[NWS-Alerts-All]

NWS Alerts (All)

[NWS-Alerts-Coastal-Hazards]

NWS Alerts (Coastal Hazards)

[NWS-Alerts-Convective]

NWS Alerts (Convective)

[NWS-Alerts-Fire-Weather]

NWS Alerts (Fire Weather)

[NWS-Alerts-Hydrologic]

NWS Alerts (Hydrologic)

[NWS-Alerts-Marine]

NWS Alerts (Marine)

[NWS-Alerts-Non-Weather-Emergencies]

NWS Alerts (Non-Weather Emergencies)

[NWS-Alerts-Non-Convective]

NWS Alerts (Non Convective)

[NWS-Alerts-Other]

NWS Alerts (Other)

[NWS-Alerts-Tropical]

NWS Alerts (Tropical)

[NWS-Alerts-Winter-Weather]

NWS Alerts (Winter Weather)

[AFIMG-Points-GINA]

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.

[MIRS-RainRate-AK]

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.

[DayLandCloudFire-RGB-GINA]

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).

[FireTemperature-RGB-GINA]

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).

[NAIPWI2015fp]

This layer displays the coverage footprints for the 2015 Wisconsin NAIP aerial photography. Right-click probe allows downloads of source imagery.

[madisonir]

Infrared 6 inch Imagery of Madison

[NAIPWI]

National Agricultural Imagery Program aerial photography from the Wisconsin Farm Service Agency (WI-FSA) of the USDA.

[NAIPWICIR]

National Agricultural Imagery Program aerial photography from the Wisconsin Farm Service Agency (WI-FSA) of the USDA (Color Infrared)

[LakesTSI]

These data represent the estimated clarity, or transparency, of the 8,000 largest of those lakes as measured by satellite remote sensing (Landsat).

[SKITrails]

SKITrails

[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 project to produce an updated land cover map of Wisconsin. The resulting dataset, known as Wiscland 2.0, was completed in August 2016.

[wi-counties-basic]

[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 visualized here with coordination and funding from the WI State Dept. of Administration, Geographic Information Office and NOAA"s coastal management program.

[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.

[global-lakes-temperature]

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.

[global-lakes-trophic]

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.

[global-lakes-turbidity]

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)

[GLERL-GLSEAimage]

Great Lakes Surface Environmental Analysis (GLSEA) from GLERL. For more info see: http://coastwatch.glerl.noaa.gov/glsea/doc

[CW-dineof-chlora]

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.

[CW-dineof-kd490]

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.

[CW-dineof-spm]

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.

[ESA-daily-chlora]

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.

[GEMS-daily]

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.

[CIMSS-CTCtargets]

CIMSS-Cloud Top Cooling targets

[AIRMET-ICE]

AIRMET Icing Advisory

[AIRMET-IFR]

AIRMET-IFR Advisory

[AIRMET-MTN]

AIRMET-Mountain Obscured Advisory

[TAF]

Terminal Aerodrome Forecast (TAF)

[AIRMET-TURB]

AIRMET-Turlulence Advisory

[VAA]

Volcanic Ash Advisories: Ash Clouds

[turb-grid-30-31-kft]

[turb-grid-32-33-kft]

[turb-grid-34-35-kft]

[turb-grid-36-37-kft]

[turb-grid-38-39-kft]

turb-grid-38-39-kft

[turb-grid-40-41-kft]

[turb-poly-30-31-kft]

[turb-poly-32-33-kft]

[turb-poly-34-35-kft]

[turb-poly-36-37-kft]

[turb-poly-38-39-kft]

[turb-poly-40-41-kft]

[turb-poly-Max-Value-30-41-kft]

[turb-grid-Max-Value-30-41-kft]

[BRDF]

MODIS Clear View ConUS Composite. BRDF (Bidirectional Reluctance Distribution Function) is a 16-day cloud-free composite.

