nucaps-grid-ght-700]
Geopotential height approximates the actual height of a pressure surfaceabove mean sea-level. A geopotential height observation represents the height of the pressure surface on which the observation was taken....
Geopotential height approximates the actual height of a pressure surface above mean sea-level. A geopotential height observation represents the height of the pressure surface on which the observation was taken. Geopotential height is valuable for locating troughs and ridges which are the upper level counterparts of surface cyclones and anticyclones.
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Gridded NUCAPS Geopotential Height 850mb
[nucaps-grid-ght-850]
Geopotential height approximates the actual height of a pressure surfaceabove mean sea-level. A geopotential height observation represents the height of the pressure surface on which the observation was taken....
Geopotential height approximates the actual height of a pressure surface above mean sea-level. A geopotential height observation represents the height of the pressure surface on which the observation was taken. Geopotential height is valuable for locating troughs and ridges which are the upper level counterparts of surface cyclones and anticyclones.
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Hydro Estimator Rainfall
[NESDIS-GHE-HourlyRainfall]
The HE algorithm uses infrared (IR) brightness temperatures to identifyregions of rainfall and retrieve rainfall rate, while using National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS)...
The HE algorithm uses infrared (IR) brightness temperatures to identify regions of rainfall and retrieve rainfall rate, while using National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model fields to account for the effects of moisture availability, evaporation, orographic modulation, and thermodynamic profile effects. Estimates of rainfall from satellites can provide critical rainfall information in regions where data from gauges or radar are unavailable or unreliable, such as over oceans or sparsely populated regions.
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IR Winds 250-100mb
[AMV-ULhigh]
AMV: Upper Level IR/WV (100-250mb)
AMV: Upper Level IR/WV (100-250mb)
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IR Winds 350-251mb
[AMV-ULmid]
AMV: Upper Level IR/WV (251-350mb)
AMV: Upper Level IR/WV (251-350mb)
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IR Winds 500-351mb
[AMV-ULlow]
AMV: Upper Level IR/WV (351-500mb)
AMV: Upper Level IR/WV (351-500mb)
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Vis Winds 800-700mb
[AMV-VISmid]
AMV: Middle Level Visible (700-800mb)
AMV: Middle Level Visible (700-800mb)
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Vis Winds 925-801mb
[AMV-VISlow]
AMV: Lower Level Visible (801-925mb)
AMV: Lower Level Visible (801-925mb)
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Hydro Estimator Rainfall
[NESDIS-GHE-HourlyRainfall]
The HE algorithm uses infrared (IR) brightness temperatures to identifyregions of rainfall and retrieve rainfall rate, while using National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS)...
The HE algorithm uses infrared (IR) brightness temperatures to identify regions of rainfall and retrieve rainfall rate, while using National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model fields to account for the effects of moisture availability, evaporation, orographic modulation, and thermodynamic profile effects. Estimates of rainfall from satellites can provide critical rainfall information in regions where data from gauges or radar are unavailable or unreliable, such as over oceans or sparsely populated regions.
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IR Winds 250-100mb
[AMV-ULhigh]
AMV: Upper Level IR/WV (100-250mb)
AMV: Upper Level IR/WV (100-250mb)
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IR Winds 350-251mb
[AMV-ULmid]
AMV: Upper Level IR/WV (251-350mb)
AMV: Upper Level IR/WV (251-350mb)
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IR Winds 500-351mb
[AMV-ULlow]
AMV: Upper Level IR/WV (351-500mb)
AMV: Upper Level IR/WV (351-500mb)
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MIMIC Total Precip Water v2
[MIMICTPW2]
MIMIC-TPW2 is an experimental global product of total precipitable water(TPW), using morphological compositing of the MIRS retrieval from several available operational microwave-frequency sensors. MIMIC stands for...
MIMIC-TPW2 is an experimental global product of total precipitable water (TPW), using morphological compositing of the MIRS retrieval from several available operational microwave-frequency sensors. MIMIC stands for "Morphed Integrated Microwave Imagery at CIMSS." The specific technique used here was initially described in a 2010 paper by Wimmers and Velden. This Version 2 is developed from an older method (still running in real-time) that uses simpler, but more limited TPW retrievals and advection calculations.
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Sea Surface Temperature
[NESDIS-SST]
NESDIS: Hi-Res Sea Surface Temperature
NESDIS: Hi-Res Sea Surface Temperature
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Vis Winds 800-700mb
[AMV-VISmid]
AMV: Middle Level Visible (700-800mb)
AMV: Middle Level Visible (700-800mb)
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Vis Winds 925-801mb
[AMV-VISlow]
AMV: Lower Level Visible (801-925mb)
AMV: Lower Level Visible (801-925mb)
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Global Infrared
[globalir]
This product is a global composite of imagery from multiple satellites. Itis completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most...
This product is a global composite of imagery from multiple satellites. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.
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Global Infrared - Aviation
[globalir-avn]
This product is an enhanced view of the global infrared composite product.It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most...
This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.
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Global Infrared - Dvorak
[globalir-bd]
This product is an enhanced view of the global infrared composite product.It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most...
This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.
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Global Infrared - Funk Top
[globalir-funk]
This product is an enhanced view of the global infrared composite product.It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most...
This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.
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Global Infrared - Rainbow
[globalir-nhc]
This product is an enhanced view of the global infrared composite product.It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most...
This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.
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Global Infrared - Rain Rate
[globalir-rr]
This product is based on a statistical relationship between cloud toptemperature and observed rain rate. It is derived every hour (at about 35-minutes after the hour UTC) using the global IR composite produced by...
This product is based on a statistical relationship between cloud top temperature and observed rain rate. It is derived every hour (at about 35-minutes after the hour UTC) using the global IR composite produced by the SSEC Data Center. While it shows the most current imagery, shifting occurs along composite seams.
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Global Infrared - Tops
[globalir-ott]
This product is an enhanced view of the global infrared composite product.It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most...
This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.
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Global Visible
[globalvis]
This product is a global composite of imagery from multiple satellites. Itis completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most...
This product is a global composite of imagery from multiple satellites. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.
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Global Visible (transparent Night)
[globalvis-tsp]
This view is based on the global Visible composite product in which nighttime regions are rendered transparent. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best...
This view is based on the global Visible composite product in which night time regions are rendered transparent. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.
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Global Visible - fill
[global1kmvis]
This product is a 15-minute snapshot of a global composite of imagery frommultiple satellites. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery....
This product is a 15-minute snapshot of a global composite of imagery from multiple satellites. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.
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Global Water Vapor
[globalwv]
This product is a global composite of imagery from multiple satellites. Itis completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most...
This product is a global composite of imagery from multiple satellites. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.
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Global Water Vapor - Gradient
[globalwv-grad]
This product is an enhanced view of the global Water Vapor compositeproduct. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it...
This product is an enhanced view of the global Water Vapor composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.
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Cladophora Classification
[clad]
Estimate of 2005 algae extent along coastal Lake Michigan.
Estimate of 2005 algae extent along coastal Lake Michigan.
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Great Lakes Surface Environmental Analysis
[GLERL-GLSEAimage]
Great Lakes Surface Environmental Analysis (GLSEA) from GLERL. For moreinfo see: http://coastwatch.glerl.noaa.gov/glsea/doc
Great Lakes Surface Environmental Analysis (GLSEA) from GLERL. For more info see:
http://coastwatch.glerl.noaa.gov/glsea/doc
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WI Coastal Imagery
[WICoast]
WI Coastal Imagery displays aerial photographs of the Lake Michigan coastof Wisconsin from 2007. The images are being used to monitor cladophora algae growth.
WI Coastal Imagery displays aerial photographs of the Lake Michigan coast of Wisconsin from 2007. The images are being used to monitor cladophora algae growth.
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WI Coastal Shaded Relief
[WIcoastalshdrlf]
WI coastal shaded relief map generated from LiDAR data.
WI coastal shaded relief map generated from LiDAR data.
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Gridded NUCAPS Haines Index - High
[nucaps-grid-haines-index-high]
The Haines Index is frequently used to determine the potential for largefire growth (Werth and Ochoa 1993) where the lapse rate and dew point depression represent the stability and moisture of the environment. The...
The Haines Index is frequently used to determine the potential for large fire growth (Werth and Ochoa 1993) where the lapse rate and dew point depression represent the stability and moisture of the environment. The pressure levels for the Haines Index calculation are adjusted based on elevation and chosen to negate the impact of diurnal surface temperature changes and surface inversions. The region where the "high" index is most appropriate includes the western states, roughly west of the 100th meridian.
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Gridded NUCAPS Haines Index - Low
[nucaps-grid-haines-index-low]
The Haines Index is frequently used to determine the potential for largefire growth (Werth and Ochoa 1993) where the lapse rate and dew point depression represent the stability and moisture of the environment. The...
The Haines Index is frequently used to determine the potential for large fire growth (Werth and Ochoa 1993) where the lapse rate and dew point depression represent the stability and moisture of the environment. The pressure levels for the Haines Index calculation are adjusted based on elevation and chosen to negate the impact of diurnal surface temperature changes and surface inversions. The region where the "low" index is most appropriate includes the eastern states, from just west of the Mississippi River to the East Coast, excluding the Appalachian Mountains, which are "mid."
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Gridded NUCAPS Haines Index - Mid
[nucaps-grid-haines-index-mid]
The Haines Index is frequently used to determine the potential for largefire growth (Werth and Ochoa 1993) where the lapse rate and dew point depression represent the stability and moisture of the environment. The...
The Haines Index is frequently used to determine the potential for large fire growth (Werth and Ochoa 1993) where the lapse rate and dew point depression represent the stability and moisture of the environment. The pressure levels for the Haines Index calculation are adjusted based on elevation and chosen to negate the impact of diurnal surface temperature changes and surface inversions. The region where the "mid" index is most appropriate includes the plains states, from North Dakota south to western Texas and a separate area corresponding to the Appalachian Mountains.
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River Flood: 1 day VIIRS composite
[RIVER-FLDglobal-composite1]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 1 day.
For more information visit: Here
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River Flood: 5 day VIIRS composite
[RIVER-FLDglobal-composite]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 5 days.
For more information visit: Here
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River Flood: Alaska
[RIVER-FLDall-AP]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Alaska region
Quick guide
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River Flood: Alaska (transparent)
[RIVER-FLDtsp-AP]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Alaska region(Transparent flood-free land)
Quick guide
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River Flood: Global
[RIVER-FLDglobal]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Global(CSPP product)
Quick guide
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River Flood: Joint ABI/VIIRS
[RIVER-FLD-joint-ABI]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available ABI-full disk imagery and VIIRS imagery since sunrise on the given day.
For more information visit: Here
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River Flood: Missouri Basin
[RIVER-FLDall-MB]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Missouri Basin
Quick guide
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River Flood: Missouri Basin (transparent)
[RIVER-FLDtsp-MB]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Missouri Basin(Transparent flood-free land)
Quick guide
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River Flood: North Central Basin
[RIVER-FLDall-NC]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North Central Basin
Quick guide
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River Flood: North Central Basin (transparent)
[RIVER-FLDtsp-NC]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North Central Basin(Transparent flood-free land)
Quick guide
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River Flood: North East Basin
[RIVER-FLDall-NE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North East Basin
Quick guide
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River Flood: North East Basin (transparent)
[RIVER-FLDtsp-NE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North East Basin(Transparent flood-free land)
Quick guide
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River Flood: North West
[RIVER-FLDall-NW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Northwest Region
Quick guide
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River Flood: North West (transparent)
[RIVER-FLDtsp-NW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Northwest Region(Transparent flood-free land)
Quick guide
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River Flood: South East
[RIVER-FLDall-SE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southeast Region
Quick guide
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River Flood: South East (transparent)
[RIVER-FLDtsp-SE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southeast Region(Transparent flood-free land)
Quick guide
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River Flood: South West
[RIVER-FLDall-SW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southwest Region
Quick guide
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River Flood: South West (tsp)
[RIVER-FLDtsp-SW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southwest Region(Transparent flood-free land)
Quick guide
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River Flood: US
[RIVER-FLDall-US]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
US
Quick guide
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River Flood: US (transparent)
[RIVER-FLDtsp-US]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
US(Transparent flood-free land)
Quick guide
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River Flood: West Gulf Basin
[RIVER-FLDall-WG]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
West Gulf Basin
Quick guide
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River Flood: West Gulf Basin (transparent)
[RIVER-FLDtsp-WG]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
West Gulf Basin(Transparent flood-free land)
Quick guide
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Earthquake Magnitude
[Earthquake-mag]
Earthquake Magnitude (Past 24hr)
Earthquake Magnitude (Past 24hr)
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Fire Hazards (Valid)
[XREDFLAG]
The National Weather Service issues a variety of Weather warnings, watchesand advisories. The event type is indicated on the map by different colors. This product contains Wildland Fire Weather Hazards VALID for a 48hr Window...
The National Weather Service issues a variety of Weather warnings, watches and advisories. The event type is indicated on the map by different colors. This product contains Wildland Fire Weather Hazards VALID for a 48hr Window spanning from the previous 24hrs to 24hrs in the future at 1hr increments
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Flood Warnings Hydrological-VTEC (Issued)
[HVTEC]
For Flood Warnings (FLW) and follow up Flood Statements (FLS) at specificriver forecast points, the H-VTEC specifies the flood severity; immediate cause, timing of flood beginning, crest, and end; and how the flood...
For Flood Warnings (FLW) and follow up Flood Statements (FLS) at specific river forecast points, the H-VTEC specifies the flood severity; immediate cause,
timing of flood beginning, crest, and end; and how the flood compares to the flood of record.
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Severe Weather Warnings
[Severe]
Tornado, Thunderstorm, Flash Flood and Marine Warning polygons.
Tornado, Thunderstorm, Flash Flood and Marine Warning polygons.
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Severe Weather Watch Box
[SAW]
Severe Weather Watch Box - Aviation
Severe Weather Watch Box - Aviation
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Tropical Storm & Hurricane Forecast
[TSFCST]
National Hurricane Center Tropical Storm & Hurricane Forecast
National Hurricane Center Tropical Storm & Hurricane Forecast
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Volcanic Ash Adv plumes
[VAA]
Volcanic Ash Advisories: Ash Clouds
Volcanic Ash Advisories: Ash Clouds
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Wind Hazards
[WWIND]
Wind Hazards is a collection of alerts associated with all types of Windrelated events. These Hazards are issued by the NWS WSFOs as Advisories, Watches and Warnings. WindEvents include Wind, LakeWind and HighWind...
Wind Hazards is a collection of alerts associated with all types of Wind related events. These Hazards are issued by the NWS WSFOs as Advisories, Watches and Warnings. WindEvents include Wind, LakeWind and HighWind categories. Click on objects to get a detailed description of the specific hazard.
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Winter Weather Hazards (Issued)
[WWINTER]
Winter Weather is a collection of Hazards associated with all types ofWinter precip and conditions. Hazards are issued by the NWS WSFOs as Advisories, Watches and Warnings. SnowEvents include SnowStorm,...
Winter Weather is a collection of Hazards associated with all types of Winter precip and conditions. Hazards are issued by the NWS WSFOs as Advisories, Watches and Warnings. SnowEvents include SnowStorm, WinterStorm, Snow, HeavySnow, LakeEffectSnow and BlowingSnow. IceEvents include Sleet, HeavySleet, FreezingRain, IceStorm and FreezingFog. Click on objects to get a detailed description of the specific hazard.
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Winter Weather Hazards (Valid)
[XWINTER]
The National Weather Service issues a variety of Winter Weather warnings,watches and advisories. The event type is indicated on the map by different colors. This product contains Winter Weather Hazards VALID for a 48hr...
The National Weather Service issues a variety of Winter Weather warnings, watches and advisories. The event type is indicated on the map by different colors. This product contains Winter Weather Hazards VALID for a 48hr Window spanning from the previous 24hrs to 24hrs in the future at 1hr increments.
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Infrared 6 inch Imagery of Madison
[madisonir]
Infrared 6 inch Imagery of Madison
Infrared 6 inch Imagery of Madison
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NAIP WI
[NAIPWI]
National Agricultural Imagery Program aerial photography from the WisconsinFarm Service Agency (WI-FSA) of the USDA.
National Agricultural Imagery Program aerial photography from the Wisconsin Farm Service Agency (WI-FSA) of the USDA.
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NAIP WI Color Infrared
[NAIPWICIR]
National Agricultural Imagery Program aerial photography from the WisconsinFarm Service Agency (WI-FSA) of the USDA (Color Infrared)
National Agricultural Imagery Program aerial photography from the Wisconsin Farm Service Agency (WI-FSA) of the USDA (Color Infrared)
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WI Coastal Shaded Relief
[WIcoastalshdrlf]
WI coastal shaded relief map generated from LiDAR data.
WI coastal shaded relief map generated from LiDAR data.
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Wisconsin LIDAR Hillshade
[wi-hillshade]
WisconsinView is a remote sensing consortium and member of AmericaView.org.These Wisconsin lidar data sets were collected by aircraft and processed by state and county agencies. These data are hosted by WisconsinView and...
WisconsinView is a remote sensing consortium and member of AmericaView.org. These Wisconsin lidar data sets were collected by aircraft and processed by state and county agencies. These data are hosted by WisconsinView and visualized here with coordination and funding from the WI State Dept. of Administration, Geographic Information Office and NOAA"s coastal management program.
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WI USGS Landsat Poster
[wilandsat]
This is a georeferenced poster from the USGS. The original source is:http://eros.usgs.gov/imagegallery/landsat-state-mosaics unfortunately the original poster imagery without graphics burned-in is not available.
This is a georeferenced poster from the USGS. The original source is: http://eros.usgs.gov/imagegallery/landsat-state-mosaics unfortunately the original poster imagery without graphics burned-in is not available.