[G18-CODF]

[G18-DCOMP]

[G18-NCOMP]

[H09-CODF]

[j01-asc-CODACHA]

[j01-asc-CODDCOMP]

[j01-asc-CODNCOMP]

[j01-des-CODACHA]

[j01-des-CODDCOMP]

[j01-des-CODNCOMP]

[npp-des-CODNCOMP]

[npp-asc-CODACHA]

[npp-asc-CODDCOMP]

[npp-asc-CODNCOMP]

[npp-des-CODACHA]

[npp-des-CODDCOMP]

[H09-ACHAF]

[G18-CTPF]

[H09-CTPF]

[j01-asc-CTP]

[j01-des-CTP]

[npp-asc-CTP]

[npp-des-CTP]

[H09-ACHTF]

[H09-ICE-L0]

[H09-ICE-L2]

[H09-ICE-L4]

[H09-ICE-L6]

[H09-ICE-L8]

[H09-QPE]

[H09-VIS]

[nucaps-grid-co-total]

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

[G18-ACMFc]

[G18-ACM]

[G18-ACMF]

[H09-ACMF]

[j01-asc-ecm]

[j01-asc-ecm-it]

[j01-des-ecm]

[j01-des-ecm-it]

[npp-asc-ecm]

[npp-asc-ecm-it]

[npp-des-ecm]

[npp-des-ecm-it]

[G18-ACTPF]

[H09-ACTPF]

[j01-asc-phase]

[j01-des-phase]

[npp-asc-phase]

[npp-des-phase]

[nucaps-grid-ctf-lower]

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.

[nucaps-grid-ctf-upper]

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.

[nucaps-grid-ctp-lower]

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.

[nucaps-grid-ctp-upper]

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.

[mean-snow-cover-1988-2017]

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.

[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 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.

[j01-ist]

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.

[j01-ithk]

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.

[j02-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 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.

[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 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.

[snpp-ist]

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.

[snpp-ithk]

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.

[RAQDPS-COL-Diff-25]

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.

[HRRR-smoke-surface]

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.

[HRRR-smoke-column]

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.

[RAP-smoke-surface]

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.

[RAP-smoke-column]

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.

[REDFLAG]

REDFLAG

[XREDFLAG]

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

[SPC-FireOutlook-CATG-cmap]

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.

[SPCfwday1]

Fire Weather Outlook Day1 (Category)

[SPCfwday2]

Fire Weather Outlook Day2 (Category)

[AFIMG-Points-GINA]

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.

[AFIMG-Points]

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.

[DayLandCloudFire-RGB-GINA]

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).

[FireTemperature-RGB-GINA]

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).

[FIRMS-Fire-Image-j01-daily]

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).

[FIRMS-Fire-Points-j01-daily]

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).

[NOAA-Fire-Image-j01-daily]

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.

[NOAA-Fire-Points-j01-daily]

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.

[NOAA-Fire-Image-j01]

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.

[NOAA-Fire-Points-j01]

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.

[FIRMS-Fire-Image-npp-daily]

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.

[FIRMS-Fire-Points-npp-daily]

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.

[NOAA-Fire-Image-npp-daily]

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.

[NOAA-Fire-Points-npp-daily]

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.

[NOAA-Fire-Image-npp]

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.

[NOAA-Fire-Points-npp]

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.

[AFIMG-Points]

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.

[RAQDPS-COL-Diff-25]

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.

[HRRR-smoke-surface]

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.

[HRRR-smoke-column]

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.

[RAP-smoke-surface]

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.

[RAP-smoke-column]

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.

[WFIGS-Perimeters]

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.

[WFIGS-Points]

[ERTA-NCEP]

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

[WFLASH]

Flash Flood Hazards

[FOP]

WPC FLood Outlook Product

[FLOODWARN]

Flood Warning Polygons

[XFLOODWARN]

XFLOODWARN

[HVTEC]

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.

[ABI-Flood-DCOM]

ABI on GOES-18 & GOES-19 from NOAA

[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, 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

[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, 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

[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, 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

[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, 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

[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, 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

[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, 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

[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, 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

[VIIRS-ABI-Flood]

[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 represent a composite of all available VIIRS daylight imagery over the past 1 day. For more information visit: Here

[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 represent a composite of all available VIIRS daylight imagery over the past 5 days. For more information visit: Here

[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 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

[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 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

[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 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

[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 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

[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 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

[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 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

[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 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

[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 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

[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 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

[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 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

[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 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

[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 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

[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 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

[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 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

[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 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

[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 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

[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 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

[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 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

[VIIRS-3Dflood]

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.

[lsat8-llook-fc]

View of lsat8-llook-fc-scenes

[lsat8-llook-tir]

View of lsat8-llook-tir-scenes

[wrs2-land]

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.

[lsat9-llook-fc]

View of lsat9-llook-fc-scenes

[lsat9-llook-tir]

View of lsat9-llook-tir-scenes

[WFLASH]

Flash Flood Hazards

[XFLOODWARN]

XFLOODWARN

[HVTEC]

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.

[ERTA-NCEP]

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

[HRR-CONUS-FZRN-SFC]

HRRR ConUS Latest Freezing MASK

[HRR-CONUS-ICEP-SFC]

HRRR ConUS Latest Ice Mask

[HRR-CONUS-PCP-LATEST]

View of HRR-CONUS-PCP-SFC

[HRR-CONUS-RAIN-SFC]

HRRR ConUS Latest Rain Mask

[HRR-CONUS-PCP-SFC]

HRR-CONUS-PCP-SFC

[HRR-CONUS-RADAR-LATEST]

View of HRR-CONUS-PCP-SFC

[HRR-CONUS-SNOW-SFC]

HRRR ConUS Latest Snow Mask

[RAP-CONUS-PRAT-SFC-DBZ]

View of RAP-CONUS-PRAT-SFC

[TAF]

Terminal Aerodrome Forecast (TAF)

[GNCA-G16-ABI-L2-CMIPF-C01]

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

[GNCA-G16-ABI-L2-CMIPF-C02]

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

[GNCA-G16-ABI-L2-CMIPF-C03]

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

[GNCA-G16-ABI-L2-CMIPF-C04]

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

[GNCA-G16-ABI-L2-CMIPF-C05]

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

[GNCA-G16-ABI-L2-CMIPF-C06]

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

[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. 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

[GNCA-G16-ABI-L2-CMIPF-C08]

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

[GNCA-G16-ABI-L2-CMIPF-C09]

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

[GNCA-G16-ABI-L2-CMIPF-C10]

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

[GNCA-G16-ABI-L2-CMIPF-C11]

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

[GNCA-G16-ABI-L2-CMIPF-C12]

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

[GNCA-G16-ABI-L2-CMIPF-C13]

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

[GNCA-G16-ABI-L2-CMIPF-C14]

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

[GNCA-G16-ABI-L2-CMIPF-C15]

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

[GNCA-G16-ABI-L2-CMIPF-C16]

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

[GNCA-G16-ABI-L2-ADPF]

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

[GNCA-G16-ABI-L2-AODF]

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

[GNCA-G16-ABI-L2-ACMF]

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

[GNCA-G16-ABI-L2-CODF]

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

[GNCA-G16-ABI-L2-CPSF]

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

[GNCA-G16-ABI-L2-ACHAF]

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

[GNCA-G16-ABI-L2-ACTPF]

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

[GNCA-G16-ABI-L2-CTPF]

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

[GNCA-G16-ABI-L2-ACHTF]

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

[GNCA-G16-ABI-L2-DMWVF-C08]

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.

[GNCA-G16-ABI-L2-DMWF-C02]

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.

[GNCA-G16-ABI-L2-DMWF-C07]

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.

[GNCA-G16-ABI-L2-DMWF-C08]

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.

[GNCA-G16-ABI-L2-DMWF-C09]

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.

[GNCA-G16-ABI-L2-DMWF-C10]

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.

[GNCA-G16-ABI-L2-DMWF-C14]

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.

[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 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

[GNCA-G16-ABI-L2-DSRF]

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

[GNCA-G16-ABI-L2-FDCF]

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

[GNCA-G16-ABI-L2-LST2KMF]

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.

[GNCA-G16-ABI-L2-RRQPEF]

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

[GNCA-G16-ABI-L2-RSRF]

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

[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 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

[GNCA-G16-ABI-L2-TPWF]

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

[GNCA-G16-GLM-FlashDensity]

 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

[GNCA-G17-ABI-L2-CMIPF-C02]

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

[GNCA-G17-ABI-L2-CMIPF-C09]

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

[GNCA-G17-ABI-L2-CMIPF-C13]

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

[nppdnb]

NPP Day/Night Band - Dynamic

[AFIMG-Points]

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.

[GNCA-RIVER-FLD-joint-ABI]

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.

[GNCA-RIVER-FLDglobal-composite]

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

[GNCA-River-Flood-ABI-hourly]

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

[G18-C14]

11.2µm: Longwave window

[G18-ACMFc]

[G18-ACM]

[G18-CODF]

[G18-ACTPF]

[G18-CTPF]

[G18-ACMF]

[G18-DCOMP]

[G18-NCOMP]

[G18-daynight]

True color - day Cloud microphysics - night

[G19-ABI-CONUS-BAND01]

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

[G19-ABI-CONUS-BAND02]

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.

[G19-ABI-CONUS-BAND03]

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.

[G19-ABI-CONUS-BAND04]

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.

[G19-ABI-CONUS-BAND05]

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.

[G19-ABI-CONUS-BAND06]

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.