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Himawari-9 - RGB
[H09-RGB341]
Red: 0.64 µm: Red
Green: 0.86 µm: Veggie
Blue 0.47 µm: Blue
Red: 0.64 µm: Red
Green: 0.86 µm: Veggie
Blue 0.47 µm: Blue
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HIMAWARI-day-microphysics-ahi
[HIMAWARI-day-microphysics-ahi]
HIMAWARI-day-microphysics-ahi
HIMAWARI-day-microphysics-ahi
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HIMAWARI-fire-temperature-awips
[HIMAWARI-fire-temperature-awips]
HIMAWARI-fire-temperature-awips
HIMAWARI-fire-temperature-awips
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HIMAWARI-night-microphysics
[HIMAWARI-night-microphysics]
HIMAWARI-night-microphysics
HIMAWARI-night-microphysics
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Himawari AHI Full Disk B01 "Blue" Visible
[HIMAWARI-B01]
Himawari AHI Full Disk B01 "Blue" Visible
Himawari AHI Full Disk B01 "Blue" Visible
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Himawari AHI Full Disk B02 "Green" Visible
[HIMAWARI-B02]
Himawari AHI Full Disk B02 "Green" Visible
Himawari AHI Full Disk B02 "Green" Visible
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Himawari AHI Full Disk B03 Hi-Res "Red" Visible
[HIMAWARI-B03]
Himawari AHI Full Disk B03 Hi-Res "Red" Visible
Himawari AHI Full Disk B03 Hi-Res "Red" Visible
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Himawari AHI Full Disk B04 "Veggie"
[HIMAWARI-B04]
Himawari AHI Full Disk B04 "Veggie"
Himawari AHI Full Disk B04 "Veggie"
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Himawari AHI Full Disk B05 Snow and Ice
[HIMAWARI-B05]
Himawari AHI Full Disk B05 Snow and Ice
Himawari AHI Full Disk B05 Snow and Ice
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Himawari AHI Full Disk B06 Cloud Particle Size
[HIMAWARI-B06]
Himawari AHI Full Disk B06 Cloud Particle Size
Himawari AHI Full Disk B06 Cloud Particle Size
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Himawari AHI Full Disk B07 "Fire"
[HIMAWARI-B07]
Himawari AHI Full Disk B07 "Fire"
Himawari AHI Full Disk B07 "Fire"
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Himawari AHI Full Disk B07 "Fire" enhanced
[HIMAWARI-B07-FIRE]
View of HIMAWARI-B07
View of HIMAWARI-B07
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Himawari AHI Full Disk B08 Upper-level Water Vapor
[HIMAWARI-B08]
Himawari AHI Full Disk B08 Upper-level Water Vapor
Himawari AHI Full Disk B08 Upper-level Water Vapor
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Himawari AHI Full Disk B08 Upper-level Water Vapor enhanced
[HIMAWARI-B08-VAPR]
View of HIMAWARI-B08
View of HIMAWARI-B08
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Himawari AHI Full Disk B09 Mid-level Water Vapor
[HIMAWARI-B09]
Himawari AHI Full Disk B09 Mid-level Water Vapor
Himawari AHI Full Disk B09 Mid-level Water Vapor
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Himawari AHI Full Disk B09 Mid-level Water Vapor enhanced
[HIMAWARI-B09-VAPR]
View of HIMAWARI-09
View of HIMAWARI-09
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Himawari AHI Full Disk B10 Low-level Water Vapor
[HIMAWARI-B10]
Himawari AHI Full Disk B10 Low-level Water Vapor
Himawari AHI Full Disk B10 Low-level Water Vapor
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Himawari AHI Full Disk B10 Low-level Water Vapor enhanced
[HIMAWARI-B10-VAPR]
View of HIMAWARI-B10
View of HIMAWARI-B10
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Himawari AHI Full Disk B11 Cloud Phase
[HIMAWARI-B11]
Himawari AHI Full Disk B11 Cloud Phase
Himawari AHI Full Disk B11 Cloud Phase
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Himawari AHI Full Disk B12 Ozone
[HIMAWARI-B12]
Himawari AHI Full Disk B12 Ozone
Himawari AHI Full Disk B12 Ozone
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Himawari AHI Full Disk B13 "Clean" Infrared
[HIMAWARI-B13]
Himawari AHI Full Disk B13 "Clean" Infrared
Himawari AHI Full Disk B13 "Clean" Infrared
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Himawari AHI Full Disk B13 "Clean" Infrared enhanced
[HIMAWARI-B13-GRAD]
View of HIMAWARI-B13
View of HIMAWARI-B13
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Himawari AHI Full Disk B14 Infrared
[HIMAWARI-B14]
Himawari AHI Full Disk B14 Infrared
Himawari AHI Full Disk B14 Infrared
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Himawari AHI Full Disk B14 Infrared enhanced
[HIMAWARI-B14-GRAD]
View of HIMAWARI-B14
View of HIMAWARI-B14
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Himawari AHI Full Disk B15 "Dirty" Infrared
[HIMAWARI-B15]
Himawari AHI Full Disk B15 "Dirty" Infrared
Himawari AHI Full Disk B15 "Dirty" Infrared
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Himawari AHI Full Disk B15 "Dirty" Infrared enhanced
[HIMAWARI-B15-GRAD]
View of HIMAWARI-B15
View of HIMAWARI-B15
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Himawari AHI Full Disk B16 Carbon Dioxide
[HIMAWARI-B16]
Himawari AHI Full Disk B16 Carbon Dioxide
Himawari AHI Full Disk B16 Carbon Dioxide
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HIMAWARI-Japan-convection
[HIMAWARI-Japan-convection]
HIMAWARI-Japan-convection
HIMAWARI-Japan-convection
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HIMAWARI-Japan-day-microphysics-ahi
[HIMAWARI-Japan-day-microphysics-ahi]
HIMAWARI-Japan-day-microphysics-ahi
HIMAWARI-Japan-day-microphysics-ahi
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HIMAWARI-Japan-fire-temperature-awips
[HIMAWARI-Japan-fire-temperature-awips]
HIMAWARI-Japan-fire-temperature-awips
HIMAWARI-Japan-fire-temperature-awips
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HIMAWARI-Japan-night-microphysics
[HIMAWARI-Japan-night-microphysics]
HIMAWARI-Japan-night-microphysics
HIMAWARI-Japan-night-microphysics
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HIMAWARI-Japan-true-color
[HIMAWARI-Japan-true-color]
HIMAWARI-Japan-true-color
HIMAWARI-Japan-true-color
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HIMAWARI-Japan-water-vapors2
[HIMAWARI-Japan-water-vapors2]
HIMAWARI-Japan-water-vapors2
HIMAWARI-Japan-water-vapors2
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HIMAWARI-day-microphysics-ahi
[HIMAWARI-day-microphysics-ahi]
HIMAWARI-day-microphysics-ahi
HIMAWARI-day-microphysics-ahi
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HIMAWARI-fire-temperature-awips
[HIMAWARI-fire-temperature-awips]
HIMAWARI-fire-temperature-awips
HIMAWARI-fire-temperature-awips
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HIMAWARI-Japan-convection
[HIMAWARI-Japan-convection]
HIMAWARI-Japan-convection
HIMAWARI-Japan-convection
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HIMAWARI-Japan-day-microphysics-ahi
[HIMAWARI-Japan-day-microphysics-ahi]
HIMAWARI-Japan-day-microphysics-ahi
HIMAWARI-Japan-day-microphysics-ahi
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HIMAWARI-Japan-fire-temperature-awips
[HIMAWARI-Japan-fire-temperature-awips]
HIMAWARI-Japan-fire-temperature-awips
HIMAWARI-Japan-fire-temperature-awips
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HIMAWARI-Japan-night-microphysics
[HIMAWARI-Japan-night-microphysics]
HIMAWARI-Japan-night-microphysics
HIMAWARI-Japan-night-microphysics
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HIMAWARI-Japan-true-color
[HIMAWARI-Japan-true-color]
HIMAWARI-Japan-true-color
HIMAWARI-Japan-true-color
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HIMAWARI-Japan-water-vapors2
[HIMAWARI-Japan-water-vapors2]
HIMAWARI-Japan-water-vapors2
HIMAWARI-Japan-water-vapors2
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HIMAWARI-night-microphysics
[HIMAWARI-night-microphysics]
HIMAWARI-night-microphysics
HIMAWARI-night-microphysics
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HIMAWARI-Target-cloudtop
[HIMAWARI-Target-cloudtop]
HIMAWARI-Target-cloudtop
HIMAWARI-Target-cloudtop
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HIMAWARI-Target-convection
[HIMAWARI-Target-convection]
HIMAWARI-Target-convection
HIMAWARI-Target-convection
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HIMAWARI-Target-day-microphysics-ahi
[HIMAWARI-Target-day-microphysics-ahi]
HIMAWARI-Target-day-microphysics-ahi
HIMAWARI-Target-day-microphysics-ahi
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HIMAWARI-Target-fire-temperature-awips
[HIMAWARI-Target-fire-temperature-awips]
HIMAWARI-Target-fire-temperature-awips
HIMAWARI-Target-fire-temperature-awips
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HIMAWARI-Target-night-microphysics
[HIMAWARI-Target-night-microphysics]
HIMAWARI-Target-night-microphysics
HIMAWARI-Target-night-microphysics
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HIMAWARI-Target-true-color
[HIMAWARI-Target-true-color]
HIMAWARI-Target-true-color
HIMAWARI-Target-true-color
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HIMAWARI-Target-water-vapors2
[HIMAWARI-Target-water-vapors2]
HIMAWARI-Target-water-vapors2
HIMAWARI-Target-water-vapors2
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HIMAWARI-Target-cloudtop
[HIMAWARI-Target-cloudtop]
HIMAWARI-Target-cloudtop
HIMAWARI-Target-cloudtop
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HIMAWARI-Target-convection
[HIMAWARI-Target-convection]
HIMAWARI-Target-convection
HIMAWARI-Target-convection
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HIMAWARI-Target-day-microphysics-ahi
[HIMAWARI-Target-day-microphysics-ahi]
HIMAWARI-Target-day-microphysics-ahi
HIMAWARI-Target-day-microphysics-ahi
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HIMAWARI-Target-fire-temperature-awips
[HIMAWARI-Target-fire-temperature-awips]
HIMAWARI-Target-fire-temperature-awips
HIMAWARI-Target-fire-temperature-awips
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HIMAWARI-Target-night-microphysics
[HIMAWARI-Target-night-microphysics]
HIMAWARI-Target-night-microphysics
HIMAWARI-Target-night-microphysics
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HIMAWARI-Target-true-color
[HIMAWARI-Target-true-color]
HIMAWARI-Target-true-color
HIMAWARI-Target-true-color
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HIMAWARI-Target-water-vapors2
[HIMAWARI-Target-water-vapors2]
HIMAWARI-Target-water-vapors2
HIMAWARI-Target-water-vapors2
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Gridded NUCAPS Relative Humidity 2m
[nucaps-grid-rh-2m]
Relative humidity at 2-meters above ground. Gridded NUCAPS from NASA-SPoRT(Short-term Prediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and...
Relative humidity at 2-meters above ground. Gridded NUCAPS from NASA-SPoRT (Short-term Prediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS to provide plan-view and cross-section displays of CrIS/ATMS temperature and moisture soundings in AWIPS (i.e. Gridded NUCAPS). Gridded NUCAPS was originally developed to diagnose Cold Air Aloft (CAA) but can also be used to diagnose the pre-convective environment. Gridded NUCAPS has been evaluated at the Anchorage CWSU for the CAA forecasting challenge and at the Hazardous Weather Testbed for assessing the pre-convective environment. As part of the JPSS NUCAPS Initiative, the team of collaborators is exploring new applications for Gridded NUCAPS and working with developers to baseline the product in AWIPS, McIDAS-V, and RealEarth.
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Gridded NUCAPS Relative Humidity 300mb
[nucaps-grid-rh-300]
Relative humidity at 300mb. Gridded NUCAPS from NASA-SPoRT (Short-termPrediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS...
Relative humidity at 300mb. Gridded NUCAPS from NASA-SPoRT (Short-term Prediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS to provide plan-view and cross-section displays of CrIS/ATMS temperature and moisture soundings in AWIPS (i.e. Gridded NUCAPS). Gridded NUCAPS was originally developed to diagnose Cold Air Aloft (CAA) but can also be used to diagnose the pre-convective environment. Gridded NUCAPS has been evaluated at the Anchorage CWSU for the CAA forecasting challenge and at the Hazardous Weather Testbed for assessing the pre-convective environment. As part of the JPSS NUCAPS Initiative, the team of collaborators is exploring new applications for Gridded NUCAPS and working with developers to baseline the product in AWIPS, McIDAS-V, and RealEarth.
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Gridded NUCAPS Relative Humidity 500mb
[nucaps-grid-rh-500]
Relative humidity at 500mb. Gridded NUCAPS from NASA-SPoRT (Short-termPrediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS...
Relative humidity at 500mb. Gridded NUCAPS from NASA-SPoRT (Short-term Prediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS to provide plan-view and cross-section displays of CrIS/ATMS temperature and moisture soundings in AWIPS (i.e. Gridded NUCAPS). Gridded NUCAPS was originally developed to diagnose Cold Air Aloft (CAA) but can also be used to diagnose the pre-convective environment. Gridded NUCAPS has been evaluated at the Anchorage CWSU for the CAA forecasting challenge and at the Hazardous Weather Testbed for assessing the pre-convective environment. As part of the JPSS NUCAPS Initiative, the team of collaborators is exploring new applications for Gridded NUCAPS and working with developers to baseline the product in AWIPS, McIDAS-V, and RealEarth.
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Gridded NUCAPS Relative Humidity 700mb
[nucaps-grid-rh-700]
Relative humidity at 700mb. Gridded NUCAPS from NASA-SPoRT (Short-termPrediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS...
Relative humidity at 700mb. Gridded NUCAPS from NASA-SPoRT (Short-term Prediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS to provide plan-view and cross-section displays of CrIS/ATMS temperature and moisture soundings in AWIPS (i.e. Gridded NUCAPS). Gridded NUCAPS was originally developed to diagnose Cold Air Aloft (CAA) but can also be used to diagnose the pre-convective environment. Gridded NUCAPS has been evaluated at the Anchorage CWSU for the CAA forecasting challenge and at the Hazardous Weather Testbed for assessing the pre-convective environment. As part of the JPSS NUCAPS Initiative, the team of collaborators is exploring new applications for Gridded NUCAPS and working with developers to baseline the product in AWIPS, McIDAS-V, and RealEarth.
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Gridded NUCAPS Relative Humidity 850mb
[nucaps-grid-rh-850]
Relative humidity at 850mb. Gridded NUCAPS from NASA-SPoRT (Short-termPrediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS...
Relative humidity at 850mb. Gridded NUCAPS from NASA-SPoRT (Short-term Prediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS to provide plan-view and cross-section displays of CrIS/ATMS temperature and moisture soundings in AWIPS (i.e. Gridded NUCAPS). Gridded NUCAPS was originally developed to diagnose Cold Air Aloft (CAA) but can also be used to diagnose the pre-convective environment. Gridded NUCAPS has been evaluated at the Anchorage CWSU for the CAA forecasting challenge and at the Hazardous Weather Testbed for assessing the pre-convective environment. As part of the JPSS NUCAPS Initiative, the team of collaborators is exploring new applications for Gridded NUCAPS and working with developers to baseline the product in AWIPS, McIDAS-V, and RealEarth.
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Tropical Storm & Hurricane Forecast
[TSFCST]
National Hurricane Center Tropical Storm & Hurricane Forecast
National Hurricane Center Tropical Storm & Hurricane Forecast
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Florence flooding GOES16-ABI-FLOOD-No-Clouds
[GOES16-ABI-FLOOD-water-only]
View of GOES16-ABI-FLOOD
View of GOES16-ABI-FLOOD
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Florence flooding GOES16-ABI-FLOOD-No-Land
[GOES16-ABI-FLOOD-No-Land]
View of GOES16-ABI-FLOOD
View of GOES16-ABI-FLOOD
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Florence flooding Merged-ABI-VIIRS-Flood-Map
[Merged-ABI-VIIRS-Flood-Map]
Merged-ABI-VIIRS-Flood-Map
Merged-ABI-VIIRS-Flood-Map
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Florence flooding Merged-ABI-VIIRS-Flood-Map-No-Land
[Merged-ABI-VIIRS-Flood-Map-No-Land]
View of Merged-ABI-VIIRS-Flood-Map
View of Merged-ABI-VIIRS-Flood-Map
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Hurricane Harvey Post-Event Imagery
[post-harvey-digital-globe]
This true-color WorldView-4 satellite imagery product is provided throughDigitalGlobe"s Open Data Program under a Creative Commons Attribution Non-Commercial 4.0 license.
This true-color WorldView-4 satellite imagery product is provided through DigitalGlobe"s Open Data Program under a Creative Commons Attribution Non-Commercial 4.0 license.
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Fire Radiative Power VIIRS I-band - GINA
[AFIMG-Points-GINA]
VIIRS 375m I-band high spatial resolution imagery provides a greaterresponse over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime...
VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software at GINA.
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VIIRS Fire Radiative Power I-band DB ConUS
[AFIMG-Points]
VIIRS 375m I-band high spatial resolution imagery provides a greaterresponse over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime...
VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software.
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VIIRS Fire RGB (GINA) DB Alaska
[DayLandCloudFire-RGB-GINA]
This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87umchannel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic...
This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic Information Network of Alaska (GINA).
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VIIRS Fire Temp RGB (GINA) DB Alaska
[FireTemperature-RGB-GINA]
This RGB is created by assigning the VIIRS 3.74um channel to red, 2.25umchannel to green, and the 1.61um channel to blue. It is used to assess fire intensity and size, with fires ranging from red (lowest) to yellow to white...
This RGB is created by assigning the VIIRS 3.74um channel to red, 2.25um channel to green, and the 1.61um channel to blue. It is used to assess fire intensity and size, with fires ranging from red (lowest) to yellow to white (hottest or biggest). These data are produced by the Geographic Information Network of Alaska (GINA).
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River Flood: Global
[RIVER-FLDglobal]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Global(CSPP product)
Quick guide
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VIIRS Floodwater Depth
[VIIRS-3Dflood]
VIIRS downscaling software is designed to downscale the VIIRS 375-m floodproducts to 30-m flood products. The software uses VIIRS daily composite flood product as a basis for the downscaling.
VIIRS downscaling software is designed to downscale the VIIRS 375-m flood products to 30-m flood products. The software uses VIIRS daily composite flood product as a basis for the downscaling.
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River Flood: 1 day VIIRS composite
[RIVER-FLDglobal-composite1]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 1 day.
For more information visit: Here
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River Flood: 5 day VIIRS composite
[RIVER-FLDglobal-composite]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 5 days.
For more information visit: Here
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River Flood: ABI-daily
[River-Flood-ABI]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 5-min CONUS imagery since sunrise on the given day. These products are expected to be most useful in mid- and low-latitude locations.
CONUS region
Quick guide
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River Flood: ABI-daily (tsp)
[River-Flood-ABItsp]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 5-min CONUS imagery since sunrize through the given hour. These products are expected to be most useful in mid- and low-latitude locations.
CONUS region
Quick guide
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River Flood: ABI-hourly
[River-Flood-ABI-hourly]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 5-min CONUS imagery since sunrize through the given hour. These products are expected to be most useful in mid- and low-latitude locations.
CONUS region
Quick guide
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River Flood: ABI-hourly (tsp)
[River-Flood-ABItsp-hourly]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 5-min CONUS imagery since sunrize through the given hour. These products are expected to be most useful in mid- and low-latitude locations.
CONUS region
Quick guide
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River Flood: AHI
[RIVER-FLD-AHI]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 10-min imagery since sunrise on the given day. These products are expected to be most useful in mid- and low-latitude locations.
For more information visit: Here
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River Flood: Alaska
[RIVER-FLDall-AP]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Alaska region
Quick guide
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River Flood: Alaska (transparent)
[RIVER-FLDtsp-AP]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Alaska region(Transparent flood-free land)
Quick guide
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River Flood: Joint ABI/VIIRS
[RIVER-FLD-joint-ABI]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available ABI-full disk imagery and VIIRS imagery since sunrise on the given day.
For more information visit: Here
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River Flood: Joint AHI/VIIRS
[RIVER-FLD-joint-AHI]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud,...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available AHI-full disk imagery and VIIRS imagery since sunrise on the given day.
For more information visit: Here
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River Flood: Missouri Basin
[RIVER-FLDall-MB]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Missouri Basin
Quick guide
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River Flood: Missouri Basin (transparent)
[RIVER-FLDtsp-MB]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Missouri Basin(Transparent flood-free land)
Quick guide
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River Flood: North Central Basin
[RIVER-FLDall-NC]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North Central Basin
Quick guide
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River Flood: North Central Basin (transparent)
[RIVER-FLDtsp-NC]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North Central Basin(Transparent flood-free land)
Quick guide
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River Flood: North East Basin
[RIVER-FLDall-NE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North East Basin
Quick guide
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River Flood: North East Basin (transparent)
[RIVER-FLDtsp-NE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North East Basin(Transparent flood-free land)
Quick guide
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River Flood: North West
[RIVER-FLDall-NW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Northwest Region
Quick guide
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River Flood: North West (transparent)
[RIVER-FLDtsp-NW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Northwest Region(Transparent flood-free land)
Quick guide
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River Flood: South East
[RIVER-FLDall-SE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southeast Region
Quick guide
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River Flood: South East (transparent)
[RIVER-FLDtsp-SE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southeast Region(Transparent flood-free land)
Quick guide
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River Flood: South West
[RIVER-FLDall-SW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southwest Region
Quick guide
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River Flood: South West (tsp)
[RIVER-FLDtsp-SW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southwest Region(Transparent flood-free land)
Quick guide
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River Flood: US
[RIVER-FLDall-US]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
US
Quick guide
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River Flood: US (transparent)
[RIVER-FLDtsp-US]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
US(Transparent flood-free land)
Quick guide
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River Flood: West Gulf Basin
[RIVER-FLDall-WG]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
West Gulf Basin
Quick guide
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River Flood: West Gulf Basin (transparent)
[RIVER-FLDtsp-WG]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
West Gulf Basin(Transparent flood-free land)
Quick guide
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MADIS Surface DewPoint
[MADIS-dewt]
The MADIS Surface Dewpoint uses a 2-dimensional boxcar spatial convolutionto smooth hourly average surface observations from the NCEP Meteorological Assimilation Data Ingest System (MADIS) to a grid resolution of 0.7 degree...
The MADIS Surface Dewpoint uses a 2-dimensional boxcar spatial convolution to smooth hourly average surface observations from the NCEP Meteorological Assimilation Data Ingest System (MADIS) to a grid resolution of 0.7 degree latitude/longitude. The source data is obtained in near-real time from https://madis.ncep.noaa.gov/.
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River Flood: Alaska
[RIVER-FLDall-AP]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Alaska region
Quick guide
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River Flood: Alaska (transparent)
[RIVER-FLDtsp-AP]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Alaska region(Transparent flood-free land)
Quick guide
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River Flood: Missouri Basin
[RIVER-FLDall-MB]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Missouri Basin
Quick guide
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River Flood: Missouri Basin (transparent)
[RIVER-FLDtsp-MB]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Missouri Basin(Transparent flood-free land)
Quick guide
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River Flood: North Central Basin
[RIVER-FLDall-NC]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North Central Basin
Quick guide
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River Flood: North Central Basin (transparent)
[RIVER-FLDtsp-NC]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North Central Basin(Transparent flood-free land)
Quick guide
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River Flood: North East Basin
[RIVER-FLDall-NE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North East Basin
Quick guide
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River Flood: North East Basin (transparent)
[RIVER-FLDtsp-NE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North East Basin(Transparent flood-free land)
Quick guide
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River Flood: North West
[RIVER-FLDall-NW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Northwest Region
Quick guide
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River Flood: North West (transparent)
[RIVER-FLDtsp-NW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Northwest Region(Transparent flood-free land)
Quick guide
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River Flood: South East
[RIVER-FLDall-SE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southeast Region
Quick guide
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River Flood: South East (transparent)
[RIVER-FLDtsp-SE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southeast Region(Transparent flood-free land)
Quick guide
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River Flood: South West
[RIVER-FLDall-SW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southwest Region
Quick guide
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River Flood: South West (tsp)
[RIVER-FLDtsp-SW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southwest Region(Transparent flood-free land)
Quick guide
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River Flood: US
[RIVER-FLDall-US]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
US
Quick guide
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River Flood: US (transparent)
[RIVER-FLDtsp-US]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
US(Transparent flood-free land)
Quick guide
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River Flood: West Gulf Basin
[RIVER-FLDall-WG]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
West Gulf Basin
Quick guide
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River Flood: West Gulf Basin (transparent)
[RIVER-FLDtsp-WG]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
West Gulf Basin(Transparent flood-free land)
Quick guide
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MIRS 90Ghz Brightness Temperature
[MIRS-BT90]
MIRS 90Ghz Brightness Temperature
MIRS 90Ghz Brightness Temperature
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MIRS Rain Rate
[MIRS-RainRate]
With inputs from the ATMS (Advanced Technology Microwave Sounder) sensoraboard JPSS satellites, the rainfall rate product from the Microwave Integrated Retrieval System (MIRS) identifies the intensity of rain at the...
With inputs from the ATMS (Advanced Technology Microwave Sounder) sensor aboard JPSS satellites, the rainfall rate product from the Microwave Integrated Retrieval System (MIRS) identifies the intensity of rain at the instant the satellite is passing over the area. It is derived from three vertically integrated MIRS products: Cloud Liquid Water (CLW), Rain Water Path (RWP), and Ice Water Path (IWP), taking advantage of the physical relationship found between atmospheric hydrometeor amounts and surface rain rate.
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SNPP Day/Night AM Composite - Adaptive
[nppadpam]
NPP Day/Night AM Composite - Adaptive
NPP Day/Night AM Composite - Adaptive
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SNPP Day/Night Band (DNB) - Honolulu DB
[nppdnbdyn-hnl]
NPP Day/Night Band (DNB) - Honolulu DB
NPP Day/Night Band (DNB) - Honolulu DB
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Aqua Aerosol Optical Depth
[AQUA-AER]
MODIS: AQUA Aerosol Optical Depth (ta)
MODIS: AQUA Aerosol Optical Depth (ta)
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Aqua False Color
[aquafalsecolor]
CIMSS-MODIS Satellite False Color (Aqua)
CIMSS-MODIS Satellite False Color (Aqua)
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Terra Aerosol Optical Depth
[TERRA-AER]
MODIS: TERRA Aerosol Optical Depth (ta)
MODIS: TERRA Aerosol Optical Depth (ta)
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Total Column Sulphur Dioxide
[AURA-SO2]
AURA - OMI Total Column Sulphur Dioxide (SO2)
AURA - OMI Total Column Sulphur Dioxide (SO2)
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True Color Clear View
[BRDF]
MODIS Clear View ConUS Composite. BRDF (Bidirectional ReluctanceDistribution Function) is a 16-day cloud-free composite.
MODIS Clear View ConUS Composite. BRDF (Bidirectional Reluctance Distribution Function) is a 16-day cloud-free composite.