[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. 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.

[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. 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.

[G19-ABI-CONUS-BAND08]

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.

[G19-ABI-CONUS-BAND08-VAPR]

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.

[G19-ABI-CONUS-BAND09]

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.

[G19-ABI-CONUS-BAND09-VAPR]

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.

[G19-ABI-CONUS-BAND10]

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.

[G19-ABI-CONUS-BAND10-VAPR]

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.

[G19-ABI-CONUS-BAND11]

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.

[G19-ABI-CONUS-BAND12]

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.

[G19-ABI-CONUS-BAND13]

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.

[G19-ABI-CONUS-BAND13-GRAD]

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.

[G19-ABI-CONUS-BAND14]

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.

[G19-ABI-CONUS-BAND15]

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.

[G19-ABI-CONUS-BAND16]

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.

[G19-ABI-CONUS-airmass]

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.

[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 (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-CONUS-convection]

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.

[G19-ABI-CONUS-day-microphysics-abi]

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.

[G19-ABI-CONUS-water-vapors2]

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.

[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 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.

[G19-ABI-CONUS-fire-temperature-awips]

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.

[G19-ABI-CONUS-fog]

G16-ABI-CONUS-fog

[G19-ABI-CONUS-geo-color]

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.

[G19-ABI-CONUS-ir-sandwich]

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.

[G19-ABI-CONUS-night-microphysics]

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.

[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 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.

[G19-ABI-CONUS-so2]

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.

[G19-ABI-CONUS-true-color]

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.

[G19-ABI-CONUS-ash]

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.

[G19-ABI-FD-BAND01]

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.

[G19-ABI-FD-BAND02]

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.

[G19-ABI-FD-BAND03]

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.

[G19-ABI-FD-BAND04]

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.

[G19-ABI-FD-BAND05]

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.

[G19-ABI-FD-BAND06]

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.

[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. 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.

[G19-ABI-FD-BAND08]

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.

[G19-ABI-FD-BAND09]

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.

[G19-ABI-FD-BAND10]

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.

[G19-ABI-FD-BAND11]

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.

[G19-ABI-FD-BAND12]

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.

[G19-ABI-FD-BAND13]

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.

[G19-ABI-FD-BAND13-GRAD]

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.

[G19-ABI-FD-BAND14]

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.

[G19-ABI-FD-BAND15]

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.

[G19-ABI-FD-BAND16]

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.

[G19-ABI-FD-airmass]

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.

[G19-ABI-FD-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 (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-FD-convection]

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.

[G19-ABI-FD-day-microphysics-abi]

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.

[G19-ABI-FD-water-vapors2]

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.

[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 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.

[G19-ABI-FD-fire-temperature-awips]

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.

[G19-ABI-FD-fog]

G16-ABI-FD-fog

[G19-ABI-FD-geo-color]

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.

[G19-ABI-FD-ir-sandwich]

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.

[G19-ABI-FD-night-microphysics]

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.

[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 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.

[G19-ABI-FD-so2]

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.

[G19-ABI-FD-true-color]

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.

[G19-ABI-FD-ash]

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-L2-AOD-DQF0]

[G19-L2-CONUS-FLS-Thickness]

Cloud thickness: Estimate of the geometric thickness (cloud top - cloud base) of a single layer liquid water stratus cloud.

[G19-L2-CONUS-FLS-IFR]

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.

[G19-L2-CONUS-FLS-LIFR]

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.

[G19-L2-CONUS-FLS-MVFR]

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.

[G19-ABI-MESO1-BAND01]

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.

[G19-ABI-MESO1-BAND02]

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.

[G19-ABI-MESO1-BAND03]

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.

[G19-ABI-MESO1-BAND04]

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.

[G19-ABI-MESO1-BAND05]

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.

[G19-ABI-MESO1-BAND06]

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.

[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. 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.

[G19-ABI-MESO1-BAND08]

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.

[G19-ABI-MESO1-BAND09]

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.

[G19-ABI-MESO1-BAND10]

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.

[G19-ABI-MESO1-BAND11]

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.

[G19-ABI-MESO1-BAND12]

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.

[G19-ABI-MESO1-BAND13]

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.

[G19-ABI-MESO1-BAND13-GRAD]

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.

[G19-ABI-MESO1-BAND14]

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.

[G19-ABI-MESO1-BAND15]

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.

[G19-ABI-MESO1-BAND16]

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.

[G19-ABI-MESO1-airmass]

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.