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WI USGS Landsat Poster
[wilandsat]
This is a georeferenced poster from the USGS. The original source is:http://eros.usgs.gov/imagegallery/landsat-state-mosaics unfortunately the original poster imagery without graphics burned-in is not available.
This is a georeferenced poster from the USGS. The original source is: http://eros.usgs.gov/imagegallery/landsat-state-mosaics unfortunately the original poster imagery without graphics burned-in is not available.
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Landsat 8 Look Natural Color (Swaths)
[lsat8-llook-fc]
View of lsat8-llook-fc-scenes
View of lsat8-llook-fc-scenes
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Landsat 8 Look Thermal IR (Swaths)
[lsat8-llook-tir]
View of lsat8-llook-tir-scenes
View of lsat8-llook-tir-scenes
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Landsat 9 Look Natural Color (Swaths)
[lsat9-llook-fc]
View of lsat9-llook-fc-scenes
View of lsat9-llook-fc-scenes
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Landsat 9 Look Thermal IR (Swaths)
[lsat9-llook-tir]
View of lsat9-llook-tir-scenes
View of lsat9-llook-tir-scenes
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Landsat Footprints (WRS-2)
[wrs2-land]
The Worldwide Reference System (WRS) is a global notation used incataloging Landsat data. Landsat 8 and Landsat 7 follow the WRS-2, as did Landsat 5 and Landsat 4.
The Worldwide Reference System (WRS) is a global notation used in cataloging Landsat data. Landsat 8 and Landsat 7 follow the WRS-2, as did Landsat 5 and Landsat 4.
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Gridded NUCAPS Lapse Rate - 400-200mb
[nucaps-grid-lr-400-200]
Lapse Rate at 400-200mb represents the rate at which air temperaturechanges with height in the specified atmosphere layer. Gridded NUCAPS lapse rate is calculated according to the Poisson Equation. Lapse rate can...
Lapse Rate at 400-200mb represents the rate at which air temperature changes with height in the specified atmosphere layer. Gridded NUCAPS lapse rate is calculated according to the Poisson Equation. Lapse rate can be used to assess atmospheric stability and is most applicable to pre-convective forecasting. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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Gridded NUCAPS Lapse Rate - 700-500mb
[nucaps-grid-lr-700-500]
Lapse Rate at 700-500mb represents the rate at which air temperaturechanges with height in the specified atmosphere layer. Gridded NUCAPS lapse rate is calculated according to the Poisson Equation. Lapse rate can...
Lapse Rate at 700-500mb represents the rate at which air temperature changes with height in the specified atmosphere layer. Gridded NUCAPS lapse rate is calculated according to the Poisson Equation. Lapse rate can be used to assess atmospheric stability and is most applicable to pre-convective forecasting. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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Gridded NUCAPS Lapse Rate - 850-300mb
[nucaps-grid-lr-850-300]
Lapse Rate at 850-300mb represents the rate at which air temperaturechanges with height in the specified atmosphere layer. Gridded NUCAPS lapse rate is calculated according to the Poisson Equation. Lapse rate can...
Lapse Rate at 850-300mb represents the rate at which air temperature changes with height in the specified atmosphere layer. Gridded NUCAPS lapse rate is calculated according to the Poisson Equation. Lapse rate can be used to assess atmospheric stability and is most applicable to pre-convective forecasting. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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Gridded NUCAPS Lapse Rate - 850-500mb
[nucaps-grid-lr-850-500]
Lapse Rate at 850-500mb represents the rate at which air temperaturechanges with height in the specified atmosphere layer. Gridded NUCAPS lapse rate is calculated according to the Poisson Equation. Lapse rate can...
Lapse Rate at 850-500mb represents the rate at which air temperature changes with height in the specified atmosphere layer. Gridded NUCAPS lapse rate is calculated according to the Poisson Equation. Lapse rate can be used to assess atmospheric stability and is most applicable to pre-convective forecasting. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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Gridded NUCAPS Lapse Rate - 850-700mb
[nucaps-grid-lr-850-700]
Lapse Rate at 850-700mb represents the rate at which air temperaturechanges with height in the specified atmosphere layer. Gridded NUCAPS lapse rate is calculated according to the Poisson Equation. Lapse rate can...
Lapse Rate at 850-700mb represents the rate at which air temperature changes with height in the specified atmosphere layer. Gridded NUCAPS lapse rate is calculated according to the Poisson Equation. Lapse rate can be used to assess atmospheric stability and is most applicable to pre-convective forecasting. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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Gridded NUCAPS Lapse Rate - 950-850mb
[nucaps-grid-lr-950-850]
Lapse Rate at 950-850mb represents the rate at which air temperaturechanges with height in the specified atmosphere layer. Gridded NUCAPS lapse rate is calculated according to the Poisson Equation. Lapse rate can...
Lapse Rate at 950-850mb represents the rate at which air temperature changes with height in the specified atmosphere layer. Gridded NUCAPS lapse rate is calculated according to the Poisson Equation. Lapse rate can be used to assess atmospheric stability and is most applicable to pre-convective forecasting. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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GOES 18 - RGB
[G18-daynight]
True color - day
Cloud microphysics - night
True color - day
Cloud microphysics - night
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Himawari-9 - RGB
[H09-RGB341]
Red: 0.64 µm: Red
Green: 0.86 µm: Veggie
Blue 0.47 µm: Blue
Red: 0.64 µm: Red
Green: 0.86 µm: Veggie
Blue 0.47 µm: Blue
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Earth Networks flashes 10-min aggregation
[ENI-flash-pts-10min]
The aggregation *ends* on the ABI file start time.
The aggregation *ends* on the ABI file start time.
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GOES-East GLM FED CONUS
[GOESEastGLMFEDRadC]
GOES-East flash-extent density, a 5-min accumulation of flashes at eachpoint.
GOES-East flash-extent density, a 5-min accumulation of flashes at each point.
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GOES-West GLM FED CONUS
[GOESWestGLMFEDRadC]
GOES-West flash-extent density, a 5-min accumulation of flashes at eachpoint.
GOES-West flash-extent density, a 5-min accumulation of flashes at each point.
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LightningCast (ABI+MRMS) GOES-East CONUS
[PLTG-abi-mrms-GOESEastRadC]
A LightningCast model with ABI and MRMS predictors.
A LightningCast model with ABI and MRMS predictors.
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LightningCast (ABI+MRMS) GOES-West CONUS
[PLTG-abi-mrms-GOESWestRadC]
A LightningCast model with ABI and MRMS predictors
A LightningCast model with ABI and MRMS predictors
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LightningCast GOES-East CONUS
[PLTGGOESEastRadC]
An AI model that predicts the probability of lightning in the next 60minutes using GOES-R ABI data.
An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.
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LightningCast GOES-East FD (OCONUS)
[PLTGGOESEastRadF]
An AI model that predicts the probability of lightning in the next 60minutes using GOES-R ABI data.
An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.
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LightningCast GOES-East MESO1
[PLTGGOESEastRadM1]
An AI model that predicts the probability of lightning in the next 60minutes using GOES-R ABI data.
An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.
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LightningCast GOES-East MESO2
[PLTGGOESEastRadM2]
An AI model that predicts the probability of lightning in the next 60minutes using GOES-R ABI data.
An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.
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LightningCast GOES-West Alaska/Western Canada
[PLTGGOESWestRadFAKCAN]
An AI model that predicts the probability of lightning in the next 60minutes using GOES-R ABI data.
An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.
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LightningCast GOES-West American Samoa
[PLTGGOESWestRadFUSSAMOA]
An AI model that predicts the probability of lightning in the next 60minutes using GOES-R ABI data.
An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.
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LightningCast GOES-West CONUS
[PLTGGOESWestRadC]
An AI model that predicts the probability of lightning in the next 60minutes using GOES-R ABI data.
An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.
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LightningCast GOES-West MESO1
[PLTGGOESWestRadM1]
An AI model that predicts the probability of lightning in the next 60minutes using GOES-R ABI data.
An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.
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LightningCast GOES-West MESO2
[PLTGGOESWestRadM2]
An AI model that predicts the probability of lightning in the next 60minutes using GOES-R ABI data.
An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.
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GOES East GLM Full Disk Group Density
[glmgroupdensity]
The Geostationary Lightning Mapper, or GLM, on board GeostationaryOperational Environmental Satellite– R Series spacecraft, is the first operational lightning mapper flown on a geostationary satellite. GLM...
The Geostationary Lightning Mapper, or GLM, on board Geostationary Operational Environmental Satellite– R Series spacecraft, is the first operational lightning mapper flown on a geostationary satellite. GLM detects the light emitted by lightning at the tops of clouds day and night and collects information such as the frequency, location and extent of lightning discharges. The instrument measures total lightning, both in-cloud and cloud-to-ground, to aid in forecasting developing severe storms and a wide range of high-impact environmental phenomena including hailstorms, microburst winds, tornadoes, hurricanes, ash clouds, and snowstorms.
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GOES East GLM Full Disk Group Points
[glmgrouppoints]
The Geostationary Lightning Mapper, or GLM, on board GeostationaryOperational Environmental Satellite– R Series spacecraft, is the first operational lightning mapper flown on a geostationary satellite. GLM...
The Geostationary Lightning Mapper, or GLM, on board Geostationary Operational Environmental Satellite– R Series spacecraft, is the first operational lightning mapper flown on a geostationary satellite. GLM detects the light emitted by lightning at the tops of clouds day and night and collects information such as the frequency, location and extent of lightning discharges. The instrument measures total lightning, both in-cloud and cloud-to-ground, to aid in forecasting developing severe storms and a wide range of high-impact environmental phenomena including hailstorms, microburst winds, tornadoes, hurricanes, ash clouds, and snowstorms.
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MIRS 90Ghz Brightness Temperature
[MIRS-BT90]
MIRS 90Ghz Brightness Temperature
MIRS 90Ghz Brightness Temperature
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MIRS Rain Rate
[MIRS-RainRate]
With inputs from the ATMS (Advanced Technology Microwave Sounder) sensoraboard JPSS satellites, the rainfall rate product from the Microwave Integrated Retrieval System (MIRS) identifies the intensity of rain at the...
With inputs from the ATMS (Advanced Technology Microwave Sounder) sensor aboard JPSS satellites, the rainfall rate product from the Microwave Integrated Retrieval System (MIRS) identifies the intensity of rain at the instant the satellite is passing over the area. It is derived from three vertically integrated MIRS products: Cloud Liquid Water (CLW), Rain Water Path (RWP), and Ice Water Path (IWP), taking advantage of the physical relationship found between atmospheric hydrometeor amounts and surface rain rate.
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MIRS RainRate - Alaska (GINA)
[MIRS-RainRate-AK]
With inputs from the ATMS (Advanced Technology Microwave Sounder) sensoraboard JPSS satellites, the rainfall rate product from the Microwave Integrated Retrieval System (MIRS) identifies the intensity of rain at the...
With inputs from the ATMS (Advanced Technology Microwave Sounder) sensor aboard JPSS satellites, the rainfall rate product from the Microwave Integrated Retrieval System (MIRS) identifies the intensity of rain at the instant the satellite is passing over the area. It is derived from three vertically integrated MIRS products: Cloud Liquid Water (CLW), Rain Water Path (RWP), and Ice Water Path (IWP), taking advantage of the physical relationship found between atmospheric hydrometeor amounts and surface rain rate.
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Aqua Aerosol Optical Depth
[AQUA-AER]
MODIS: AQUA Aerosol Optical Depth (ta)
MODIS: AQUA Aerosol Optical Depth (ta)
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Aqua False Color
[aquafalsecolor]
CIMSS-MODIS Satellite False Color (Aqua)
CIMSS-MODIS Satellite False Color (Aqua)
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Terra Aerosol Optical Depth
[TERRA-AER]
MODIS: TERRA Aerosol Optical Depth (ta)
MODIS: TERRA Aerosol Optical Depth (ta)
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True Color Clear View
[BRDF]
MODIS Clear View ConUS Composite. BRDF (Bidirectional ReluctanceDistribution Function) is a 16-day cloud-free composite.
MODIS Clear View ConUS Composite. BRDF (Bidirectional Reluctance Distribution Function) is a 16-day cloud-free composite.
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Meteosat 8 SEVIRI Full Disk B01 Vis (0.6um)
[Met8-SEVIRI-FD-BAND01]
VIS0.6: Known from the Advanced Very High Resolution Radiometer (AVHRR) ofthe polar-orbiting NOAA satellites. It is essential for cloud detection, cloud tracking, scene identification, aerosol, and land surface and veg...
VIS0.6: Known from the Advanced Very High Resolution Radiometer (AVHRR) of the polar-orbiting NOAA satellites. It is essential for cloud detection, cloud tracking, scene identification, aerosol, and land surface and veg etation monitoring.
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Meteosat 8 SEVIRI Full Disk B02 Vis (0.8um)
[Met8-SEVIRI-FD-BAND02]
VIS0.8: Known from the Advanced Very High Resolution Radiometer (AVHRR) ofthe polar-orbiting NOAA satellites. It is essential for cloud detection, cloud tracking, scene identification, aerosol, and land surface and veg...
VIS0.8: Known from the Advanced Very High Resolution Radiometer (AVHRR) of the polar-orbiting NOAA satellites. It is essential for cloud detection, cloud tracking, scene identification, aerosol, and land surface and veg etation monitoring.
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Meteosat 8 SEVIRI Full Disk B03 NIR (1.6um)
[Met8-SEVIRI-FD-BAND03]
NIR1.6: Discriminates between snow and cloud, ice and water clouds, andprovides aerosol infor mation. Observations are, among others, available from the Along Track Scanning Radiometer (ATSR) on the Earth Remote Sensing...
NIR1.6: Discriminates between snow and cloud, ice and water clouds, and provides aerosol infor mation. Observations are, among others, available from the Along Track Scanning Radiometer (ATSR) on the Earth Remote Sensing Satellite (ERS).
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Meteosat 8 SEVIRI Full Disk B04 IR Fire (3.9um)
[Met8-SEVIRI-FD-BAND04]
IR3.9: Known from AVHRR. Primarily for low cloud and fog detection (Eyre etal. 1984; Lee et al. 1997). Also supports measurement of land and sea surface temperature at night and increases the low- level wind coverage...
IR3.9: Known from AVHRR. Primarily for low cloud and fog detection (Eyre et al. 1984; Lee et al. 1997). Also supports measurement of land and sea surface temperature at night and increases the low- level wind coverage from cloud tracking (Velden et al. 2001). For MSG, the spectral band has been broadened to longer wavelengths to improve
signal-to-noise ratio.
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Meteosat 8 SEVIRI Full Disk B05 WV High (6.2um)
[Met8-SEVIRI-FD-BAND05]
WV6.2: Continues mission of
Meteosat broadband water vapor channel forobserving water vapor and winds. Enhanced to two channels peaking at different levels in the tropo sphere. Also supports height allocation of...
WV6.2: Continues mission of
Meteosat broadband water vapor channel for observing water vapor and winds. Enhanced to two channels peaking at different levels in the tropo sphere. Also supports height allocation of semitransparent clouds (Nieman et al. 1993; Schmetz et al. 1993).
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Meteosat 8 SEVIRI Full Disk B05 WV High (6.2um) enhanced
[Met8-SEVIRI-FD-BAND05-enh]
WV6.2: Continues mission of
Meteosat broadband water vapor channel forobserving water vapor and winds. Enhanced to two channels peaking at different levels in the tropo sphere. Also supports height allocation of...
WV6.2: Continues mission of
Meteosat broadband water vapor channel for observing water vapor and winds. Enhanced to two channels peaking at different levels in the tropo sphere. Also supports height allocation of semitransparent clouds (Nieman et al. 1993; Schmetz et al. 1993).
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Meteosat 8 SEVIRI Full Disk B06 WV Mid (7.3um)
[Met8-SEVIRI-FD-BAND06]
WV7.3: Continues mission of
Meteosat broadband water vapor channel for observing water vapor and winds. Enhanced to two channels peaking at different levels in the tropo sphere. Also supports height allocation of...
WV7.3: Continues mission of
Meteosat broadband water vapor channel for ob serving water vapor and winds. Enhanced to two channels peaking at different levels in the tropo sphere. Also supports height allocation of semitransparent clouds (Nieman et al. 1993; Schmetz et al. 1993).
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Meteosat 8 SEVIRI Full Disk B07 IR Phase (8.7um)
[Met8-SEVIRI-FD-BAND07]
IR8.7: Known from the High Resolution Infrared Sounder (HIRS) instrumenton the polar-orbiting NOAA satellites. The channel provides quantitative information on thin cirrus clouds and supports the discrimination between...
IR8.7: Known from the High Resolution Infrared Sounder (HIRS) instrument on
the polar-orbiting NOAA satellites. The
channel provides quantitative information on thin cirrus clouds and supports the discrimination between ice and water clouds.
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Meteosat 8 SEVIRI Full Disk B08 IR Ozone (9.7um)
[Met8-SEVIRI-FD-BAND08]
IR9.7: Known from HIRS and current GOES satellites. Ozone radiances couldbe used as an input to numerical weather pre diction (NWP). As an experi mental channel, it will be used for tracking ozone patterns that...
IR9.7: Known from HIRS and current GOES satellites. Ozone radiances could be used as an input to numerical weather pre diction (NWP). As an experi mental channel, it will be used for tracking ozone patterns that should be representative for wind motion in the lower strato sphere. The evolution of the to tal ozone field with time can also be monitored.
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Meteosat 8 SEVIRI Full Disk B09 IR Clean (10.8um)
[Met8-SEVIRI-FD-BAND09]
IR10.8: Well-known split window channel (e.g., AVHRR). Essential formeasur ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata...
IR10.8: Well-known split window channel (e.g., AVHRR). Essential for measur ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and
volcanic ash clouds (Prata 1989).
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Meteosat 8 SEVIRI Full Disk B09 IR Clean (10.8um) enhanced
[Met8-SEVIRI-FD-BAND09-enh]
IR10.8: Well-known split window channel (e.g., AVHRR). Essential formeasur ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata...
IR10.8: Well-known split window channel (e.g., AVHRR). Essential for measur ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and
volcanic ash clouds (Prata 1989).
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Meteosat 8 SEVIRI Full Disk B10 IR Dirty (12.0 um)
[Met8-SEVIRI-FD-BAND10]
IR12.0: Well-known split window channel (e.g., AVHRR). Essential formeasur ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata...
IR12.0: Well-known split window channel (e.g., AVHRR). Essential for measur ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and
volcanic ash clouds (Prata 1989).
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Meteosat 8 SEVIRI Full Disk B11 IR CO2(13.4UM)
[Met8-SEVIRI-FD-BAND11]
IR13.4: The CO2 absorption channel known
from the former GOES VISSRAtmospheric Sounder (VAS) instrument, where VISSR stands for Vis ible Infrared Spin-Scan Radiometer. It improves height allocation of tenuous...
IR13.4: The CO2 absorption channel known
from the former GOES VISSR Atmospheric Sounder (VAS) instrument, where VISSR stands for Vis ible Infrared Spin-Scan Radiometer. It improves height allocation of tenuous cirrus clouds (Menzel et al. 1983). In cloud-free areas, it will contribute to temperature information from the lower tro posphere that can be used for estimating static in-stability.
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Meteosat 8 SEVIRI Full Disk B12 Vis HRV (0.7um)
[Met8-SEVIRI-HRV-BAND12]
The high-resolution visible (HRV) channel covers half of the full disk inthe east–west direction and a full disk in the north–south direction (see Fig. 3). The high-resolution visible channel has a spatial resolution...
The high-resolution visible (HRV) channel covers half of the full disk in the east–west direction and a full disk in the north–south direction (see Fig. 3). The high-resolution visible channel has a spatial resolution of 1.67 km, as the oversampling factor is 1.67 the sampling distance is 1 km at nadir.
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Meteosat 11 SEVIRI Full Disk B01 Vis (0.6um)
[Met11-SEVIRI-FD-BAND01]
VIS0.6: Known from the Advanced Very High Resolution Radiometer (AVHRR) ofthe polar-orbiting NOAA satellites. It is essential for cloud detection, cloud tracking, scene identification, aerosol, and land surface and veg...
VIS0.6: Known from the Advanced Very High Resolution Radiometer (AVHRR) of the polar-orbiting NOAA satellites. It is essential for cloud detection, cloud tracking, scene identification, aerosol, and land surface and veg etation monitoring.
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Meteosat 11 SEVIRI Full Disk B02 Vis (0.8um)
[Met11-SEVIRI-FD-BAND02]
VIS0.8: Known from the Advanced Very High Resolution Radiometer (AVHRR) ofthe polar-orbiting NOAA satellites. It is essential for cloud detection, cloud tracking, scene identification, aerosol, and land surface and veg...
VIS0.8: Known from the Advanced Very High Resolution Radiometer (AVHRR) of the polar-orbiting NOAA satellites. It is essential for cloud detection, cloud tracking, scene identification, aerosol, and land surface and veg etation monitoring.
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Meteosat 11 SEVIRI Full Disk B03 NIR (1.6um)
[Met11-SEVIRI-FD-BAND03]
NIR1.6: Discriminates between snow and cloud, ice and water clouds, andprovides aerosol infor mation. Observations are, among others, available from the Along Track Scanning Radiometer (ATSR) on the Earth Remote Sensing...
NIR1.6: Discriminates between snow and cloud, ice and water clouds, and provides aerosol infor mation. Observations are, among others, available from the Along Track Scanning Radiometer (ATSR) on the Earth Remote Sensing Satellite (ERS).
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Meteosat 11 SEVIRI Full Disk B04 IR Fire (3.9um)
[Met11-SEVIRI-FD-BAND04]
IR3.9: Known from AVHRR. Primarily for low cloud and fog detection (Eyre etal. 1984; Lee et al. 1997). Also supports measurement of land and sea surface temperature at night and increases the low- level wind coverage...
IR3.9: Known from AVHRR. Primarily for low cloud and fog detection (Eyre et al. 1984; Lee et al. 1997). Also supports measurement of land and sea surface temperature at night and increases the low- level wind coverage from cloud tracking (Velden et al. 2001). For MSG, the spectral band has been broadened to longer wavelengths to improve
signal-to-noise ratio.
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Meteosat 11 SEVIRI Full Disk B05 WV High (6.2um)
[Met11-SEVIRI-FD-BAND05]
WV6.2: Continues mission of
Meteosat broadband water vapor channel forobserving water vapor and winds. Enhanced to two channels peaking at different levels in the tropo sphere. Also supports height allocation of...