[G19-ABI-MESO1-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 (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-MESO1-convection]

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.

[G19-ABI-MESO1-day-microphysics-abi]

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.

[G19-ABI-MESO1-water-vapors2]

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.

[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 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.

[G19-ABI-MESO1-fire-temperature-awips]

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.

[G19-ABI-MESO1-fog]

G16-ABI-MESO1-fog

[G19-ABI-MESO1-geo-color]

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.

[G19-ABI-MESO1-ir-sandwich]

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.

[G19-ABI-MESO1-night-microphysics]

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.

[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 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.

[G19-ABI-MESO1-so2]

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.

[G19-ABI-MESO1-true-color]

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.

[G19-ABI-MESO1-ash]

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.

[G19-ABI-MESO2-BAND01]

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.

[G19-ABI-MESO2-BAND02]

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.

[G19-ABI-MESO2-BAND03]

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.

[G19-ABI-MESO2-BAND04]

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.

[G19-ABI-MESO2-BAND05]

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.

[G19-ABI-MESO2-BAND06]

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.

[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. 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.

[G19-ABI-MESO2-BAND08]

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.

[G19-ABI-MESO2-BAND09]

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.

[G19-ABI-MESO2-BAND10]

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.

[G19-ABI-MESO2-BAND11]

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.

[G19-ABI-MESO2-BAND12]

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.

[G19-ABI-MESO2-BAND13]

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.

[G19-ABI-MESO2-BAND13-GRAD]

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.

[G19-ABI-MESO2-BAND14]

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.

[G19-ABI-MESO2-BAND15]

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.

[G19-ABI-MESO2-BAND16]

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.

[G19-ABI-MESO2-airmass]

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.

[G19-ABI-MESO2-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 (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-MESO2-convection]

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.

[G19-ABI-MESO2-day-microphysics-abi]

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.

[G19-ABI-MESO2-water-vapors2]

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.

[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 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.

[G19-ABI-MESO2-fire-temperature-awips]

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.

[G19-ABI-MESO2-fog]

G16-ABI-MESO2-fog

[G19-ABI-MESO2-geo-color]

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.

[G19-ABI-MESO2-ir-sandwich]

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.

[G19-ABI-MESO2-night-microphysics]

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.

[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 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.

[G19-ABI-MESO2-so2]

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.

[G19-ABI-MESO2-true-color]

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.

[G19-ABI-MESO2-ash]

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.

[G19-ABI-CONUS-airmass]

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.

[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 (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-CONUS-convection]

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.

[G19-ABI-CONUS-day-microphysics-abi]

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.

[G19-ABI-CONUS-water-vapors2]

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.

[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 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.

[G19-ABI-CONUS-fire-temperature-awips]

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.

[G19-ABI-CONUS-fog]

G16-ABI-CONUS-fog

[G19-ABI-CONUS-geo-color]

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.

[G19-ABI-CONUS-ir-sandwich]

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.

[G19-ABI-CONUS-night-microphysics]

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.

[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 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.

[G19-ABI-CONUS-so2]

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.

[G19-ABI-CONUS-true-color]

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.

[G19-ABI-CONUS-ash]

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.

[G19-ABI-FD-airmass]

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.

[G19-ABI-FD-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 (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-FD-convection]

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.

[G19-ABI-FD-day-microphysics-abi]

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.

[G19-ABI-FD-water-vapors2]

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.

[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 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.

[G19-ABI-FD-fire-temperature-awips]

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.

[G19-ABI-FD-fog]

G16-ABI-FD-fog

[G19-ABI-FD-geo-color]

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.

[G19-ABI-FD-ir-sandwich]

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.

[G19-ABI-FD-night-microphysics]

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.

[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 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.

[G19-ABI-FD-so2]

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.

[G19-ABI-FD-true-color]

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.

[G19-ABI-FD-ash]

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.

[G19-ABI-MESO1-airmass]

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.

[G19-ABI-MESO1-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 (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-MESO1-convection]

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.

[G19-ABI-MESO1-day-microphysics-abi]

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.

[G19-ABI-MESO1-water-vapors2]

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.

[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 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.

[G19-ABI-MESO1-fire-temperature-awips]

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.

[G19-ABI-MESO1-fog]

G16-ABI-MESO1-fog

[G19-ABI-MESO1-geo-color]

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.

[G19-ABI-MESO1-ir-sandwich]

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.

[G19-ABI-MESO1-night-microphysics]

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.

[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 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.

[G19-ABI-MESO1-so2]

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.