WV6.2: Continues mission of
Meteosat broadband water vapor channel for observing water vapor and winds. Enhanced to two channels peaking at different levels in the tropo sphere. Also supports height allocation of semitransparent clouds (Nieman et al. 1993; Schmetz et al. 1993).
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Meteosat 11 SEVIRI Full Disk B05 WV High (6.2um) enhanced
[Met11-SEVIRI-FD-BAND05-enh]
WV6.2: Continues mission of
Meteosat broadband water vapor channel forobserving water vapor and winds. Enhanced to two channels peaking at different levels in the tropo sphere. Also supports height allocation of...
WV6.2: Continues mission of
Meteosat broadband water vapor channel for observing water vapor and winds. Enhanced to two channels peaking at different levels in the tropo sphere. Also supports height allocation of semitransparent clouds (Nieman et al. 1993; Schmetz et al. 1993).
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Meteosat 11 SEVIRI Full Disk B06 WV Mid (7.3um)
[Met11-SEVIRI-FD-BAND06]
WV7.3: Continues mission of
Meteosat broadband water vapor channel for observing water vapor and winds. Enhanced to two channels peaking at different levels in the tropo sphere. Also supports height allocation of...
WV7.3: Continues mission of
Meteosat broadband water vapor channel for ob serving water vapor and winds. Enhanced to two channels peaking at different levels in the tropo sphere. Also supports height allocation of semitransparent clouds (Nieman et al. 1993; Schmetz et al. 1993).
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Meteosat 11 SEVIRI Full Disk B07 IR Phase (8.7 um)
[Met11-SEVIRI-FD-BAND07]
IR8.7: Known from the High Resolution Infrared Sounder (HIRS) instrumenton the polar-orbiting NOAA satellites. The channel provides quantitative information on thin cirrus clouds and supports the discrimination between...
IR8.7: Known from the High Resolution Infrared Sounder (HIRS) instrument on
the polar-orbiting NOAA satellites. The
channel provides quantitative information on thin cirrus clouds and supports the discrimination between ice and water clouds.
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Meteosat 11 SEVIRI Full Disk B08 IR Ozone (9.7um)
[Met11-SEVIRI-FD-BAND08]
IR9.7: Known from HIRS and current GOES satellites. Ozone radiances couldbe used as an input to numerical weather pre diction (NWP). As an experi mental channel, it will be used for tracking ozone patterns that...
IR9.7: Known from HIRS and current GOES satellites. Ozone radiances could be used as an input to numerical weather pre diction (NWP). As an experi mental channel, it will be used for tracking ozone patterns that should be representative for wind motion in the lower strato sphere. The evolution of the to tal ozone field with time can also be monitored.
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Meteosat 11 SEVIRI Full Disk B09 IR Clean (10.8um)
[Met11-SEVIRI-FD-BAND09]
IR10.8: Well-known split window channel (e.g., AVHRR). Essential formeasur ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata...
IR10.8: Well-known split window channel (e.g., AVHRR). Essential for measur ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and
volcanic ash clouds (Prata 1989).
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Meteosat 11 SEVIRI Full Disk B09 IR Clean (10.8um) enhanced
[Met11-SEVIRI-FD-BAND09-enh]
IR10.8: Well-known split window channel (e.g., AVHRR). Essential formeasur ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata...
IR10.8: Well-known split window channel (e.g., AVHRR). Essential for measur ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and
volcanic ash clouds (Prata 1989).
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Meteosat 11 SEVIRI Full Disk B10 IR Dirty (12.0um)
[Met11-SEVIRI-FD-BAND10]
IR12.0: Well-known split window channel (e.g., AVHRR). Essential formeasur ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata...
IR12.0: Well-known split window channel (e.g., AVHRR). Essential for measur ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and
volcanic ash clouds (Prata 1989).
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Meteosat 11 SEVIRI Full Disk B11 IR CO2 (13.4um)
[Met11-SEVIRI-FD-BAND11]
IR13.4: The CO2 absorption channel known
from the former GOES VISSRAtmospheric Sounder (VAS) instrument, where VISSR stands for Vis ible Infrared Spin-Scan Radiometer. It improves height allocation of tenuous...
IR13.4: The CO2 absorption channel known
from the former GOES VISSR Atmospheric Sounder (VAS) instrument, where VISSR stands for Vis ible Infrared Spin-Scan Radiometer. It improves height allocation of tenuous cirrus clouds (Menzel et al. 1983). In cloud-free areas, it will contribute to temperature information from the lower tro posphere that can be used for estimating static in-stability.
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Meteosat 11 SEVIRI Full Disk B12 Vis HRV (0.7um)
[Met11-SEVIRI-HRV-BAND12]
The high-resolution visible (HRV) channel covers half of the full disk inthe east–west direction and a full disk in the north–south direction (see Fig. 3). The high-resolution visible channel has a spatial resolution...
The high-resolution visible (HRV) channel covers half of the full disk in the east–west direction and a full disk in the north–south direction (see Fig. 3). The high-resolution visible channel has a spatial resolution of 1.67 km, as the oversampling factor is 1.67 the sampling distance is 1 km at nadir.
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Gridded NUCAPS Total Column Methane (CH4)
[nucaps-grid-ch4-total]
Total Column Methane represents the amount of methane in the atmosphericcolumn in units of 10e19 molecules/cm2. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a...
Total Column Methane represents the amount of methane in the atmospheric column in units of 10e19 molecules/cm2. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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INPE Monitoring of Vegetation Fires - Multimission
[GNCA-INPE-MVF]
INPE Global Monitoring of Vegetation Fires (MVF) Multimission. SatellitesAQUA, TERRA, METOP, NOAA, NPP, METEOSAT and GOES
INPE Global Monitoring of Vegetation Fires (MVF) Multimission. Satellites AQUA, TERRA, METOP, NOAA, NPP, METEOSAT and GOES
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RANET Day 1-3 QPF & 850hPa Winds
[GNCA-RANET-crb1-crb3]
Quantitative Precipitation Forecast (QPF) & 850 hPa Winds for day 1-3
Quantitative Precipitation Forecast (QPF) & 850 hPa Winds for day 1-3
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RANET Day 1-6 Surface Forecast
[GNCA-RANET-d1-d6]
NCEP Day 1-6 Surface Forecast
NCEP Day 1-6 Surface Forecast
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RANET Hazards Outlook - Central America (ENG)
[GNCA-RANET-central-america-hazard]
Climate Prediction Center Central America Weekly Hazards Outlook
Climate Prediction Center Central America Weekly Hazards Outlook
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RANET Hazards Outlook - Central America (SP)
[GNCA-RANET-central-america-hazard-sp]
Climate Prediction Center Central America Weekly Hazards Outlook (SP)
Climate Prediction Center Central America Weekly Hazards Outlook (SP)
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RANET Hazards Outlook - Hispaniola (ENG)
[GNCA-RANET-haiti-hazard]
Climate Prediction Center’s Weekly Hispaniola Hazards Outlook
Climate Prediction Center’s Weekly Hispaniola Hazards Outlook
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RANET Sea Surface Temperature and Anomalies
[GNCA-RANET-gsstanim]
NCEP Animation of weekly averaged SST (Sea Surface Temperature) & Anomaliesfor the past 12 weeks
NCEP Animation of weekly averaged SST (Sea Surface Temperature) & Anomalies for the past 12 weeks
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NAIP WI
[NAIPWI]
National Agricultural Imagery Program aerial photography from the WisconsinFarm Service Agency (WI-FSA) of the USDA.
National Agricultural Imagery Program aerial photography from the Wisconsin Farm Service Agency (WI-FSA) of the USDA.
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NAIP WI Color Infrared
[NAIPWICIR]
National Agricultural Imagery Program aerial photography from the WisconsinFarm Service Agency (WI-FSA) of the USDA (Color Infrared)
National Agricultural Imagery Program aerial photography from the Wisconsin Farm Service Agency (WI-FSA) of the USDA (Color Infrared)
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NEXRAD CanAm Precipitation Phase
[nexrphase]
NEXRAD CanAm Precipitation Phase
NEXRAD CanAm Precipitation Phase
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Storm Cell ID and Tracking - Filter 1
[SCIT-ALL]
Storm Cell Identification and Tracking (SCIT)
Filters
1| ALLCells 2| Moderate Threat level Cells 3| Severe Threat level Cells
Storm Cell Identification and Tracking (SCIT)
Filters
1| ALL Cells
2| Moderate Threat level Cells
3| Severe Threat level Cells
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Storm Cell ID and Tracking - Filter 2
[SCIT-MOD]
Storm Cell Identification and Tracking (SCIT)
Filters
1| ALLCells 2| Moderate Threat level Cells 3| Severe Threat level Cells
Storm Cell Identification and Tracking (SCIT)
Filters
1| ALL Cells
2| Moderate Threat level Cells
3| Severe Threat level Cells
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Storm Cell ID and Tracking - Filter 3
[SCIT-SEV]
Storm Cell Identification and Tracking (SCIT)
Filters
1| ALLCells 2| Moderate Threat level Cells 3| Severe Threat level Cells
Storm Cell Identification and Tracking (SCIT)
Filters
1| ALL Cells
2| Moderate Threat level Cells
3| Severe Threat level Cells
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Storm Cell ID and Tracking - Forecast 2
[SCIT-MOD-FCST]
Storm Cell Identification and Tracking (SCIT)
Filter2 - 15min ForecastTrajectories 1| ALL Cells 2| Moderate Threat level Cells 3| Severe Threat level Cells
Storm Cell Identification and Tracking (SCIT)
Filter2 - 15min Forecast Trajectories
1| ALL Cells
2| Moderate Threat level Cells
3| Severe Threat level Cells
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National Reflectivity MRMS Composite
[nexrcomp]
The Multi-Radar Multi-Sensor (MRMS) system is now operational at theNational Centers for Environmental Prediction (NCEP). The MRMS system consists of the Warning Decision Support System–Integrated Information...
The Multi-Radar Multi-Sensor (MRMS) system is now operational at the National Centers for Environmental Prediction (NCEP). The MRMS system consists of the Warning Decision Support System–Integrated Information suite of severe weather and aviation products and the quantitative precipitation estimation (QPE) products created by the National Mosaic and Multi-Sensor QPE system. The MRMS system provides operational guidance for severe convective weather, QPE, and aviation hazards on a seamless three-dimensional grid that is created at a spatial resolution of 0.01° latitude × 0.01° longitude, with 33 vertical levels, every 2 min over the conterminous United States (CONUS) and southern Canada.
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National Reflectivity MRMS Composite mask
[nexrrain]
The Multi-Radar Multi-Sensor (MRMS) system is now operational at theNational Centers for Environmental Prediction (NCEP). The MRMS system consists of the Warning Decision Support System–Integrated Information...
The Multi-Radar Multi-Sensor (MRMS) system is now operational at the National Centers for Environmental Prediction (NCEP). The MRMS system consists of the Warning Decision Support System–Integrated Information suite of severe weather and aviation products and the quantitative precipitation estimation (QPE) products created by the National Mosaic and Multi-Sensor QPE system. The MRMS system provides operational guidance for severe convective weather, QPE, and aviation hazards on a seamless three-dimensional grid that is created at a spatial resolution of 0.01° latitude × 0.01° longitude, with 33 vertical levels, every 2 min over the conterminous United States (CONUS) and southern Canada.
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NEXRAD ConUS 1hr Precipitation Total
[nexr1hpcp]
One-Hour Precipitation (N1P/78)
This product displays estimated one-hourprecipitation accumulation on a 1.1-nm x 1-degree grid using the Precipitation Processing System (PPS) algorithm. This product assesses...
One-Hour Precipitation (N1P/78)
This product displays estimated one-hour precipitation accumulation on a 1.1-nm x 1-degree grid using the Precipitation Processing System (PPS) algorithm. This product assesses rainfall intensities for flash flood warnings, urban flood statements, and special weather statements.
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NEXRAD ConUS Digital Integrated Liquid
[nexrdvl]
This product color codes and plots the water content of a 2.2 x 2.2nautical mile (nm) column of air. It is an effective hail indicator that can be used to locate most significant storms and identify areas of heavy...
This product color codes and plots the water content of a 2.2 x 2.2 nautical mile (nm) column of air. It is an effective hail indicator that can be used to locate most significant storms and identify areas of heavy rainfall. The DVL version of the product provides a higher spatial resolution and enhanced processing.
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NEXRAD ConUS Enhanced Echo Tops
[nexreet]
This product generates a color coded image that shows the height of an echotop. Scientists use this product to quickly estimate the most intense convection and higher echo tops, as an aid to identify storm structure...
This product generates a color coded image that shows the height of an echo top. Scientists use this product to quickly estimate the most intense convection and higher echo tops, as an aid to identify storm structure features, and for pilot briefing purposes. The EET version of the product provided a higher spatial resolution, and enhanced processing, including identification of weather that is higher than the radar can scan.
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NEXRAD ConUS Hybrid Hydrometeor Class
[nexrhhc]
Hydrometeor Classification is a computer algorithm output that tries toclassify targets in the radar volume. The product compares targets to a set of predefined categories, and displays a list of the most likely echo...
Hydrometeor Classification is a computer algorithm output that tries to classify targets in the radar volume. The product compares targets to a set of predefined categories, and displays a list of the most likely echo sources.
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NEXRAD ConUS Hybrid Reflectivity
[nexrdhr]
The same as N*R products, except data values are actual reflectivity valuesinstead of categories, data extends to further range, and additional elevations are available. Products from elevation angles at or below 3.5...
The same as N*R products, except data values are actual reflectivity values instead of categories, data extends to further range, and additional elevations are available. Products from elevation angles at or below 3.5 degrees are available, and select sites may also scan at an additional low elevation angle, as low as -0.2 degrees. Specific elevation angles depend on the site and scanning mode of the Radar.
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NEXRAD ConUS Hybrid Reflectivity mask
[nexrhres]
Values are actual reflectivity values instead of categories, data extendsto further range, and additional elevations are available. Products from elevation angles at or below 3.5 degrees are available, and select sites...
Values are actual reflectivity values instead of categories, data extends to further range, and additional elevations are available. Products from elevation angles at or below 3.5 degrees are available, and select sites may also scan at an additional low elevation angle, as low as -0.2 degrees. Specific elevation angles depend on the site and scanning mode of the Radar.
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NEXRAD ConUS Storm Total Precipitation
[nexrstorm]
Storm Total Precipitation (NTP/80)
This product uses the PPS algorithm tocreate a continuously updated estimate of a storm’s accumulated precipitation. Accumulation is tracked on a 1.1 nm x 1 degree grid....
Storm Total Precipitation (NTP/80)
This product uses the PPS algorithm to create a continuously updated estimate of a storm’s accumulated precipitation. Accumulation is tracked on a 1.1 nm x 1 degree grid. Scientists use this product to locate flood potential over urban or rural areas, estimate total basin runoff, and provide rainfall data 24 hours a day.
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NEXRAD Alaska Base Reflectivity
[NEXRAD-Alaska]
WSR 88D NEXRAD Radar Mosiac Base Reflectivity Tilt 1 for Alaska Region.Bethel (KABC) Sitka (KACG) Nome (KAEC) Anchorage (KANG) Middleton Island (KAIH) King Salmon (KAKC) Fairbanks (KAPD)
WSR 88D NEXRAD Radar Mosiac Base Reflectivity Tilt 1 for Alaska Region.
Bethel (KABC)
Sitka (KACG)
Nome (KAEC)
Anchorage (KANG)
Middleton Island (KAIH)
King Salmon (KAKC)
Fairbanks (KAPD)
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NEXRAD Guam Base Reflectivity
[NEXRAD-Guam]
NEXRAD Guam Base Reflectivity
NEXRAD Guam Base Reflectivity
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NEXRAD Hawaii Base Reflectivity
[NEXRAD-Hawaii]
NEXRAD Hawaii Base Reflectivity
NEXRAD Hawaii Base Reflectivity
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NEXRAD Puerto Rico Base Reflectivity
[NEXRAD-PuertoRico]
WSR 88D NEXRAD Radar Base Reflectivity Tilt 1 for San Juan, Puerto Rico
WSR 88D NEXRAD Radar Base Reflectivity Tilt 1 for San Juan, Puerto Rico
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CMORPH2 1-Day Precip Accumulation
[c2accum1dy]
This satellite-derived precipitation product represents global 1-dayaccumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree...
This satellite-derived precipitation product represents global 1-day accumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree lat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include various rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available polar or "low earth" (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS).
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CMORPH2 1-Hour Precip Accumulation
[c2accum1hr]
This satellite-derived precipitation product represents global 1-houraccumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree...
This satellite-derived precipitation product represents global 1-hour accumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree lat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include various rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available polar or "low earth" (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS).
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CMORPH2 7-Day Precip Accumulation
[c2accum7dy]
This satellite-derived precipitation product represents global 7-dayaccumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree...
This satellite-derived precipitation product represents global 7-day accumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree lat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include various rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available polar or "low earth" (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS).
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Fronts and Troughs
[Fronts]
NCEP Frontal Analysis: fronts and troughs
NCEP Frontal Analysis: fronts and troughs
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Low/High Pressure
[HighLow]
NCEP Frontal Analysis: Highs and Lows
NCEP Frontal Analysis: Highs and Lows
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Snow Depth (SNODAS)
[SNODAS-Thickness]
SNODAS (SNOw Data Assimilation System) is a modeling and data assimilationsystem developed by NOAA National Weather Service"s NOHRSC (National Operational Hydrologic Remote Sensing Center) to provide the best possible...
SNODAS (SNOw Data Assimilation System) is a modeling and data assimilation system developed by NOAA National Weather Service"s NOHRSC (National Operational Hydrologic Remote Sensing Center) to provide the best possible estimates of snow cover and associated parameters to support hydrologic modeling and analysis. The aim of SNODAS is to provide a physically consistent framework to integrate snow data from satellite, airborne platforms, and ground stations with model estimates of snow cover. The 24hr Snow Thickness is a daily snapshot of snow thickness at 0600hr UTC.
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Snowfall Total - 24hr (SNODAS)
[SNODAS-Accumulate]
SNODAS (SNOw Data Assimilation System) is a modeling and data assimilationsystem developed by NOAA National Weather Service"s NOHRSC (National Operational Hydrologic Remote Sensing Center) to provide the best possible...
SNODAS (SNOw Data Assimilation System) is a modeling and data assimilation system developed by NOAA National Weather Service"s NOHRSC (National Operational Hydrologic Remote Sensing Center) to provide the best possible estimates of snow cover and associated parameters to support hydrologic modeling and analysis. The aim of SNODAS is to provide a physically consistent framework to integrate snow data from satellite, airborne platforms, and ground stations with model estimates of snow cover. 24hr Snow Fall Total is calculated every 24 hours at 0600hr UTC and posted shortly thereafter.
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AMG
[AMG]
Poligono del Área Metropolitana de Guadalajara
Poligono del Área Metropolitana de Guadalajara
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Vermont Flooding 2023 - Color Infrared
[vt-floods-2023-CIR]
Sentinel 2a captured this image of flooding in Vermont on July 11, 2023 at11:38am local time. This image represents bands 8, 4, and 3 as RGB showing vegetation in red and water in black or gray. Source: Copernicus Open...
Sentinel 2a captured this image of flooding in Vermont on July 11, 2023 at 11:38am local time. This image represents bands 8, 4, and 3 as RGB showing vegetation in red and water in black or gray. Source: Copernicus Open Access Hub.
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Vermont Flooding 2023 - Natural Color
[vt-floods-2023]
Sentinel 2a captured this image of flooding in Vermont on July 11, 2023 at11:38am local time. This image represents bands 4, 3, and 2 as RGB to approximate true color. Source: Copernicus Open Access Hub.
Sentinel 2a captured this image of flooding in Vermont on July 11, 2023 at 11:38am local time. This image represents bands 4, 3, and 2 as RGB to approximate true color. Source: Copernicus Open Access Hub.
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Vermont Flooding 2023 - Normalized Difference
[vt-floods-2023-nbr]
These images were produced from Sentinel 2a imagery with Google EarthEngine and represent normalized differences between the 10-meter green band (B3) and 20-meter SWIR2 band (B12). Clouds have been masked. The "before"...
These images were produced from Sentinel 2a imagery with Google Earth Engine and represent normalized differences between the 10-meter green band (B3) and 20-meter SWIR2 band (B12). Clouds have been masked. The "before" image is a composite marked as June 11, 2023 00:00UTC. The "after" image was captured July 11, 2023 11:38am EDT. Switching between the two time steps highlights new water in dark blue. Credit: Danielle Losos - SSEC/CIMSS University of Wisconsin-Madison
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Convective Outlook - Categorical
[SPC-ConvOutlook-CATG]
SPC Convective Outlook - Categorical
SPC Convective Outlook - Categorical
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Convective Outlook - Categorical (color map)
[SPC-ConvOutlook-CATG-cmap]
View of SPC-ConvOutlook-CATG
View of SPC-ConvOutlook-CATG
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Insolation East
[InsolationEast]
This product displays daily-integrated solar radiation estimates (MJ-m-2)for the previous calendar day derived using half-hourly imagery data from GOES-EAST and GOES-WEST geostationary satellites and a simple radiative...
This product displays daily-integrated solar radiation estimates (MJ-m-2) for the previous calendar day derived using half-hourly imagery data from GOES-EAST and GOES-WEST geostationary satellites and a simple radiative transfer model of the atmosphere. GOES-EAST data are used for the eastern half of the U.S., GOES-WEST for the western half and both these results are displayed upon initial invocation of this insolation page. Raw data values scaled by 100. Right-click on a pixel and select "probe" to view values.
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Insolation West
[Insolation]
This product displays daily-integrated solar radiation estimates (MJ-m-2)for the previous calendar day derived using half-hourly imagery data from GOES-EAST and GOES-WEST geostationary satellites and a simple radiative...
This product displays daily-integrated solar radiation estimates (MJ-m-2) for the previous calendar day derived using half-hourly imagery data from GOES-EAST and GOES-WEST geostationary satellites and a simple radiative transfer model of the atmosphere. GOES-EAST data are used for the eastern half of the U.S., GOES-WEST for the western half and both these results are displayed upon initial invocation of this insolation page. Raw data values scaled by 100. Right-click on a pixel and select "probe" to view values.
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US Landsat Analysis Ready Data (ARD) Grids
[usgs-ard-grid]
This product shows the Analysis Ready Data (ARD) grids for Landsatsatellite data. It includes all three grids for the contiguous United States (CONUS), Alaska, and Hawaii. Landsat data have been produced,...