[G19-ABI-MESO1-true-color]

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.

[G19-ABI-MESO1-ash]

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.

[G19-ABI-MESO2-airmass]

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.

[G19-ABI-MESO2-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 (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-MESO2-convection]

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.

[G19-ABI-MESO2-day-microphysics-abi]

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.

[G19-ABI-MESO2-water-vapors2]

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.

[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 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.

[G19-ABI-MESO2-fire-temperature-awips]

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.

[G19-ABI-MESO2-fog]

G16-ABI-MESO2-fog

[G19-ABI-MESO2-geo-color]

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.

[G19-ABI-MESO2-ir-sandwich]

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.

[G19-ABI-MESO2-night-microphysics]

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.

[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 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.

[G19-ABI-MESO2-so2]

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.

[G19-ABI-MESO2-true-color]

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.

[G19-ABI-MESO2-ash]

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.

[G18-ABI-CONUS-airmass]

G18-ABI-CONUS-airmass

[G18-ABI-CONUS-ash]

G18-ABI-CONUS-ash

[G18-ABI-CONUS-cloud-phase]

G18-ABI-CONUS-cloud-phase

[G18-ABI-CONUS-convection]

G18-ABI-CONUS-convection

[G18-ABI-CONUS-day-microphysics-abi]

G18-ABI-CONUS-day-microphysics-abi

[G18-ABI-CONUS-dust]

G18-ABI-CONUS-dust

[G18-ABI-CONUS-fire-temperature-awips]

G18-ABI-CONUS-fire-temperature-awips

[G18-ABI-CONUS-fog]

G18-ABI-CONUS-fog

[G18-ABI-CONUS-geo-color]

G18-ABI-CONUS-geo-color

[G18-ABI-CONUS-ir-sandwich]

G18-ABI-CONUS-ir-sandwich

[G18-ABI-CONUS-night-microphysics]

G18-ABI-CONUS-night-microphysics

[G18-ABI-CONUS-snow-fog]

G18-ABI-CONUS-snow-fog

[G18-ABI-CONUS-so2]

G18-ABI-CONUS-so2

[G18-ABI-CONUS-true-color]

View of G18-ABI-CONUS-geo-color

[G18-ABI-CONUS-water-vapors2]

G18-ABI-CONUS-water-vapors2

[G18-ABI-CONUS-BAND01]

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

[G18-ABI-CONUS-BAND02]

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.

[G18-ABI-CONUS-BAND03]

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.

[G18-ABI-CONUS-BAND04]

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.

[G18-ABI-CONUS-BAND05]

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.

[G18-ABI-CONUS-BAND06]

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.

[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. 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.

[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. 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.

[G18-ABI-CONUS-BAND08]

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.

[G18-ABI-CONUS-BAND08-VAPR]

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.

[G18-ABI-CONUS-BAND09]

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.

[G18-ABI-CONUS-BAND09-VAPR]

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.

[G18-ABI-CONUS-BAND10]

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.

[G18-ABI-CONUS-BAND10-VAPR]

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.

[G18-ABI-CONUS-BAND11]

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.

[G18-ABI-CONUS-BAND12]

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.

[G18-ABI-CONUS-BAND13]

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.

[G18-ABI-CONUS-BAND13-GRAD]

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.

[G18-ABI-CONUS-BAND14]

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.

[G18-ABI-CONUS-BAND15]

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.

[G18-ABI-CONUS-BAND16]

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.

[G18-ABI-FD-airmass]

G18-ABI-FD-airmass

[G18-ABI-FD-ash]

G18-ABI-FD-ash

[G18-ABI-FD-cloud-phase]

G18-ABI-FD-cloud-phase

[G18-ABI-FD-convection]

G18-ABI-FD-convection

[G18-ABI-FD-day-microphysics-abi]

G18-ABI-FD-day-microphysics-abi

[G18-ABI-FD-dust]

G18-ABI-FD-dust

[G18-ABI-FD-fire-temperature-awips]

G18-ABI-FD-fire-temperature-awips

[G18-ABI-FD-fog]

G18-ABI-FD-fog

[G18-ABI-FD-geo-color]

G18-ABI-FD-geo-color

[G18-ABI-FD-ir-sandwich]

G18-ABI-FD-ir-sandwich

[G18-ABI-FD-night-microphysics]

G18-ABI-FD-night-microphysics

[G18-ABI-FD-snow-fog]

G18-ABI-FD-snow-fog

[G18-ABI-FD-so2]

G18-ABI-FD-so2

[G18-ABI-FD-true-color]

View of G18-ABI-FD-geo-color

[G18-ABI-FD-water-vapors2]

G18-ABI-FD-water-vapors2

[G18-ABI-FD-BAND13-GRAD]

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.

[G18-ABI-FD-BAND01]

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.

[G18-ABI-FD-BAND02]

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.

[G18-ABI-FD-BAND03]

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.

[G18-ABI-FD-BAND04]

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.

[G18-ABI-FD-BAND05]

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.

[G18-ABI-FD-BAND06]

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.

[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. 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.

[G18-ABI-FD-BAND08]

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.

[G18-ABI-FD-BAND09]

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.

[G18-ABI-FD-BAND10]

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.

[G18-ABI-FD-BAND11]

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.

[G18-ABI-FD-BAND12]

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.

[G18-ABI-FD-BAND13]

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.

[G18-ABI-FD-BAND14]

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.

[G18-ABI-FD-BAND15]

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.

[G18-ABI-FD-BAND16]

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.

[GOES-WEST-L2-AOD-DQF0]

[G18-L2-CONUS-FLS-Thickness]

Cloud thickness: Estimate of the geometric thickness (cloud top - cloud base) of a single layer liquid water stratus cloud.

[G18-L2-CONUS-FLS-IFR]

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.

[G18-L2-CONUS-FLS-LIFR]

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.

[G18-L2-CONUS-FLS-MVFR]

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.

[G18-L2-FD-FLS-Thickness]

Cloud thickness: Estimate of the geometric thickness (cloud top - cloud base) of a single layer liquid water stratus cloud.

[G18-L2-FD-FLS-IFR]

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.

[G18-L2-FD-FLS-LIFR]

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.

[G18-L2-FD-FLS-MVFR]

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.

[G18-ABI-MESO1-airmass]

G18-ABI-MESO1-airmass

[G18-ABI-MESO1-ash]

G18-ABI-MESO1-ash

[G18-ABI-MESO1-cloud-phase]

G18-ABI-MESO1-cloud-phase

[G18-ABI-MESO1-convection]

G18-ABI-MESO1-convection

[G18-ABI-MESO1-day-microphysics-abi]

G18-ABI-MESO1-day-microphysics-abi

[G18-ABI-MESO1-dust]

G18-ABI-MESO1-dust

[G18-ABI-MESO1-fire-temperature-awips]

G18-ABI-MESO1-fire-temperature-awips

[G18-ABI-MESO1-fog]

G18-ABI-MESO1-fog

[G18-ABI-MESO1-geo-color]

G18-ABI-MESO1-geo-color

[G18-ABI-MESO1-ir-sandwich]

G18-ABI-MESO1-ir-sandwich

[G18-ABI-MESO1-night-microphysics]

G18-ABI-MESO1-night-microphysics

[G18-ABI-MESO1-snow-fog]

G18-ABI-MESO1-snow-fog

[G18-ABI-MESO1-so2]

G18-ABI-MESO1-so2

[G18-ABI-MESO1-true-color]

View of G18-ABI-MESO1-geo-color

[G18-ABI-MESO1-water-vapors2]

G18-ABI-MESO1-water-vapors2

[G18-ABI-MESO1-BAND01]

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

[G18-ABI-MESO1-BAND02]

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.

[G18-ABI-MESO1-BAND03]

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.

[G18-ABI-MESO1-BAND04]

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.

[G18-ABI-MESO1-BAND05]

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.

[G18-ABI-MESO1-BAND06]

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.

[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. 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.

[G18-ABI-MESO1-BAND08]

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

[G18-ABI-MESO1-BAND09]

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.

[G18-ABI-MESO1-BAND10]

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.

[G18-ABI-MESO1-BAND11]

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.

[G18-ABI-MESO1-BAND12]

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.

[G18-ABI-MESO1-BAND13]

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.

[G18-ABI-MESO1-BAND13-GRAD]

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.

[G18-ABI-MESO1-BAND14]

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.

[G18-ABI-MESO1-BAND15]

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.

[G18-ABI-MESO1-BAND16]

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.