This product shows the Analysis Ready Data (ARD) grids for Landsat satellite data. It includes all three grids for the contiguous United States (CONUS), Alaska, and Hawaii. Landsat data have been produced, archived, and distributed by the U.S. Geological Survey (USGS) since 1972. Users rely upon these data for conducting historical studies of land surface change, but they have shouldered the burden of post-production processing to create application-ready datasets. To alleviate this burden on the user, the USGS has initiated an effort to produce a collection of Landsat Science Products to support land surface change studies. The effort involves re-gridding Landsat imagery in regular 150km x 150km squares using an Albers Equal Area projection.
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Current Fire Incidents: lightning dashboards
[CURRENTNIFC]
LightningCast and GLM meteograms for current fire incidents.
LightningCast and GLM meteograms for current fire incidents.
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G16-GLM-FOV
[G16-GLM-FOV]
GOES-East Geostationary Lightning Mapper (GLM) field-of-view
GOES-East Geostationary Lightning Mapper (GLM) field-of-view
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G17-GLM-FOV
[G17-GLM-FOV]
GOES-West Geostationary Lightning Mapper (GLM) field-of-view
GOES-West Geostationary Lightning Mapper (GLM) field-of-view
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GOES-18 LightningCast parallax-corrected Full Disk
[G18-LC-plax-corr-RadF]
GOES-18 LightningCast parallax-corrected Full Disk
GOES-18 LightningCast parallax-corrected Full Disk
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GOES-East GLM MFA CONUS
[GOESEastGLMMFARadC]
GOES-East minimum flash density
GOES-East minimum flash density
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GOES-East GLM TOE CONUS
[GOESEastGLMTOERadC]
GOES-East total optical energy, in femto Joules (fJ).
GOES-East total optical energy, in femto Joules (fJ).
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LightningCast 10fl-60min GOES-East CONUS
[PLTG-10fl-60min-GOESEastRadC]
P(10fl/5min in next 60min)
P(10fl/5min in next 60min)
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LightningCast GOES-East RadM2 Gridded
[PLTGGOESEastRadM2Gridded]
An AI model that predicts the probability of lightning in the next 60minutes using GOES-R ABI data.
An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.
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LightningCast Himawari Guam
[PLTGAHIJAPANFLDKGUAM]
An AI model that predicts the probability of lightning in the next 60minutes using Himawari AHI data.
An AI model that predicts the probability of lightning in the next 60 minutes using Himawari AHI data.
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RAP North America Vertically Integrated Smoke
[RAP-smoke-column]
The Rapid Refresh (RAP) is the continental-scale NOAA hourly-updatedassimilation/modeling system operational at NCEP. RAP covers North America and is comprised primarily of a numerical forecast model and an...
The Rapid Refresh (RAP) is the continental-scale NOAA hourly-updated assimilation/modeling system operational at NCEP. RAP covers North America and is comprised primarily of a numerical forecast model and an analysis/assimilation system to initialize that model. The RAP has a resolution of 13.5km and includes smoke forecast variables derived in part from VIIRS satellite inputs. RAP is complemented by the higher-resolution 3km High-Resolution Rapid Refresh (HRRR) model, which is also updated hourly and covering a smaller geographic domain.
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SEDAC Global Population Count
[gpw-v4-population-count]
The The Gridded Population of the World, Version 4 (GPWv4): PopulationCount consists of estimates of human population, consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015,...
The The Gridded Population of the World, Version 4 (GPWv4): Population Count consists of estimates of human population, consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 12.5 million national and sub-national administrative units, is used to assign population values to 30 arc-second (~1 km) grid cells. The population count grids contain estimates of the number of persons per grid cell.
Citation: Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Count. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4X63JVC
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SEDAC Global Population Density
[gpw-v4-population-density]
The Gridded Population of the World, Version 4 (GPWv4): Population Densityconsists of estimates of human population density based on counts consistent with national censuses and population registers, for the years...
The Gridded Population of the World, Version 4 (GPWv4): Population Density consists of estimates of human population density based on counts consistent with national censuses and population registers, for the years 2000, 2005, 2010, 2015, and 2020. A proportional allocation gridding algorithm, utilizing approximately 12.5 million national and sub-national administrative units, is used to assign population values to 30 arc-second (~1 km) grid cells. The population density grids are created by dividing the population count grids by the land area grids. The pixel values represent persons per square kilometer.
Citation: Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4NP22DQ
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Historic Fire Scars (MTBS)
[historic-fire-scars-conus]
These data come from the interagency MTBS (Monitoring Trends in BurnSeverity) program through their direct download service.
These data come from the interagency MTBS (Monitoring Trends in Burn Severity) program through their direct download service.
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Gridded NUCAPS Ozone Anomaly
[nucaps-grid-o3-anomaly]
Ozone Anomaly is an identifier of stratospheric air compared to totalcolumn ozone. This SPoRT developed product is displayed in percent of normal from 0-200%. Shades of blue or values >125% indicate stratospheric...
Ozone Anomaly is an identifier of stratospheric air compared to total column ozone. This SPoRT developed product is displayed in percent of normal from 0-200%. Shades of blue or values >125% indicate stratospheric air, and the ozone values are anomalous for the month and latitude.
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Gridded NUCAPS Total Ozone
[nucaps-grid-o3-total]
High ozone is a tracer for stratospheric air and tropopause folding whichcan dynamically affect cyclogenesis or hurricane tropical to extratropical transition, or be a driver for non-convective high wind events in the...
High ozone is a tracer for stratospheric air and tropopause folding which can dynamically affect cyclogenesis or hurricane tropical to extratropical transition, or be a driver for non-convective high wind events in the western U.S. A general rule to identify stratospheric air is 300 Dobson Units (DU). Values range from 100-600 DU.
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Himawari-8 - RGB
[H08-RGB341-precip]
Daytime: Bands 3,4,1
Night: Band 13
Daytime: Bands 3,4,1
Night: Band 13
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Gridded NUCAPS Precipitable Water - High
[nucaps-grid-pw-high]
Precipitable Water represents the water vapor content contained in avertical column of unit cross-sectional area extending between any two specified levels or the total atmospheric column. It is expressed in terms...
Precipitable Water represents the water vapor content contained in a vertical column of unit cross-sectional area extending between any two specified levels or the total atmospheric column. It is expressed in terms of the height the water would stand (in millimeters) if completely condensed into the same unit area. Precipitable Water can be used to assess atmospheric moisture content. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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Gridded NUCAPS Precipitable Water - Low
[nucaps-grid-pw-low]
Precipitable Water represents the water vapor content contained in avertical column of unit cross-sectional area extending between any two specified levels or the total atmospheric column. It is expressed in terms...
Precipitable Water represents the water vapor content contained in a vertical column of unit cross-sectional area extending between any two specified levels or the total atmospheric column. It is expressed in terms of the height the water would stand (in millimeters) if completely condensed into the same unit area. Precipitable Water can be used to assess atmospheric moisture content. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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Gridded NUCAPS Precipitable Water - Mid
[nucaps-grid-pw-mid]
Precipitable Water represents the water vapor content contained in avertical column of unit cross-sectional area extending between any two specified levels or the total atmospheric column. It is expressed in terms...
Precipitable Water represents the water vapor content contained in a vertical column of unit cross-sectional area extending between any two specified levels or the total atmospheric column. It is expressed in terms of the height the water would stand (in millimeters) if completely condensed into the same unit area. Precipitable Water can be used to assess atmospheric moisture content. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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Gridded NUCAPS Precipitable Water - Top
[nucaps-grid-pw-top]
Precipitable Water represents the water vapor content contained in avertical column of unit cross-sectional area extending between any two specified levels or the total atmospheric column. It is expressed in terms...
Precipitable Water represents the water vapor content contained in a vertical column of unit cross-sectional area extending between any two specified levels or the total atmospheric column. It is expressed in terms of the height the water would stand (in millimeters) if completely condensed into the same unit area. Precipitable Water can be used to assess atmospheric moisture content. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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Gridded NUCAPS Precipitable Water - Total
[nucaps-grid-pw-total]
Precipitable Water represents the water vapor content contained in avertical column of unit cross-sectional area extending between any two specified levels or the total atmospheric column. It is expressed in terms...
Precipitable Water represents the water vapor content contained in a vertical column of unit cross-sectional area extending between any two specified levels or the total atmospheric column. It is expressed in terms of the height the water would stand (in millimeters) if completely condensed into the same unit area. Precipitable Water can be used to assess atmospheric moisture content. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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Hydro Estimator Rainfall
[NESDIS-GHE-HourlyRainfall]
The HE algorithm uses infrared (IR) brightness temperatures to identifyregions of rainfall and retrieve rainfall rate, while using National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS)...
The HE algorithm uses infrared (IR) brightness temperatures to identify regions of rainfall and retrieve rainfall rate, while using National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model fields to account for the effects of moisture availability, evaporation, orographic modulation, and thermodynamic profile effects. Estimates of rainfall from satellites can provide critical rainfall information in regions where data from gauges or radar are unavailable or unreliable, such as over oceans or sparsely populated regions.
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Snow Fall Rate
[NESDIS-SnowFallRate]
AMSU Snow Fall Rate Global by NOAA-NESDIS
AMSU Snow Fall Rate Global by NOAA-NESDIS
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Gridded NUCAPS Quality Flag
[nucaps-grid-qc]
A plan view display of the quality control fields where green indicatessuccessful infrared and microwave retrieval, yellow indicates the infrared retrieval failed but the microwave retrieval was successful, and red...
A plan view display of the quality control fields where green indicates successful infrared and microwave retrieval, yellow indicates the infrared retrieval failed but the microwave retrieval was successful, and red indicates both the infrared and microwave retrievals failed. There are cases where the yellow colored soundings are still usable and is not necessarily an indication of moderate quality.
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HRRR ConUS Latest Simulated Radar
[HRR-CONUS-RADAR-LATEST]
View of HRR-CONUS-PCP-SFC
View of HRR-CONUS-PCP-SFC
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RAP ConUS Latest Simulated Radar
[RAP-CONUS-PRAT-SFC-DBZ]
View of RAP-CONUS-PRAT-SFC
View of RAP-CONUS-PRAT-SFC
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Storm Cell ID and Tracking - Filter 1
[SCIT-ALL]
Storm Cell Identification and Tracking (SCIT)
Filters
1| ALLCells 2| Moderate Threat level Cells 3| Severe Threat level Cells
Storm Cell Identification and Tracking (SCIT)
Filters
1| ALL Cells
2| Moderate Threat level Cells
3| Severe Threat level Cells
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Storm Cell ID and Tracking - Filter 2
[SCIT-MOD]
Storm Cell Identification and Tracking (SCIT)
Filters
1| ALLCells 2| Moderate Threat level Cells 3| Severe Threat level Cells
Storm Cell Identification and Tracking (SCIT)
Filters
1| ALL Cells
2| Moderate Threat level Cells
3| Severe Threat level Cells
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Storm Cell ID and Tracking - Filter 3
[SCIT-SEV]
Storm Cell Identification and Tracking (SCIT)
Filters
1| ALLCells 2| Moderate Threat level Cells 3| Severe Threat level Cells
Storm Cell Identification and Tracking (SCIT)
Filters
1| ALL Cells
2| Moderate Threat level Cells
3| Severe Threat level Cells
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Storm Cell ID and Tracking - Forecast 2
[SCIT-MOD-FCST]
Storm Cell Identification and Tracking (SCIT)
Filter2 - 15min ForecastTrajectories 1| ALL Cells 2| Moderate Threat level Cells 3| Severe Threat level Cells
Storm Cell Identification and Tracking (SCIT)
Filter2 - 15min Forecast Trajectories
1| ALL Cells
2| Moderate Threat level Cells
3| Severe Threat level Cells
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MRMS MergedReflectivity
[MERGEDREF]
Multi-Radar/Multi-Sensor MergedReflectivityQCComposite
Multi-Radar/Multi-Sensor MergedReflectivityQCComposite
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ProbSevere (version 3)
[PROBSEVEREV3]
PSv3 models use a machine-learning model called gradient-boosted decisiontrees.
PSv3 models use a machine-learning model called gradient-boosted decision trees.
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National Reflectivity MRMS Composite
[nexrcomp]
The Multi-Radar Multi-Sensor (MRMS) system is now operational at theNational Centers for Environmental Prediction (NCEP). The MRMS system consists of the Warning Decision Support System–Integrated Information...
The Multi-Radar Multi-Sensor (MRMS) system is now operational at the National Centers for Environmental Prediction (NCEP). The MRMS system consists of the Warning Decision Support System–Integrated Information suite of severe weather and aviation products and the quantitative precipitation estimation (QPE) products created by the National Mosaic and Multi-Sensor QPE system. The MRMS system provides operational guidance for severe convective weather, QPE, and aviation hazards on a seamless three-dimensional grid that is created at a spatial resolution of 0.01° latitude × 0.01° longitude, with 33 vertical levels, every 2 min over the conterminous United States (CONUS) and southern Canada.
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National Reflectivity MRMS Composite mask
[nexrrain]
The Multi-Radar Multi-Sensor (MRMS) system is now operational at theNational Centers for Environmental Prediction (NCEP). The MRMS system consists of the Warning Decision Support System–Integrated Information...
The Multi-Radar Multi-Sensor (MRMS) system is now operational at the National Centers for Environmental Prediction (NCEP). The MRMS system consists of the Warning Decision Support System–Integrated Information suite of severe weather and aviation products and the quantitative precipitation estimation (QPE) products created by the National Mosaic and Multi-Sensor QPE system. The MRMS system provides operational guidance for severe convective weather, QPE, and aviation hazards on a seamless three-dimensional grid that is created at a spatial resolution of 0.01° latitude × 0.01° longitude, with 33 vertical levels, every 2 min over the conterminous United States (CONUS) and southern Canada.
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NEXRAD Alaska Base Reflectivity
[NEXRAD-Alaska]
WSR 88D NEXRAD Radar Mosiac Base Reflectivity Tilt 1 for Alaska Region.Bethel (KABC) Sitka (KACG) Nome (KAEC) Anchorage (KANG) Middleton Island (KAIH) King Salmon (KAKC) Fairbanks (KAPD)
WSR 88D NEXRAD Radar Mosiac Base Reflectivity Tilt 1 for Alaska Region.
Bethel (KABC)
Sitka (KACG)
Nome (KAEC)
Anchorage (KANG)
Middleton Island (KAIH)
King Salmon (KAKC)
Fairbanks (KAPD)
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NEXRAD CanAm Precipitation Phase
[nexrphase]
NEXRAD CanAm Precipitation Phase
NEXRAD CanAm Precipitation Phase
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NEXRAD ConUS 1hr Precipitation Total
[nexr1hpcp]
One-Hour Precipitation (N1P/78)
This product displays estimated one-hourprecipitation accumulation on a 1.1-nm x 1-degree grid using the Precipitation Processing System (PPS) algorithm. This product assesses...
One-Hour Precipitation (N1P/78)
This product displays estimated one-hour precipitation accumulation on a 1.1-nm x 1-degree grid using the Precipitation Processing System (PPS) algorithm. This product assesses rainfall intensities for flash flood warnings, urban flood statements, and special weather statements.
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NEXRAD ConUS Digital Integrated Liquid
[nexrdvl]
This product color codes and plots the water content of a 2.2 x 2.2nautical mile (nm) column of air. It is an effective hail indicator that can be used to locate most significant storms and identify areas of heavy...
This product color codes and plots the water content of a 2.2 x 2.2 nautical mile (nm) column of air. It is an effective hail indicator that can be used to locate most significant storms and identify areas of heavy rainfall. The DVL version of the product provides a higher spatial resolution and enhanced processing.
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NEXRAD ConUS Enhanced Echo Tops
[nexreet]
This product generates a color coded image that shows the height of an echotop. Scientists use this product to quickly estimate the most intense convection and higher echo tops, as an aid to identify storm structure...
This product generates a color coded image that shows the height of an echo top. Scientists use this product to quickly estimate the most intense convection and higher echo tops, as an aid to identify storm structure features, and for pilot briefing purposes. The EET version of the product provided a higher spatial resolution, and enhanced processing, including identification of weather that is higher than the radar can scan.
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NEXRAD ConUS Hybrid Hydrometeor Class
[nexrhhc]
Hydrometeor Classification is a computer algorithm output that tries toclassify targets in the radar volume. The product compares targets to a set of predefined categories, and displays a list of the most likely echo...
Hydrometeor Classification is a computer algorithm output that tries to classify targets in the radar volume. The product compares targets to a set of predefined categories, and displays a list of the most likely echo sources.
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NEXRAD ConUS Hybrid Reflectivity
[nexrdhr]
The same as N*R products, except data values are actual reflectivity valuesinstead of categories, data extends to further range, and additional elevations are available. Products from elevation angles at or below 3.5...
The same as N*R products, except data values are actual reflectivity values instead of categories, data extends to further range, and additional elevations are available. Products from elevation angles at or below 3.5 degrees are available, and select sites may also scan at an additional low elevation angle, as low as -0.2 degrees. Specific elevation angles depend on the site and scanning mode of the Radar.
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NEXRAD ConUS Hybrid Reflectivity mask
[nexrhres]
Values are actual reflectivity values instead of categories, data extendsto further range, and additional elevations are available. Products from elevation angles at or below 3.5 degrees are available, and select sites...
Values are actual reflectivity values instead of categories, data extends to further range, and additional elevations are available. Products from elevation angles at or below 3.5 degrees are available, and select sites may also scan at an additional low elevation angle, as low as -0.2 degrees. Specific elevation angles depend on the site and scanning mode of the Radar.
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NEXRAD ConUS Storm Total Precipitation
[nexrstorm]
Storm Total Precipitation (NTP/80)
This product uses the PPS algorithm tocreate a continuously updated estimate of a storm’s accumulated precipitation. Accumulation is tracked on a 1.1 nm x 1 degree grid....
Storm Total Precipitation (NTP/80)
This product uses the PPS algorithm to create a continuously updated estimate of a storm’s accumulated precipitation. Accumulation is tracked on a 1.1 nm x 1 degree grid. Scientists use this product to locate flood potential over urban or rural areas, estimate total basin runoff, and provide rainfall data 24 hours a day.
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NEXRAD Guam Base Reflectivity
[NEXRAD-Guam]
NEXRAD Guam Base Reflectivity
NEXRAD Guam Base Reflectivity
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NEXRAD Hawaii Base Reflectivity
[NEXRAD-Hawaii]
NEXRAD Hawaii Base Reflectivity
NEXRAD Hawaii Base Reflectivity
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NEXRAD Puerto Rico Base Reflectivity
[NEXRAD-PuertoRico]
WSR 88D NEXRAD Radar Base Reflectivity Tilt 1 for San Juan, Puerto Rico
WSR 88D NEXRAD Radar Base Reflectivity Tilt 1 for San Juan, Puerto Rico
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River Flood: Alaska
[RIVER-FLDall-AP]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Alaska region
Quick guide
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River Flood: Alaska (transparent)
[RIVER-FLDtsp-AP]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Alaska region(Transparent flood-free land)
Quick guide
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River Flood: Missouri Basin
[RIVER-FLDall-MB]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Missouri Basin
Quick guide
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River Flood: Missouri Basin (transparent)
[RIVER-FLDtsp-MB]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Missouri Basin(Transparent flood-free land)
Quick guide
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River Flood: North Central Basin
[RIVER-FLDall-NC]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North Central Basin
Quick guide
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River Flood: North Central Basin (transparent)
[RIVER-FLDtsp-NC]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North Central Basin(Transparent flood-free land)
Quick guide
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River Flood: North East Basin
[RIVER-FLDall-NE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North East Basin
Quick guide
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River Flood: North East Basin (transparent)
[RIVER-FLDtsp-NE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North East Basin(Transparent flood-free land)
Quick guide
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River Flood: North West
[RIVER-FLDall-NW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Northwest Region
Quick guide
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River Flood: North West (transparent)
[RIVER-FLDtsp-NW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Northwest Region(Transparent flood-free land)
Quick guide
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River Flood: South East
[RIVER-FLDall-SE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southeast Region
Quick guide
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River Flood: South East (transparent)
[RIVER-FLDtsp-SE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southeast Region(Transparent flood-free land)
Quick guide
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River Flood: South West
[RIVER-FLDall-SW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southwest Region
Quick guide
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River Flood: South West (tsp)
[RIVER-FLDtsp-SW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southwest Region(Transparent flood-free land)
Quick guide
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River Flood: US
[RIVER-FLDall-US]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
US
Quick guide
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River Flood: US (transparent)
[RIVER-FLDtsp-US]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
US(Transparent flood-free land)
Quick guide
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River Flood: West Gulf Basin
[RIVER-FLDall-WG]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
West Gulf Basin
Quick guide
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River Flood: West Gulf Basin (transparent)
[RIVER-FLDtsp-WG]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
West Gulf Basin(Transparent flood-free land)
Quick guide
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River Flood: Alaska
[RIVER-FLDall-AP]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Alaska region
Quick guide
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River Flood: Alaska (transparent)
[RIVER-FLDtsp-AP]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Alaska region(Transparent flood-free land)
Quick guide
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River Flood: Missouri Basin
[RIVER-FLDall-MB]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Missouri Basin
Quick guide
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River Flood: Missouri Basin (transparent)
[RIVER-FLDtsp-MB]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Missouri Basin(Transparent flood-free land)
Quick guide
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River Flood: North Central Basin
[RIVER-FLDall-NC]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North Central Basin
Quick guide
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River Flood: North Central Basin (transparent)
[RIVER-FLDtsp-NC]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North Central Basin(Transparent flood-free land)
Quick guide
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River Flood: North East Basin
[RIVER-FLDall-NE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North East Basin
Quick guide
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River Flood: North East Basin (transparent)
[RIVER-FLDtsp-NE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
North East Basin(Transparent flood-free land)
Quick guide
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River Flood: North West
[RIVER-FLDall-NW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Northwest Region
Quick guide
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River Flood: North West (transparent)
[RIVER-FLDtsp-NW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Northwest Region(Transparent flood-free land)
Quick guide
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River Flood: South East
[RIVER-FLDall-SE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southeast Region
Quick guide
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River Flood: South East (transparent)
[RIVER-FLDtsp-SE]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southeast Region(Transparent flood-free land)
Quick guide
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River Flood: South West
[RIVER-FLDall-SW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southwest Region
Quick guide
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River Flood: South West (tsp)
[RIVER-FLDtsp-SW]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
Southwest Region(Transparent flood-free land)
Quick guide
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River Flood: US
[RIVER-FLDall-US]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
US
Quick guide
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River Flood: US (transparent)
[RIVER-FLDtsp-US]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
US(Transparent flood-free land)
Quick guide
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River Flood: West Gulf Basin
[RIVER-FLDall-WG]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
West Gulf Basin
Quick guide
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River Flood: West Gulf Basin (transparent)
[RIVER-FLDtsp-WG]
CIMSS hosts a flood product developed at George Mason University (GMU)derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are...
CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations.
West Gulf Basin(Transparent flood-free land)
Quick guide
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Cloud Top Cooling targets
[CIMSS-CTCtargets]
CIMSS-Cloud Top Cooling targets
CIMSS-Cloud Top Cooling targets
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Convective Outlook Day1
[SPCcoday1]
Convective Outlook Day1 (Category)
id=SPCcoday1
Convective Outlook Day1 (Category)
id=SPCcoday1
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Convective Outlook Day2
[SPCcoday2]
Convective Outlook Day2 (Category)
Convective Outlook Day2 (Category)
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Convective Outlook Day3
[SPCcoday3]
Convective Outlook Day3 (Categorical)
Convective Outlook Day3 (Categorical)
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Fire Weather Outlook Day1
[SPCfwday1]
Fire Weather Outlook Day1 (Category)
Fire Weather Outlook Day1 (Category)
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Fire Weather Outlook Day2
[SPCfwday2]
Fire Weather Outlook Day2 (Category)
Fire Weather Outlook Day2 (Category)
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Overshooting Tops targets
[CIMSS-OTtargets]
Cloud OverShooting Tops targets
Cloud OverShooting Tops targets
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Severe Weather Warning Outlines
[SevereOutl]
Tornado, Thunderstorm, Flash Flood and Marine Warnings (outlines only, nofill)
Tornado, Thunderstorm, Flash Flood and Marine Warnings (outlines only, no fill)
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Severe Weather Warnings
[Severe]
Tornado, Thunderstorm, Flash Flood and Marine Warning polygons.
Tornado, Thunderstorm, Flash Flood and Marine Warning polygons.
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Severe Weather Warning Vectors
[SevereVect]
Tornado and Thunderstorm Warning Vectors
Tornado and Thunderstorm Warning Vectors
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Severe Weather Watch Box
[SAW]
Severe Weather Watch Box - Aviation
Severe Weather Watch Box - Aviation
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Thunderstorm Watches/Warnings
[WWSEVTRW]
Thunderstorm Watches and Warnings
Thunderstorm Watches and Warnings
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IntenseStormNet -- GOES-East CONUS
[ICP]
Deep learning model that predicts where "intense" convection" is present,based on features that humans associate with intense convection.
Deep learning model that predicts where "intense" convection" is present, based on features that humans associate with intense convection.
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IntenseStormNet -- GOES-East MESO1
[ICPRadM1]
IntenseStormNet -- GOES East Mesoscale 1
IntenseStormNet -- GOES East Mesoscale 1
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IntenseStormNet -- GOES-East MESO2
[ICPRadM2]
IntenseStormNet -- GOES East Mesoscale 2
IntenseStormNet -- GOES East Mesoscale 2
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MRMS MergedReflectivity
[MERGEDREF]
Multi-Radar/Multi-Sensor MergedReflectivityQCComposite
Multi-Radar/Multi-Sensor MergedReflectivityQCComposite
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NWSWARNS12Z12Z
[NWSWARNS12Z12Z]
NWSWARNS12Z12Z (Severe and Tornado. No SVSs)
NWSWARNS12Z12Z (Severe and Tornado. No SVSs)
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ProbSevere (version2)
[PROBSEVERE]
The probability of any severe is the max(ProbHail,ProbWind,ProbTor).
The probability of any severe is the max(ProbHail,ProbWind,ProbTor).
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ProbSevere Accumulation 20% to 49%
[PROBSEVACCUMLOW]
ProbSevere Accumulation 20% to 49%
ProbSevere Accumulation 20% to 49%
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Gridded NUCAPS Total Column Sulfur Dioxide (SO2)
[nucaps-grid-so2-total]
Total Column Sulfur Dioxide represents the amount of sulfur dioxide in theatmospheric column in Dobson Units. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a...
Total Column Sulfur Dioxide represents the amount of sulfur dioxide in the atmospheric column in Dobson Units. Gridded NUCAPS was developed by NASA SPoRT (Short-term Prediction Research and Transition Center) through a collaborative effort with the JPSS Proving Ground and Risk Reduction Program involving NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS. See Berndt et al. 2020
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Sea Surface Temperature
[NESDIS-SST]
NESDIS: Hi-Res Sea Surface Temperature
NESDIS: Hi-Res Sea Surface Temperature
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Cloud Top Cooling targets
[CIMSS-CTCtargets]
CIMSS-Cloud Top Cooling targets
CIMSS-Cloud Top Cooling targets
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Earthquake Magnitude
[Earthquake-mag]
Earthquake Magnitude (Past 24hr)
Earthquake Magnitude (Past 24hr)
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Overshooting Tops targets
[CIMSS-OTtargets]
Cloud OverShooting Tops targets
Cloud OverShooting Tops targets
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Volcanic Ash Adv plumes
[VAA]
Volcanic Ash Advisories: Ash Clouds
Volcanic Ash Advisories: Ash Clouds
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Gridded NUCAPS Temperature 2m
[nucaps-grid-temp-2m]
Temperature at 2 meters above ground. Gridded NUCAPS from NASA-SPoRT(Short-term Prediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and...
Temperature at 2 meters above ground. Gridded NUCAPS from NASA-SPoRT (Short-term Prediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS to provide plan-view and cross-section displays of CrIS/ATMS temperature and moisture soundings in AWIPS (i.e. Gridded NUCAPS). Gridded NUCAPS was originally developed to diagnose Cold Air Aloft (CAA) but can also be used to diagnose the pre-convective environment. Gridded NUCAPS has been evaluated at the Anchorage CWSU for the CAA forecasting challenge and at the Hazardous Weather Testbed for assessing the pre-convective environment. As part of the JPSS NUCAPS Initiative, the team of collaborators is exploring new applications for Gridded NUCAPS and working with developers to baseline the product in AWIPS, McIDAS-V, and RealEarth.
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Gridded NUCAPS Temperature 300mb
[nucaps-grid-temp-300]
Temperature at 300mb. Gridded NUCAPS from NASA-SPoRT (Short-termPrediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS...
Temperature at 300mb. Gridded NUCAPS from NASA-SPoRT (Short-term Prediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS to provide plan-view and cross-section displays of CrIS/ATMS temperature and moisture soundings in AWIPS (i.e. Gridded NUCAPS). Gridded NUCAPS was originally developed to diagnose Cold Air Aloft (CAA) but can also be used to diagnose the pre-convective environment. Gridded NUCAPS has been evaluated at the Anchorage CWSU for the CAA forecasting challenge and at the Hazardous Weather Testbed for assessing the pre-convective environment. As part of the JPSS NUCAPS Initiative, the team of collaborators is exploring new applications for Gridded NUCAPS and working with developers to baseline the product in AWIPS, McIDAS-V, and RealEarth.
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Gridded NUCAPS Temperature 500mb
[nucaps-grid-temp-500]
Temperature at 500mb. Gridded NUCAPS from NASA-SPoRT (Short-termPrediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS...
Temperature at 500mb. Gridded NUCAPS from NASA-SPoRT (Short-term Prediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS to provide plan-view and cross-section displays of CrIS/ATMS temperature and moisture soundings in AWIPS (i.e. Gridded NUCAPS). Gridded NUCAPS was originally developed to diagnose Cold Air Aloft (CAA) but can also be used to diagnose the pre-convective environment. Gridded NUCAPS has been evaluated at the Anchorage CWSU for the CAA forecasting challenge and at the Hazardous Weather Testbed for assessing the pre-convective environment. As part of the JPSS NUCAPS Initiative, the team of collaborators is exploring new applications for Gridded NUCAPS and working with developers to baseline the product in AWIPS, McIDAS-V, and RealEarth.
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Gridded NUCAPS Temperature 700mb
[nucaps-grid-temp-700]
Temperature at 700mb. Gridded NUCAPS from NASA-SPoRT (Short-termPrediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS...
Temperature at 700mb. Gridded NUCAPS from NASA-SPoRT (Short-term Prediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS to provide plan-view and cross-section displays of CrIS/ATMS temperature and moisture soundings in AWIPS (i.e. Gridded NUCAPS). Gridded NUCAPS was originally developed to diagnose Cold Air Aloft (CAA) but can also be used to diagnose the pre-convective environment. Gridded NUCAPS has been evaluated at the Anchorage CWSU for the CAA forecasting challenge and at the Hazardous Weather Testbed for assessing the pre-convective environment. As part of the JPSS NUCAPS Initiative, the team of collaborators is exploring new applications for Gridded NUCAPS and working with developers to baseline the product in AWIPS, McIDAS-V, and RealEarth.
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Gridded NUCAPS Temperature 850mb
[nucaps-grid-temp-850]
Temperature at 850mb. Gridded NUCAPS from NASA-SPoRT (Short-termPrediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS...
Temperature at 850mb. Gridded NUCAPS from NASA-SPoRT (Short-term Prediction Research and Transition Center). SPoRT has been part of a collaborative effort with JPSS, NOAA NWS, STC, CIRA, GINA, and SSEC/CIMSS to provide plan-view and cross-section displays of CrIS/ATMS temperature and moisture soundings in AWIPS (i.e. Gridded NUCAPS). Gridded NUCAPS was originally developed to diagnose Cold Air Aloft (CAA) but can also be used to diagnose the pre-convective environment. Gridded NUCAPS has been evaluated at the Anchorage CWSU for the CAA forecasting challenge and at the Hazardous Weather Testbed for assessing the pre-convective environment. As part of the JPSS NUCAPS Initiative, the team of collaborators is exploring new applications for Gridded NUCAPS and working with developers to baseline the product in AWIPS, McIDAS-V, and RealEarth.
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Tropical Storm & Hurricane Forecast
[TSFCST]
National Hurricane Center Tropical Storm & Hurricane Forecast
National Hurricane Center Tropical Storm & Hurricane Forecast
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Gridded NUCAPS Tropopause Height
[nucaps-grid-trop-level]
The Tropopause Level product was developed to assess the depth of thestratospheric intrusion or tropopause fold to anticipate rapid cyclogenesis or hurricane tropical to extratropical transition. The seasonal variation...
The Tropopause Level product was developed to assess the depth of the stratospheric intrusion or tropopause fold to anticipate rapid cyclogenesis or hurricane tropical to extratropical transition. The seasonal variation of ozone at the dynamic tropopause (2 PVU) by Thouret et al. (2006) is used to identify the tropopause height in mb by matching the level where the ozone value is greater than or equal to the Thouret et al. (2006) value.
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SNPP VIIRS DNB Adaptive DB ConUS
[npp-viirs-adaptive-dnb-msn]
npp-viirs-adaptive-dnb-msn
npp-viirs-adaptive-dnb-msn
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SNPP VIIRS False Color (Daily) Global
[npp-viirs-false-color-daily]
View of npp-viirs-false-color-swath
View of npp-viirs-false-color-swath
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SNPP VIIRS False Color (Swaths) Global
[npp-viirs-false-color-swath]
View of npp-viirs-false-color
View of npp-viirs-false-color
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SNPP VIIRS Fire RGB (Swaths) Global
[npp-viirs-swath-fire-color]
View of npp-viirs-bands-day-swath
View of npp-viirs-bands-day-swath
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SNPP VIIRS Fire Temp (Swaths) Global
[npp-viirs-swath-fire-temp]
View of npp-viirs-bands-day-swath
View of npp-viirs-bands-day-swath
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SNPP VIIRS True Color (Daily) Global
[npp-viirs-true-color-daily]
View of npp-viirs-true-color-swath
View of npp-viirs-true-color-swath
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SNPP VIIRS True Color (Swaths) Global
[npp-viirs-true-color-swath]
View of npp-viirs-true-color
View of npp-viirs-true-color
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NOAA20 VIIRS DNB (Swaths) Global
[j01-viirs-dnb-swath]
View of j01-viirs-bands-night-swath
View of j01-viirs-bands-night-swath
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NOAA20 VIIRS False Color (Daily) Global
[j01-viirs-false-color-daily]
View of j01-viirs-false-color-swath
View of j01-viirs-false-color-swath
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NOAA20 VIIRS False Color (Swaths) Global
[j01-viirs-false-color-swath]
View of j01-viirs-false-color
View of j01-viirs-false-color
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NOAA20 VIIRS M-Band Fire RGB (Swaths) Global
[j01-viirs-swath-fire-color]
This image is made by on-the-fly combining VIIRS bands M11 (2.25um) as red,M7 (8.66um) as green, and M4 (5.55) as blue. Because the M11 shortwave infrared band is sensitive to bright fires, it highlights active especially...
This image is made by on-the-fly combining VIIRS bands M11 (2.25um) as red, M7 (8.66um) as green, and M4 (5.55) as blue. Because the M11 shortwave infrared band is sensitive to bright fires, it highlights active especially hot fires in red while preserving a natural color appearance in the rest of the image.
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NOAA20 VIIRS M-Band Fire Temp (Swaths) Global
[j01-viirs-swath-fire-temp]
On-the-fly combination of bands 11, 10, 12.
On-the-fly combination of bands 11, 10, 12.
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NOAA20 VIIRS True Color (Daily) Global
[j01-viirs-true-color-daily]
View of j01-viirs-true-color-swath
View of j01-viirs-true-color-swath
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NOAA20 VIIRS True Color (Swaths) Global
[j01-viirs-true-color-swath]
View of j01-viirs-true-color
View of j01-viirs-true-color
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SNPP Day/Night AM Composite - Adaptive
[nppadpam]
NPP Day/Night AM Composite - Adaptive
NPP Day/Night AM Composite - Adaptive
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SNPP Day/Night Band (DNB) - Honolulu DB
[nppdnbdyn-hnl]
NPP Day/Night Band (DNB) - Honolulu DB
NPP Day/Night Band (DNB) - Honolulu DB
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SNPP VIIRS True Color DB Hawaii
[npptc-hnl]
NPP True Color (TC) - Honolulu DB
NPP True Color (TC) - Honolulu DB
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SNPP VIIRS True Color DB Puerto Rico
[npptc-upr]
NPP True Color (TC) - Puerto Rico DB
NPP True Color (TC) - Puerto Rico DB
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VIIRS NDVI 16-day Composite DB ConUS
[NDVI-16day-before]
This CONUS NDVI product is clipped from the global VIIRS composite productVNP13A1-001 using AppEEARS at the NASA LPDAAC. The spatial resolution is 500m. It is made with in alternating 8-day cycles from best available...
This CONUS NDVI product is clipped from the global VIIRS composite product VNP13A1-001 using AppEEARS at the NASA LPDAAC. The spatial resolution is 500m. It is made with in alternating 8-day cycles from best available pixels. See link below for more information.
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NOAA20 VIIRS Sea Ice Concentration Global
[j01-sic]
The Sea Ice Concentration products uses threshold reflectance(temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km...
The Sea Ice Concentration products uses threshold reflectance (temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km resolution covered by ice. The product is available over oceans, seas and lakes only under clear-sky conditions that is determined by VIIRS cloud mask.
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SNPP VIIRS SEA Ice Concentration Global
[snpp-sic]
The Sea Ice Concentration products uses threshold reflectance(temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km...
The Sea Ice Concentration products uses threshold reflectance (temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km resolution covered by ice. The product is available over oceans, seas and lakes only under clear-sky conditions that is determined by VIIRS cloud mask.
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EOG Night Lights (J01)
[EOG-NightLights-j01]
Earth Observation Group VIIRS Nighttime Lights (J01)
Earth Observation Group VIIRS Nighttime Lights (J01)
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EOG Night Lights (NPP)
[EOG-NightLights-npp]
Earth Observation Group VIIRS Nighttime Lights (NPP)
Earth Observation Group VIIRS Nighttime Lights (NPP)
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SNPP VIIRS DNB Adaptive DB ConUS
[npp-viirs-adaptive-dnb-msn]
npp-viirs-adaptive-dnb-msn
npp-viirs-adaptive-dnb-msn
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SNPP VIIRS False Color (Daily) Global
[npp-viirs-false-color-daily]
View of npp-viirs-false-color-swath
View of npp-viirs-false-color-swath
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SNPP VIIRS False Color (Swaths) Global
[npp-viirs-false-color-swath]
View of npp-viirs-false-color
View of npp-viirs-false-color
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SNPP VIIRS Fire RGB (Swaths) Global
[npp-viirs-swath-fire-color]
View of npp-viirs-bands-day-swath
View of npp-viirs-bands-day-swath
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SNPP VIIRS Fire Temp (Swaths) Global
[npp-viirs-swath-fire-temp]
View of npp-viirs-bands-day-swath
View of npp-viirs-bands-day-swath
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SNPP VIIRS True Color (Daily) Global
[npp-viirs-true-color-daily]
View of npp-viirs-true-color-swath
View of npp-viirs-true-color-swath
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SNPP VIIRS True Color (Swaths) Global
[npp-viirs-true-color-swath]
View of npp-viirs-true-color
View of npp-viirs-true-color
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NOAA20 VIIRS DNB (Swaths) Global
[j01-viirs-dnb-swath]
View of j01-viirs-bands-night-swath
View of j01-viirs-bands-night-swath
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NOAA20 VIIRS False Color (Daily) Global
[j01-viirs-false-color-daily]
View of j01-viirs-false-color-swath
View of j01-viirs-false-color-swath
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NOAA20 VIIRS False Color (Swaths) Global
[j01-viirs-false-color-swath]
View of j01-viirs-false-color
View of j01-viirs-false-color
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NOAA20 VIIRS M-Band Fire RGB (Swaths) Global
[j01-viirs-swath-fire-color]
This image is made by on-the-fly combining VIIRS bands M11 (2.25um) as red,M7 (8.66um) as green, and M4 (5.55) as blue. Because the M11 shortwave infrared band is sensitive to bright fires, it highlights active especially...
This image is made by on-the-fly combining VIIRS bands M11 (2.25um) as red, M7 (8.66um) as green, and M4 (5.55) as blue. Because the M11 shortwave infrared band is sensitive to bright fires, it highlights active especially hot fires in red while preserving a natural color appearance in the rest of the image.
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NOAA20 VIIRS M-Band Fire Temp (Swaths) Global
[j01-viirs-swath-fire-temp]
On-the-fly combination of bands 11, 10, 12.
On-the-fly combination of bands 11, 10, 12.
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NOAA20 VIIRS True Color (Daily) Global
[j01-viirs-true-color-daily]
View of j01-viirs-true-color-swath
View of j01-viirs-true-color-swath
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NOAA20 VIIRS True Color (Swaths) Global
[j01-viirs-true-color-swath]
View of j01-viirs-true-color
View of j01-viirs-true-color
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NOAA20 VIIRS Sea Ice Concentration Global
[j01-sic]
The Sea Ice Concentration products uses threshold reflectance(temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km...
The Sea Ice Concentration products uses threshold reflectance (temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km resolution covered by ice. The product is available over oceans, seas and lakes only under clear-sky conditions that is determined by VIIRS cloud mask.
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NOAA20 VIIRS Sea Ice Temperature Global
[j01-ist]
The Sea Ice Temperature product uses a split window algorithm that isdependent on bands M15 (~11 um) and M16 (~12 um) along with satellite scan angle to come up with an atmospheric correction term that adjusts clear...
The Sea Ice Temperature product uses a split window algorithm that is dependent on bands M15 (~11 um) and M16 (~12 um) along with satellite scan angle to come up with an atmospheric correction term that adjusts clear window Brightness Temperature to come up with a final IST value that is at 750 m resolution and has been shown to be within 1.5 K of validation measurements. The product is available over all water bodies, including rivers under clear-sky conditions that is determined by VIIRS cloud mask.
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NOAA20 VIIRS Sea Ice Thickness Global
[j01-ithk]
The Sea Ice Thickness product uses a one-dimensional thermodynamic icemodel (OTIM) The OTIM is based on the surface energy balance but does not directly use any channel data. Instead, it takes into account variables...
The Sea Ice Thickness product uses a one-dimensional thermodynamic ice model (OTIM) The OTIM is based on the surface energy balance but does not directly use any channel data. Instead, it takes into account variables such as VIIRS ice surface temperature and the VIIRS cloud mask to determine sea and lake ice thickness. The product is at 750 m resolution and available over all water bodies, including rivers under clear sky conditions that is determined by VIIRS cloud mask.
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SNPP VIIRS False Color (Daily) Global
[npp-viirs-false-color-daily]
View of npp-viirs-false-color-swath
View of npp-viirs-false-color-swath
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SNPP VIIRS False Color (Swaths) Global
[npp-viirs-false-color-swath]
View of npp-viirs-false-color
View of npp-viirs-false-color
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SNPP VIIRS Fire RGB (Swaths) Global
[npp-viirs-swath-fire-color]
View of npp-viirs-bands-day-swath
View of npp-viirs-bands-day-swath
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SNPP VIIRS Fire Temp (Swaths) Global
[npp-viirs-swath-fire-temp]
View of npp-viirs-bands-day-swath
View of npp-viirs-bands-day-swath
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SNPP VIIRS True Color (Daily) Global
[npp-viirs-true-color-daily]
View of npp-viirs-true-color-swath
View of npp-viirs-true-color-swath
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SNPP VIIRS True Color (Swaths) Global
[npp-viirs-true-color-swath]
View of npp-viirs-true-color
View of npp-viirs-true-color
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SNPP VIIRS SEA Ice Concentration Global
[snpp-sic]
The Sea Ice Concentration products uses threshold reflectance(temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km...
The Sea Ice Concentration products uses threshold reflectance (temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km resolution covered by ice. The product is available over oceans, seas and lakes only under clear-sky conditions that is determined by VIIRS cloud mask.
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SNPP VIIRS Sea Ice Temperature Global
[snpp-ist]
The Sea Ice Temperature product uses a split window algorithm that isdependent on bands M15 (~11 um) and M16 (~12 um) along with satellite scan angle to come up with an atmospheric correction term that adjusts clear...
The Sea Ice Temperature product uses a split window algorithm that is dependent on bands M15 (~11 um) and M16 (~12 um) along with satellite scan angle to come up with an atmospheric correction term that adjusts clear window Brightness Temperature to come up with a final IST value that is at 750 m resolution and has been shown to be within 1.5 K of validation measurements. The product is available over all water bodies, including rivers under clear-sky conditions that is determined by VIIRS cloud mask.
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SNPP VIIRS Sea Ice Thickness Global
[snpp-ithk]
The Sea Ice Thickness product uses a one-dimensional thermodynamic icemodel (OTIM) The OTIM is based on the surface energy balance but does not directly use any channel data. Instead, it takes into account variables...
The Sea Ice Thickness product uses a one-dimensional thermodynamic ice model (OTIM) The OTIM is based on the surface energy balance but does not directly use any channel data. Instead, it takes into account variables such as VIIRS ice surface temperature and the VIIRS cloud mask to determine sea and lake ice thickness. The product is at 750 m resolution and available over all water bodies, including rivers under clear sky conditions that is determined by VIIRS cloud mask.
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SNPP VIIRS False Color (Daily) Global
[npp-viirs-false-color-daily]
View of npp-viirs-false-color-swath
View of npp-viirs-false-color-swath
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SNPP VIIRS False Color (Swaths) Global
[npp-viirs-false-color-swath]
View of npp-viirs-false-color
View of npp-viirs-false-color
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SNPP VIIRS Fire RGB (Swaths) Global
[npp-viirs-swath-fire-color]
View of npp-viirs-bands-day-swath
View of npp-viirs-bands-day-swath
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SNPP VIIRS Fire Temp (Swaths) Global
[npp-viirs-swath-fire-temp]
View of npp-viirs-bands-day-swath
View of npp-viirs-bands-day-swath
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SNPP VIIRS True Color (Daily) Global
[npp-viirs-true-color-daily]
View of npp-viirs-true-color-swath
View of npp-viirs-true-color-swath
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SNPP VIIRS True Color (Swaths) Global
[npp-viirs-true-color-swath]
View of npp-viirs-true-color
View of npp-viirs-true-color
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Fire Radiative Power VIIRS I-band - GINA
[AFIMG-Points-GINA]
VIIRS 375m I-band high spatial resolution imagery provides a greaterresponse over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime...
VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software at GINA.
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MIRS RainRate - Alaska (GINA)
[MIRS-RainRate-AK]
With inputs from the ATMS (Advanced Technology Microwave Sounder) sensoraboard JPSS satellites, the rainfall rate product from the Microwave Integrated Retrieval System (MIRS) identifies the intensity of rain at the...
With inputs from the ATMS (Advanced Technology Microwave Sounder) sensor aboard JPSS satellites, the rainfall rate product from the Microwave Integrated Retrieval System (MIRS) identifies the intensity of rain at the instant the satellite is passing over the area. It is derived from three vertically integrated MIRS products: Cloud Liquid Water (CLW), Rain Water Path (RWP), and Ice Water Path (IWP), taking advantage of the physical relationship found between atmospheric hydrometeor amounts and surface rain rate.
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VIIRS Aerosol Optical Depth (GINA) DB Alaska
[AOD-RGB-GINA]
Aerosol optical depth is a measure of the extinction of the solar beam bydust and haze. In other words, particles in the atmosphere (dust, smoke, pollution) can block sunlight by absorbing or by scattering light. AOD...
Aerosol optical depth is a measure of the extinction of the solar beam by dust and haze. In other words, particles in the atmosphere (dust, smoke, pollution) can block sunlight by absorbing or by scattering light. AOD tells us how much direct sunlight is prevented from reaching the ground by these aerosol particles. It is a dimensionless number that is related to the amount of aerosol in the vertical column of atmosphere over the observation location. A value of 0.01 corresponds to an extremely clean atmosphere, and a value of 0.4 would correspond to a very hazy condition. An average aerosol optical depth for the U.S. is 0.1 to 0.15.
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VIIRS Fire Radiative Power I-band DB ConUS
[AFIMG-Points]
VIIRS 375m I-band high spatial resolution imagery provides a greaterresponse over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime...
VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software.
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VIIRS Fire RGB (GINA) DB Alaska
[DayLandCloudFire-RGB-GINA]
This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87umchannel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic...
This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic Information Network of Alaska (GINA).
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VIIRS Fire Temp RGB (GINA) DB Alaska
[FireTemperature-RGB-GINA]
This RGB is created by assigning the VIIRS 3.74um channel to red, 2.25umchannel to green, and the 1.61um channel to blue. It is used to assess fire intensity and size, with fires ranging from red (lowest) to yellow to white...
This RGB is created by assigning the VIIRS 3.74um channel to red, 2.25um channel to green, and the 1.61um channel to blue. It is used to assess fire intensity and size, with fires ranging from red (lowest) to yellow to white (hottest or biggest). These data are produced by the Geographic Information Network of Alaska (GINA).
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VIIRS i04 (GINA) DB Alaska
[VIIRS-i04-GINA]
This is the VIIRS 3.74um single channel i-band with 376 m resolution. It isan IR channel that is very sensitive to fires and hot spots and is available day or night. A special colormap is used to enhance the warm-hot...
This is the VIIRS 3.74um single channel i-band with 376 m resolution. It is an IR channel that is very sensitive to fires and hot spots and is available day or night. A special colormap is used to enhance the warm-hot pixels. The sensors can become saturated by very intense fires and daytime radiance values can affected by reflected sunlight. These data are produced by the Geographic Information Network of Alaska (GINA).
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VIIRS Snowmelt (GINA) DB Alaska
[VIIRS-Snowmelt-GINA]
This RGB is created by assigning the VIIRS 1.61um channel to red, 1.24umchannel to green, and the 0.64um channel to blue. The blue shades identify snow cover characteristics. Darker blue shows wetter or older snow and...
This RGB is created by assigning the VIIRS 1.61um channel to red, 1.24um channel to green, and the 0.64um channel to blue. The blue shades identify snow cover characteristics. Darker blue shows wetter or older snow and lighter blues show drier or newer snow. These data are produced by the Geographic Information Network of Alaska (GINA).
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VIIRS True Color RGB (GINA) DB Alaska
[TrueColor-RGB-GINA]
This RGB is made from the red (0.64um), green (0.56um) and blue (0.49um)visible VIIRS channels. It produces a product that is close to what the human eye would see from space.
This RGB is made from the red (0.64um), green (0.56um) and blue (0.49um) visible VIIRS channels. It produces a product that is close to what the human eye would see from space.
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VIIRS NDVI 16-day Composite DB ConUS
[NDVI-16day-before]
This CONUS NDVI product is clipped from the global VIIRS composite productVNP13A1-001 using AppEEARS at the NASA LPDAAC. The spatial resolution is 500m. It is made with in alternating 8-day cycles from best available...
This CONUS NDVI product is clipped from the global VIIRS composite product VNP13A1-001 using AppEEARS at the NASA LPDAAC. The spatial resolution is 500m. It is made with in alternating 8-day cycles from best available pixels. See link below for more information.
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SNPP VIIRS True Color DB Hawaii
[npptc-hnl]
NPP True Color (TC) - Honolulu DB
NPP True Color (TC) - Honolulu DB
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NOAA20 VIIRS DNB Adaptive (Daily) DB ConUS
[j01-viirs-adaptive-dnb-msn-daily]
View of j01-viirs-adaptive-dnb-msn
View of j01-viirs-adaptive-dnb-msn
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NOAA20 VIIRS DNB Adaptive DB ConUS
[j01-viirs-adaptive-dnb-msn]
j01-viirs-adaptive-dnb-msn
j01-viirs-adaptive-dnb-msn
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NOAA20 VIIRS DNB Dynamic (Daily) DB ConUS
[j01-viirs-dynamic-dnb-msn-daily]
View of j01-viirs-dynamic-dnb-msn
View of j01-viirs-dynamic-dnb-msn
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NOAA20 VIIRS DNB Dynamic DB ConUS
[j01-viirs-dynamic-dnb-msn]
j01-viirs-dynamic-dnb-msn
j01-viirs-dynamic-dnb-msn
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NOAA20 VIIRS DNB Histogram (Daily) DB ConUS
[j01-viirs-hncc-dnb-msn-daily]
View of j01-viirs-hncc-dnb-msn
View of j01-viirs-hncc-dnb-msn
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NOAA20 VIIRS DNB Histogram DB ConUS
[j01-viirs-hncc-dnb-msn]
j01-viirs-hncc-dnb-msn
j01-viirs-hncc-dnb-msn
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NOAA20 VIIRS False Color (Daily) DB ConUS
[j01-viirs-fc-msn-daily]
View of j01-viirs-fc-msn
View of j01-viirs-fc-msn
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NOAA20 VIIRS Footprint DB ConUS
[j01-viirs-footprint-msn]
This product represents the area of data collected through direct broadcast(DB) during the overpass of NOAA-20 (J01) from multiple stations operated in affiliation with the Space Science and Engineering Center (SSEC) at the...
This product represents the area of data collected through direct broadcast (DB) during the overpass of NOAA-20 (J01) from multiple stations operated in affiliation with the Space Science and Engineering Center (SSEC) at the University of Wisconsin-Madison.
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NOAA20 VIIRS SST (Daily) DB ConUS
[j01-viirs-sst-msn-daily]
NOAA20 VIIRS SST (Daily) CIMSS-DB
NOAA20 VIIRS SST (Daily) CIMSS-DB
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NOAA20 VIIRS True Color (Daily) DB ConUS
[j01-viirs-tc-msn-daily]
View of j01-viirs-tc-msn
View of j01-viirs-tc-msn
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NOAA20 VIIRS DNB Adaptive (Daily) DB ConUS
[j01-viirs-adaptive-dnb-msn-daily]
View of j01-viirs-adaptive-dnb-msn
View of j01-viirs-adaptive-dnb-msn
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NOAA20 VIIRS DNB Adaptive DB ConUS
[j01-viirs-adaptive-dnb-msn]
j01-viirs-adaptive-dnb-msn
j01-viirs-adaptive-dnb-msn
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NOAA20 VIIRS DNB Dynamic (Daily) DB ConUS
[j01-viirs-dynamic-dnb-msn-daily]
View of j01-viirs-dynamic-dnb-msn
View of j01-viirs-dynamic-dnb-msn
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NOAA20 VIIRS DNB Dynamic DB ConUS
[j01-viirs-dynamic-dnb-msn]
j01-viirs-dynamic-dnb-msn
j01-viirs-dynamic-dnb-msn
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NOAA20 VIIRS DNB Histogram (Daily) DB ConUS
[j01-viirs-hncc-dnb-msn-daily]
View of j01-viirs-hncc-dnb-msn
View of j01-viirs-hncc-dnb-msn
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NOAA20 VIIRS DNB Histogram DB ConUS
[j01-viirs-hncc-dnb-msn]
j01-viirs-hncc-dnb-msn
j01-viirs-hncc-dnb-msn
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NOAA20 VIIRS False Color (Daily) DB ConUS
[j01-viirs-fc-msn-daily]
View of j01-viirs-fc-msn
View of j01-viirs-fc-msn
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NOAA20 VIIRS Footprint DB ConUS
[j01-viirs-footprint-msn]
This product represents the area of data collected through direct broadcast(DB) during the overpass of NOAA-20 (J01) from multiple stations operated in affiliation with the Space Science and Engineering Center (SSEC) at the...
This product represents the area of data collected through direct broadcast (DB) during the overpass of NOAA-20 (J01) from multiple stations operated in affiliation with the Space Science and Engineering Center (SSEC) at the University of Wisconsin-Madison.
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NOAA20 VIIRS SST (Daily) DB ConUS
[j01-viirs-sst-msn-daily]
NOAA20 VIIRS SST (Daily) CIMSS-DB
NOAA20 VIIRS SST (Daily) CIMSS-DB
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NOAA20 VIIRS True Color (Daily) DB ConUS
[j01-viirs-tc-msn-daily]
View of j01-viirs-tc-msn
View of j01-viirs-tc-msn
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NOAA20 VIIRS DNB Adaptive (Daily) DB ConUS
[j01-viirs-adaptive-dnb-msn-daily]
View of j01-viirs-adaptive-dnb-msn
View of j01-viirs-adaptive-dnb-msn
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NOAA20 VIIRS DNB Adaptive DB ConUS
[j01-viirs-adaptive-dnb-msn]
j01-viirs-adaptive-dnb-msn
j01-viirs-adaptive-dnb-msn
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NOAA20 VIIRS DNB Dynamic (Daily) DB ConUS
[j01-viirs-dynamic-dnb-msn-daily]
View of j01-viirs-dynamic-dnb-msn
View of j01-viirs-dynamic-dnb-msn
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NOAA20 VIIRS DNB Dynamic DB ConUS
[j01-viirs-dynamic-dnb-msn]
j01-viirs-dynamic-dnb-msn
j01-viirs-dynamic-dnb-msn
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NOAA20 VIIRS DNB Histogram (Daily) DB ConUS
[j01-viirs-hncc-dnb-msn-daily]
View of j01-viirs-hncc-dnb-msn
View of j01-viirs-hncc-dnb-msn
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NOAA20 VIIRS DNB Histogram DB ConUS
[j01-viirs-hncc-dnb-msn]
j01-viirs-hncc-dnb-msn
j01-viirs-hncc-dnb-msn
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NOAA20 VIIRS False Color (Daily) DB ConUS
[j01-viirs-fc-msn-daily]
View of j01-viirs-fc-msn
View of j01-viirs-fc-msn
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NOAA20 VIIRS Footprint DB ConUS
[j01-viirs-footprint-msn]
This product represents the area of data collected through direct broadcast(DB) during the overpass of NOAA-20 (J01) from multiple stations operated in affiliation with the Space Science and Engineering Center (SSEC) at the...
This product represents the area of data collected through direct broadcast (DB) during the overpass of NOAA-20 (J01) from multiple stations operated in affiliation with the Space Science and Engineering Center (SSEC) at the University of Wisconsin-Madison.
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NOAA20 VIIRS SST (Daily) DB ConUS
[j01-viirs-sst-msn-daily]
NOAA20 VIIRS SST (Daily) CIMSS-DB
NOAA20 VIIRS SST (Daily) CIMSS-DB
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NOAA20 VIIRS True Color (Daily) DB ConUS
[j01-viirs-tc-msn-daily]
View of j01-viirs-tc-msn
View of j01-viirs-tc-msn
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NOAA20 VIIRS DNB Adaptive (Daily) DB ConUS
[j01-viirs-adaptive-dnb-msn-daily]
View of j01-viirs-adaptive-dnb-msn
View of j01-viirs-adaptive-dnb-msn
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NOAA20 VIIRS DNB Adaptive DB ConUS
[j01-viirs-adaptive-dnb-msn]
j01-viirs-adaptive-dnb-msn
j01-viirs-adaptive-dnb-msn
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NOAA20 VIIRS DNB Dynamic (Daily) DB ConUS
[j01-viirs-dynamic-dnb-msn-daily]
View of j01-viirs-dynamic-dnb-msn
View of j01-viirs-dynamic-dnb-msn
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NOAA20 VIIRS DNB Dynamic DB ConUS
[j01-viirs-dynamic-dnb-msn]
j01-viirs-dynamic-dnb-msn
j01-viirs-dynamic-dnb-msn
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NOAA20 VIIRS DNB Histogram (Daily) DB ConUS
[j01-viirs-hncc-dnb-msn-daily]
View of j01-viirs-hncc-dnb-msn
View of j01-viirs-hncc-dnb-msn
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NOAA20 VIIRS DNB Histogram DB ConUS
[j01-viirs-hncc-dnb-msn]
j01-viirs-hncc-dnb-msn
j01-viirs-hncc-dnb-msn
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NOAA20 VIIRS False Color (Daily) DB ConUS
[j01-viirs-fc-msn-daily]
View of j01-viirs-fc-msn
View of j01-viirs-fc-msn
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NOAA20 VIIRS Footprint DB ConUS
[j01-viirs-footprint-msn]
This product represents the area of data collected through direct broadcast(DB) during the overpass of NOAA-20 (J01) from multiple stations operated in affiliation with the Space Science and Engineering Center (SSEC) at the...
This product represents the area of data collected through direct broadcast (DB) during the overpass of NOAA-20 (J01) from multiple stations operated in affiliation with the Space Science and Engineering Center (SSEC) at the University of Wisconsin-Madison.
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NOAA20 VIIRS SST (Daily) DB ConUS
[j01-viirs-sst-msn-daily]
NOAA20 VIIRS SST (Daily) CIMSS-DB
NOAA20 VIIRS SST (Daily) CIMSS-DB
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NOAA20 VIIRS True Color (Daily) DB ConUS
[j01-viirs-tc-msn-daily]
View of j01-viirs-tc-msn
View of j01-viirs-tc-msn
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SNPP VIIRS True Color DB Puerto Rico
[npptc-upr]
NPP True Color (TC) - Puerto Rico DB
NPP True Color (TC) - Puerto Rico DB
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SNPP VIIRS DNB Adaptive (Daily) DB ConUS
[npp-viirs-adaptive-dnb-msn-daily]
View of npp-viirs-adaptive-dnb-msn
View of npp-viirs-adaptive-dnb-msn
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SNPP VIIRS DNB Adaptive DB ConUS
[npp-viirs-adaptive-dnb-msn]
npp-viirs-adaptive-dnb-msn
npp-viirs-adaptive-dnb-msn
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SNPP VIIRS DNB Dynamic (Daily) DB ConUS
[npp-viirs-dynamic-dnb-msn-daily]
View of npp-viirs-dynamic-dnb-msn
View of npp-viirs-dynamic-dnb-msn
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SNPP VIIRS DNB Dynamic DB ConUS
[npp-viirs-dynamic-dnb-msn]
npp-viirs-dynamic-dnb-msn
npp-viirs-dynamic-dnb-msn
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SNPP VIIRS DNB Histogram (Daily) DB ConUS
[npp-viirs-hncc-dnb-msn-daily]
View of npp-viirs-hncc-dnb-msn
View of npp-viirs-hncc-dnb-msn
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SNPP VIIRS DNB Histogram DB ConUS
[npp-viirs-hncc-dnb-msn]
npp-viirs-hncc-dnb-msn
npp-viirs-hncc-dnb-msn
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SNPP VIIRS False Color (Daily) DB ConUS
[npp-viirs-fc-msn-daily]
View of npp-viirs-fc-msn
View of npp-viirs-fc-msn
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SNPP VIIRS Footprint DB ConUS
[npp-viirs-footprint-msn]
This product represents the area of data collected through direct broadcast(DB) during the overpass of Suomi-NPP (SNPP) from multiple stations operated in affiliation with the Space Science and Engineering Center...
This product represents the area of data collected through direct broadcast (DB) during the overpass of Suomi-NPP (SNPP) from multiple stations operated in affiliation with the Space Science and Engineering Center (SSEC) at the University of Wisconsin-Madison.
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SNPP VIIRS SST (Daily) DB ConUS
[npp-viirs-sst-msn-daily]
SNPP VIIRS SST (Daily) CIMSS-DB
SNPP VIIRS SST (Daily) CIMSS-DB
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SNPP VIIRS True Color (Daily) DB ConUS
[npp-viirs-tc-msn-daily]
View of npp-viirs-tc-msn
View of npp-viirs-tc-msn
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SNPP VIIRS DNB Adaptive (Daily) DB ConUS
[npp-viirs-adaptive-dnb-msn-daily]
View of npp-viirs-adaptive-dnb-msn
View of npp-viirs-adaptive-dnb-msn
|
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SNPP VIIRS DNB Adaptive DB ConUS
[npp-viirs-adaptive-dnb-msn]
npp-viirs-adaptive-dnb-msn
npp-viirs-adaptive-dnb-msn
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SNPP VIIRS DNB Dynamic (Daily) DB ConUS
[npp-viirs-dynamic-dnb-msn-daily]
View of npp-viirs-dynamic-dnb-msn
View of npp-viirs-dynamic-dnb-msn
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SNPP VIIRS DNB Dynamic DB ConUS
[npp-viirs-dynamic-dnb-msn]
npp-viirs-dynamic-dnb-msn
npp-viirs-dynamic-dnb-msn
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SNPP VIIRS DNB Histogram (Daily) DB ConUS
[npp-viirs-hncc-dnb-msn-daily]
View of npp-viirs-hncc-dnb-msn
View of npp-viirs-hncc-dnb-msn
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SNPP VIIRS DNB Histogram DB ConUS
[npp-viirs-hncc-dnb-msn]
npp-viirs-hncc-dnb-msn
npp-viirs-hncc-dnb-msn
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SNPP VIIRS False Color (Daily) DB ConUS
[npp-viirs-fc-msn-daily]
View of npp-viirs-fc-msn
View of npp-viirs-fc-msn
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SNPP VIIRS Footprint DB ConUS
[npp-viirs-footprint-msn]
This product represents the area of data collected through direct broadcast(DB) during the overpass of Suomi-NPP (SNPP) from multiple stations operated in affiliation with the Space Science and Engineering Center...
This product represents the area of data collected through direct broadcast (DB) during the overpass of Suomi-NPP (SNPP) from multiple stations operated in affiliation with the Space Science and Engineering Center (SSEC) at the University of Wisconsin-Madison.
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SNPP VIIRS SST (Daily) DB ConUS
[npp-viirs-sst-msn-daily]
SNPP VIIRS SST (Daily) CIMSS-DB
SNPP VIIRS SST (Daily) CIMSS-DB
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SNPP VIIRS True Color (Daily) DB ConUS
[npp-viirs-tc-msn-daily]
View of npp-viirs-tc-msn
View of npp-viirs-tc-msn
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SNPP VIIRS DNB Adaptive (Daily) DB ConUS
[npp-viirs-adaptive-dnb-msn-daily]
View of npp-viirs-adaptive-dnb-msn
View of npp-viirs-adaptive-dnb-msn
|
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SNPP VIIRS DNB Adaptive DB ConUS
[npp-viirs-adaptive-dnb-msn]
npp-viirs-adaptive-dnb-msn
npp-viirs-adaptive-dnb-msn
|
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SNPP VIIRS DNB Dynamic (Daily) DB ConUS
[npp-viirs-dynamic-dnb-msn-daily]
View of npp-viirs-dynamic-dnb-msn
View of npp-viirs-dynamic-dnb-msn
|
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SNPP VIIRS DNB Dynamic DB ConUS
[npp-viirs-dynamic-dnb-msn]
npp-viirs-dynamic-dnb-msn
npp-viirs-dynamic-dnb-msn
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SNPP VIIRS DNB Histogram (Daily) DB ConUS
[npp-viirs-hncc-dnb-msn-daily]
View of npp-viirs-hncc-dnb-msn
View of npp-viirs-hncc-dnb-msn
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SNPP VIIRS DNB Histogram DB ConUS
[npp-viirs-hncc-dnb-msn]
npp-viirs-hncc-dnb-msn
npp-viirs-hncc-dnb-msn
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SNPP VIIRS False Color (Daily) DB ConUS
[npp-viirs-fc-msn-daily]
View of npp-viirs-fc-msn
View of npp-viirs-fc-msn
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SNPP VIIRS Footprint DB ConUS
[npp-viirs-footprint-msn]
This product represents the area of data collected through direct broadcast(DB) during the overpass of Suomi-NPP (SNPP) from multiple stations operated in affiliation with the Space Science and Engineering Center...
This product represents the area of data collected through direct broadcast (DB) during the overpass of Suomi-NPP (SNPP) from multiple stations operated in affiliation with the Space Science and Engineering Center (SSEC) at the University of Wisconsin-Madison.
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SNPP VIIRS SST (Daily) DB ConUS
[npp-viirs-sst-msn-daily]
SNPP VIIRS SST (Daily) CIMSS-DB
SNPP VIIRS SST (Daily) CIMSS-DB
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SNPP VIIRS True Color (Daily) DB ConUS
[npp-viirs-tc-msn-daily]
View of npp-viirs-tc-msn
View of npp-viirs-tc-msn
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SNPP VIIRS DNB Adaptive (Daily) DB ConUS
[npp-viirs-adaptive-dnb-msn-daily]
View of npp-viirs-adaptive-dnb-msn
View of npp-viirs-adaptive-dnb-msn
|
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SNPP VIIRS DNB Adaptive DB ConUS
[npp-viirs-adaptive-dnb-msn]
npp-viirs-adaptive-dnb-msn
npp-viirs-adaptive-dnb-msn
|
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SNPP VIIRS DNB Dynamic (Daily) DB ConUS
[npp-viirs-dynamic-dnb-msn-daily]
View of npp-viirs-dynamic-dnb-msn
View of npp-viirs-dynamic-dnb-msn
|
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SNPP VIIRS DNB Dynamic DB ConUS
[npp-viirs-dynamic-dnb-msn]
npp-viirs-dynamic-dnb-msn
npp-viirs-dynamic-dnb-msn
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SNPP VIIRS DNB Histogram (Daily) DB ConUS
[npp-viirs-hncc-dnb-msn-daily]
View of npp-viirs-hncc-dnb-msn
View of npp-viirs-hncc-dnb-msn
|
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SNPP VIIRS DNB Histogram DB ConUS
[npp-viirs-hncc-dnb-msn]
npp-viirs-hncc-dnb-msn
npp-viirs-hncc-dnb-msn
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SNPP VIIRS False Color (Daily) DB ConUS
[npp-viirs-fc-msn-daily]
View of npp-viirs-fc-msn
View of npp-viirs-fc-msn
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SNPP VIIRS Footprint DB ConUS
[npp-viirs-footprint-msn]
This product represents the area of data collected through direct broadcast(DB) during the overpass of Suomi-NPP (SNPP) from multiple stations operated in affiliation with the Space Science and Engineering Center...
This product represents the area of data collected through direct broadcast (DB) during the overpass of Suomi-NPP (SNPP) from multiple stations operated in affiliation with the Space Science and Engineering Center (SSEC) at the University of Wisconsin-Madison.
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SNPP VIIRS SST (Daily) DB ConUS
[npp-viirs-sst-msn-daily]
SNPP VIIRS SST (Daily) CIMSS-DB
SNPP VIIRS SST (Daily) CIMSS-DB
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SNPP VIIRS True Color (Daily) DB ConUS
[npp-viirs-tc-msn-daily]
View of npp-viirs-tc-msn
View of npp-viirs-tc-msn
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Freezing Rain Probability >= .25" Final Forecast
[WPC-picezgt25]
The Probability of Freezing Rain Accumulating ≥ .25" Days 1-3
Theoperational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h...
The Probability of Freezing Rain Accumulating ≥ .25" Days 1-3
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
The product depicts the probability of freezing rain reaching or exceeding 0.25 inch for Days 1-3.
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Freezing Rain Probability >= 0.01"/24h
[WPC-picez24gep01]
The 24-Hour Probability of Freezing Rain Accumulating ≥ .01"
Theoperational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h...
The 24-Hour Probability of Freezing Rain Accumulating ≥ .01"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Freezing Rain Probability >= 0.10"/24h
[WPC-picez24gep10]
The 24-Hour Probability of Freezing Rain Accumulating ≥ .10"
Theoperational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h...
The 24-Hour Probability of Freezing Rain Accumulating ≥ .10"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Freezing Rain Probability >= 0.25"/24h
[WPC-picez24gep25]
The 24-Hour Probability of Freezing Rain Accumulating ≥ .25"
Theoperational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h...
The 24-Hour Probability of Freezing Rain Accumulating ≥ .25"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Freezing Rain Probability >=0.50"/24h
[WPC-picez24gep50]
The 24-Hour Probability of Freezing Rain Accumulating ≥ .50"
Theoperational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h...
The 24-Hour Probability of Freezing Rain Accumulating ≥ .50"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Freezing Rain Probability >=1.00"/24h
[WPC-picez24ge1]
The 24-Hour Probability of Freezing Rain Accumulating ≥ 1.00"
Theoperational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h...
The 24-Hour Probability of Freezing Rain Accumulating ≥ 1.00"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 0.1"/24h
[WPC-psnow24gep1]
24Hour Probability of Snow Accumulating ≥.1"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥.1"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 1.0"/24h
[WPC-psnow24ge1]
24Hour Probability of Snow Accumulating ≥1"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥1"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 2.0"/24h
[WPC-psnow24ge2]
24Hour Probability of Snow Accumulating ≥2"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥2"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 4.0"/24h
[WPC-psnow24ge4]
24Hour Probability of Snow Accumulating ≥4"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥4"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 6.0"/24h
[WPC-psnow24ge6]
24Hour Probability of Snow Accumulating ≥6"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥6"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 8.0"/24h
[WPC-psnow24ge8]
24Hour Probability of Snow Accumulating ≥8"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥8"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 12.0"/24h
[WPC-psnow24ge12p0]
24Hour Probability of Snow Accumulating ≥12"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥12"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 18.0"/24h
[WPC-psnow24ge18p0]
24Hour Probability of Snow Accumulating ≥18"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥18"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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WSSI Blowing Snow
[WPC-WSSI-BlowingSnow]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
Blowing Snow Index Indicates the potential disruption due to blowing and drifting snow. This index accounts for land use type. For example, more densely forested areas will show less blowing snow than open grassland areas.
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WSSI Flash Freeze
[WPC-WSSI-FlashFreeze]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
Flash Freeze Index Indicates the potential impacts of flash freezing (temperatures starting above freezing and quickly dropping below freezing) during or after precipitation events
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WSSI Ground Blizzard
[WPC-WSSI-Blizzard]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
Ground Blizzard indicates the potential travel-related impacts of strong winds interacting with
pre-existing snow cover. This is the only sub-component that does not require snow to be forecast in order for calculations to be made. The NOHRSC snow cover data along with forecast winds are used to model the ground blizzard.
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WSSI Ice Accumulation
[WPC-WSSI-IceAccum]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
Ice Accumulation indicates potential infrastructure impacts (e.g. roads/bridges) due to combined effects and severity of ice and wind. Designated urban areas are also weighted a little more than non-urban areas. Please note that not all NWS offices provide ice accumulation information into the NDFD. In those areas, the ice accumulation is not calculated.
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WSSI Overall Impact
[WPC-WSSI]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
The Overall WSSI Impact value is the maximum value from all the sub-components. The specific sub-components are:
● Snow Load Index
● Snow Amount Index
● Ice Accumulation
● Blowing Snow Index
● Flash Freeze Index
● Ground Blizzard
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WSSI Snow Amount
[WPC-WSSI-SnowAmount]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
Snow Amount indicates potential impacts due to the total amount of snow or the snow accumulation rate. This index also normalizes for climatology, such that regions of the country that experience, on average, less snowfall will show a higher level of severity for the same amount of snow that is forecast across a region that experiences more snowfall on average.
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WSSI Snow Load
[WPC-WSSI-SnowLoad]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
Snow Load indicates potential infrastructure impacts due to the weight of the snow. This index accounts for the land cover type. For example, more forested and urban areas will show increased severity versus the same snow conditions in grasslands.
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IR Winds 250-100mb
[AMV-ULhigh]
AMV: Upper Level IR/WV (100-250mb)
AMV: Upper Level IR/WV (100-250mb)
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IR Winds 350-251mb
[AMV-ULmid]
AMV: Upper Level IR/WV (251-350mb)
AMV: Upper Level IR/WV (251-350mb)
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IR Winds 500-351mb
[AMV-ULlow]
AMV: Upper Level IR/WV (351-500mb)
AMV: Upper Level IR/WV (351-500mb)
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Vis Winds 800-700mb
[AMV-VISmid]
AMV: Middle Level Visible (700-800mb)
AMV: Middle Level Visible (700-800mb)
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Vis Winds 925-801mb
[AMV-VISlow]
AMV: Lower Level Visible (801-925mb)
AMV: Lower Level Visible (801-925mb)
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Snow Depth (SNODAS)
[SNODAS-Thickness]
SNODAS (SNOw Data Assimilation System) is a modeling and data assimilationsystem developed by NOAA National Weather Service"s NOHRSC (National Operational Hydrologic Remote Sensing Center) to provide the best possible...
SNODAS (SNOw Data Assimilation System) is a modeling and data assimilation system developed by NOAA National Weather Service"s NOHRSC (National Operational Hydrologic Remote Sensing Center) to provide the best possible estimates of snow cover and associated parameters to support hydrologic modeling and analysis. The aim of SNODAS is to provide a physically consistent framework to integrate snow data from satellite, airborne platforms, and ground stations with model estimates of snow cover. The 24hr Snow Thickness is a daily snapshot of snow thickness at 0600hr UTC.
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Snowfall Total - 24hr (SNODAS)
[SNODAS-Accumulate]
SNODAS (SNOw Data Assimilation System) is a modeling and data assimilationsystem developed by NOAA National Weather Service"s NOHRSC (National Operational Hydrologic Remote Sensing Center) to provide the best possible...
SNODAS (SNOw Data Assimilation System) is a modeling and data assimilation system developed by NOAA National Weather Service"s NOHRSC (National Operational Hydrologic Remote Sensing Center) to provide the best possible estimates of snow cover and associated parameters to support hydrologic modeling and analysis. The aim of SNODAS is to provide a physically consistent framework to integrate snow data from satellite, airborne platforms, and ground stations with model estimates of snow cover. 24hr Snow Fall Total is calculated every 24 hours at 0600hr UTC and posted shortly thereafter.
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Winter Weather Hazards (Issued)
[WWINTER]
Winter Weather is a collection of Hazards associated with all types ofWinter precip and conditions. Hazards are issued by the NWS WSFOs as Advisories, Watches and Warnings. SnowEvents include SnowStorm,...
Winter Weather is a collection of Hazards associated with all types of Winter precip and conditions. Hazards are issued by the NWS WSFOs as Advisories, Watches and Warnings. SnowEvents include SnowStorm, WinterStorm, Snow, HeavySnow, LakeEffectSnow and BlowingSnow. IceEvents include Sleet, HeavySleet, FreezingRain, IceStorm and FreezingFog. Click on objects to get a detailed description of the specific hazard.
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Winter Weather Hazards (Valid)
[XWINTER]
The National Weather Service issues a variety of Winter Weather warnings,watches and advisories. The event type is indicated on the map by different colors. This product contains Winter Weather Hazards VALID for a 48hr...
The National Weather Service issues a variety of Winter Weather warnings, watches and advisories. The event type is indicated on the map by different colors. This product contains Winter Weather Hazards VALID for a 48hr Window spanning from the previous 24hrs to 24hrs in the future at 1hr increments.
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Freezing Rain Probability >= .25" Final Forecast
[WPC-picezgt25]
The Probability of Freezing Rain Accumulating ≥ .25" Days 1-3
Theoperational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h...
The Probability of Freezing Rain Accumulating ≥ .25" Days 1-3
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
The product depicts the probability of freezing rain reaching or exceeding 0.25 inch for Days 1-3.
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Freezing Rain Probability >= 0.01"/24h
[WPC-picez24gep01]
The 24-Hour Probability of Freezing Rain Accumulating ≥ .01"
Theoperational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h...
The 24-Hour Probability of Freezing Rain Accumulating ≥ .01"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Freezing Rain Probability >= 0.10"/24h
[WPC-picez24gep10]
The 24-Hour Probability of Freezing Rain Accumulating ≥ .10"
Theoperational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h...
The 24-Hour Probability of Freezing Rain Accumulating ≥ .10"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Freezing Rain Probability >= 0.25"/24h
[WPC-picez24gep25]
The 24-Hour Probability of Freezing Rain Accumulating ≥ .25"
Theoperational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h...
The 24-Hour Probability of Freezing Rain Accumulating ≥ .25"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Freezing Rain Probability >=0.50"/24h
[WPC-picez24gep50]
The 24-Hour Probability of Freezing Rain Accumulating ≥ .50"
Theoperational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h...
The 24-Hour Probability of Freezing Rain Accumulating ≥ .50"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Freezing Rain Probability >=1.00"/24h
[WPC-picez24ge1]
The 24-Hour Probability of Freezing Rain Accumulating ≥ 1.00"
Theoperational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h...
The 24-Hour Probability of Freezing Rain Accumulating ≥ 1.00"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Midwest Winter Road Conditions
[ROADS-IADOT]
Conditions are updated every 10 minutes during the winter season (October15 to April 15) and on an as-needed basis during the non-winter months. Layer and service is maintained by the Iowa DOT GIS team on behalf of the...
Conditions are updated every 10 minutes during the winter season (October 15 to April 15) and on an as-needed basis during the non-winter months. Layer and service is maintained by the Iowa DOT GIS team on behalf of the Office of Traffic Operations. This data is provided as is through this value added REST service. All conditions have been remapped to the best of our ability to meet the condition reporting criteria as defined by the Iowa DOT. Some discrepancies may appear. This data service should only be used for reference only. For the most accurate information, please utilize the authoritative state 511 sites below.
State 511 Sites 511 Vendor Disclaimers
North Dakota Iteris The data is provided as is and without liability from the North Dakota Department of Transportation (NDDOT). The NDDOT does not guarantee this data to be free from errors, or inaccuracies, and disclaims any responsibility or liability for interpretations or decisions based on this data.
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Snow Fall Rate
[NESDIS-SnowFallRate]
AMSU Snow Fall Rate Global by NOAA-NESDIS
AMSU Snow Fall Rate Global by NOAA-NESDIS
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Snowfall Reports - 6hr
[lsr-snow]
NWS reported 6hr Snowfall Totals (inches).
NWS reported 6hr Snowfall Totals (inches).
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Snow Probability >= 0.1"/24h
[WPC-psnow24gep1]
24Hour Probability of Snow Accumulating ≥.1"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥.1"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 1.0"/24h
[WPC-psnow24ge1]
24Hour Probability of Snow Accumulating ≥1"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥1"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 2.0"/24h
[WPC-psnow24ge2]
24Hour Probability of Snow Accumulating ≥2"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥2"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 4.0"/24h
[WPC-psnow24ge4]
24Hour Probability of Snow Accumulating ≥4"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥4"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 6.0"/24h
[WPC-psnow24ge6]
24Hour Probability of Snow Accumulating ≥6"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥6"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 8.0"/24h
[WPC-psnow24ge8]
24Hour Probability of Snow Accumulating ≥8"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥8"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 12.0"/24h
[WPC-psnow24ge12p0]
24Hour Probability of Snow Accumulating ≥12"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥12"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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Snow Probability >= 18.0"/24h
[WPC-psnow24ge18p0]
24Hour Probability of Snow Accumulating ≥18"
The operational WPC WinterWeather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending...
24Hour Probability of Snow Accumulating ≥18"
The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
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WSSI Blowing Snow
[WPC-WSSI-BlowingSnow]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
Blowing Snow Index Indicates the potential disruption due to blowing and drifting snow. This index accounts for land use type. For example, more densely forested areas will show less blowing snow than open grassland areas.
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WSSI Flash Freeze
[WPC-WSSI-FlashFreeze]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
Flash Freeze Index Indicates the potential impacts of flash freezing (temperatures starting above freezing and quickly dropping below freezing) during or after precipitation events
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WSSI Ground Blizzard
[WPC-WSSI-Blizzard]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
Ground Blizzard indicates the potential travel-related impacts of strong winds interacting with
pre-existing snow cover. This is the only sub-component that does not require snow to be forecast in order for calculations to be made. The NOHRSC snow cover data along with forecast winds are used to model the ground blizzard.
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WSSI Ice Accumulation
[WPC-WSSI-IceAccum]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
Ice Accumulation indicates potential infrastructure impacts (e.g. roads/bridges) due to combined effects and severity of ice and wind. Designated urban areas are also weighted a little more than non-urban areas. Please note that not all NWS offices provide ice accumulation information into the NDFD. In those areas, the ice accumulation is not calculated.
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WSSI Overall Impact
[WPC-WSSI]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
The Overall WSSI Impact value is the maximum value from all the sub-components. The specific sub-components are:
● Snow Load Index
● Snow Amount Index
● Ice Accumulation
● Blowing Snow Index
● Flash Freeze Index
● Ground Blizzard
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WSSI Snow Amount
[WPC-WSSI-SnowAmount]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
Snow Amount indicates potential impacts due to the total amount of snow or the snow accumulation rate. This index also normalizes for climatology, such that regions of the country that experience, on average, less snowfall will show a higher level of severity for the same amount of snow that is forecast across a region that experiences more snowfall on average.
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WSSI Snow Load
[WPC-WSSI-SnowLoad]
The WSSI is created by screening the official National Weather Servicegridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or...
The WSSI is created by screening the official National Weather Service gridded forecasts from
the National Digital Forecast Database (NDFD) for winter weather elements and combining
those data with non-meteorological or static information datasets (e.g., climatology, land-use,
urban areas). This process creates a graphical depiction of anticipated overall impacts to
society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts.
Snow Load indicates potential infrastructure impacts due to the weight of the snow. This index accounts for the land cover type. For example, more forested and urban areas will show increased severity versus the same snow conditions in grasslands.
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2015 WI NAIP Counties
[wi-counties]
This layer displays Wisconsin county outlines. Right-click-probe allowsdownloads of source imagery for the 2015 Wisconsin NAIP aerial photography county mosaics.
This layer displays Wisconsin county outlines. Right-click-probe allows downloads of source imagery for the 2015 Wisconsin NAIP aerial photography county mosaics.
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2015 WI NAIP DOQQs
[NAIPWI2015fp]
This layer displays the coverage footprints for the 2015 Wisconsin NAIPaerial photography. Right-click probe allows downloads of source imagery.
This layer displays the coverage footprints for the 2015 Wisconsin NAIP aerial photography. Right-click probe allows downloads of source imagery.
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Infrared 6 inch Imagery of Madison
[madisonir]
Infrared 6 inch Imagery of Madison
Infrared 6 inch Imagery of Madison
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NAIP WI
[NAIPWI]
National Agricultural Imagery Program aerial photography from the WisconsinFarm Service Agency (WI-FSA) of the USDA.
National Agricultural Imagery Program aerial photography from the Wisconsin Farm Service Agency (WI-FSA) of the USDA.
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NAIP WI Color Infrared
[NAIPWICIR]
National Agricultural Imagery Program aerial photography from the WisconsinFarm Service Agency (WI-FSA) of the USDA (Color Infrared)
National Agricultural Imagery Program aerial photography from the Wisconsin Farm Service Agency (WI-FSA) of the USDA (Color Infrared)
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WI Coastal Imagery
[WICoast]
WI Coastal Imagery displays aerial photographs of the Lake Michigan coastof Wisconsin from 2007. The images are being used to monitor cladophora algae growth.
WI Coastal Imagery displays aerial photographs of the Lake Michigan coast of Wisconsin from 2007. The images are being used to monitor cladophora algae growth.
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WI Coastal Shaded Relief
[WIcoastalshdrlf]
WI coastal shaded relief map generated from LiDAR data.
WI coastal shaded relief map generated from LiDAR data.
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WI Lake Clarity
[LakesTSI]
These data represent the estimated clarity, or transparency, of the 8,000largest of those lakes as measured by satellite remote sensing (Landsat).
These data represent the estimated clarity, or transparency, of the 8,000 largest of those lakes as measured by satellite remote sensing (Landsat).
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WISCLAND 1993
[wiscland]
In 1993 a team of researchers from University of Wisconsin-Madison (ERSC)and the Wisconsin DNR developed WISCLAND, the first satellite-derived land cover map of Wisconsin. The UW-Madison (SCO) and the DNR partnered on a...
In 1993 a team of researchers from University of Wisconsin-Madison (ERSC) and the Wisconsin DNR developed WISCLAND, the first satellite-derived land cover map of Wisconsin. The UW-Madison (SCO) and the DNR partnered on a project to produce an updated land cover map of Wisconsin. The resulting dataset, known as Wiscland 2.0, was completed in August 2016.
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Wisconsin in 3D (SRTM)
[wisc-3d]
The Space Shuttle Endeavour collected data to produce a digital elevationmodel of the Earth during the Shuttle Radar Topography Mission (SRTM), flown from February 11-22, 2000. Researchers clipped Wisconsin from this...
The Space Shuttle Endeavour collected data to produce a digital elevation model of the Earth during the Shuttle Radar Topography Mission (SRTM), flown from February 11-22, 2000. Researchers clipped Wisconsin from this data to produce this 3D anaglyph. To see the 3D effect, use Red-Blue 3D glasses (red over left eye).
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Wisconsin LIDAR Hillshade
[wi-hillshade]
WisconsinView is a remote sensing consortium and member of AmericaView.org.These Wisconsin lidar data sets were collected by aircraft and processed by state and county agencies. These data are hosted by WisconsinView and...
WisconsinView is a remote sensing consortium and member of AmericaView.org. These Wisconsin lidar data sets were collected by aircraft and processed by state and county agencies. These data are hosted by WisconsinView and visualized here with coordination and funding from the WI State Dept. of Administration, Geographic Information Office and NOAA"s coastal management program.
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WI USGS Landsat Poster
[wilandsat]
This is a georeferenced poster from the USGS. The original source is:http://eros.usgs.gov/imagegallery/landsat-state-mosaics unfortunately the original poster imagery without graphics burned-in is not available.
This is a georeferenced poster from the USGS. The original source is: http://eros.usgs.gov/imagegallery/landsat-state-mosaics unfortunately the original poster imagery without graphics burned-in is not available.
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