[G18-ABI-MESO2-airmass]

G18-ABI-MESO2-airmass

[G18-ABI-MESO2-ash]

G18-ABI-MESO2-ash

[G18-ABI-MESO2-cloud-phase]

G18-ABI-MESO2-cloud-phase

[G18-ABI-MESO2-convection]

G18-ABI-MESO2-convection

[G18-ABI-MESO2-day-microphysics-abi]

G18-ABI-MESO2-day-microphysics-abi

[G18-ABI-MESO2-dust]

G18-ABI-MESO2-dust

[G18-ABI-MESO2-fire-temperature-awips]

G18-ABI-MESO2-fire-temperature-awips

[G18-ABI-MESO2-fog]

G18-ABI-MESO2-fog

[G18-ABI-MESO2-geo-color]

G18-ABI-MESO2-geo-color

[G18-ABI-MESO2-ir-sandwich]

G18-ABI-MESO2-ir-sandwich

[G18-ABI-MESO2-night-microphysics]

G18-ABI-MESO2-night-microphysics

[G18-ABI-MESO2-snow-fog]

G18-ABI-MESO2-snow-fog

[G18-ABI-MESO2-so2]

G18-ABI-MESO2-so2

[G18-ABI-MESO2-true-color]

View of G18-ABI-MESO2-geo-color

[G18-ABI-MESO2-water-vapors2]

G18-ABI-MESO2-water-vapors2

[G18-ABI-MESO2-BAND01]

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

[G18-ABI-MESO2-BAND02]

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.

[G18-ABI-MESO2-BAND03]

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.

[G18-ABI-MESO2-BAND04]

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.

[G18-ABI-MESO2-BAND05]

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.

[G18-ABI-MESO2-BAND06]

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.

[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. 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.

[G18-ABI-MESO2-BAND08]

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

[G18-ABI-MESO2-BAND09]

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.

[G18-ABI-MESO2-BAND10]

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.

[G18-ABI-MESO2-BAND11]

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.

[G18-ABI-MESO2-BAND12]

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.

[G18-ABI-MESO2-BAND13]

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.

[G18-ABI-MESO2-BAND13-GRAD]

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.

[G18-ABI-MESO2-BAND14]

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.

[G18-ABI-MESO2-BAND15]

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.

[G18-ABI-MESO2-BAND16]

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.

[G18-ABI-CONUS-airmass]

G18-ABI-CONUS-airmass

[G18-ABI-CONUS-ash]

G18-ABI-CONUS-ash

[G18-ABI-CONUS-cloud-phase]

G18-ABI-CONUS-cloud-phase

[G18-ABI-CONUS-convection]

G18-ABI-CONUS-convection

[G18-ABI-CONUS-day-microphysics-abi]

G18-ABI-CONUS-day-microphysics-abi

[G18-ABI-CONUS-dust]

G18-ABI-CONUS-dust

[G18-ABI-CONUS-fire-temperature-awips]

G18-ABI-CONUS-fire-temperature-awips

[G18-ABI-CONUS-fog]

G18-ABI-CONUS-fog

[G18-ABI-CONUS-geo-color]

G18-ABI-CONUS-geo-color

[G18-ABI-CONUS-ir-sandwich]

G18-ABI-CONUS-ir-sandwich

[G18-ABI-CONUS-night-microphysics]

G18-ABI-CONUS-night-microphysics

[G18-ABI-CONUS-snow-fog]

G18-ABI-CONUS-snow-fog

[G18-ABI-CONUS-so2]

G18-ABI-CONUS-so2

[G18-ABI-CONUS-true-color]

View of G18-ABI-CONUS-geo-color

[G18-ABI-CONUS-water-vapors2]

G18-ABI-CONUS-water-vapors2

[G18-ABI-FD-airmass]

G18-ABI-FD-airmass

[G18-ABI-FD-ash]

G18-ABI-FD-ash

[G18-ABI-FD-cloud-phase]

G18-ABI-FD-cloud-phase

[G18-ABI-FD-convection]

G18-ABI-FD-convection

[G18-ABI-FD-day-microphysics-abi]

G18-ABI-FD-day-microphysics-abi

[G18-ABI-FD-dust]

G18-ABI-FD-dust

[G18-ABI-FD-fire-temperature-awips]

G18-ABI-FD-fire-temperature-awips

[G18-ABI-FD-fog]

G18-ABI-FD-fog

[G18-ABI-FD-geo-color]

G18-ABI-FD-geo-color

[G18-ABI-FD-ir-sandwich]

G18-ABI-FD-ir-sandwich

[G18-ABI-FD-night-microphysics]

G18-ABI-FD-night-microphysics

[G18-ABI-FD-snow-fog]

G18-ABI-FD-snow-fog

[G18-ABI-FD-so2]

G18-ABI-FD-so2

[G18-ABI-FD-true-color]

View of G18-ABI-FD-geo-color

[G18-ABI-FD-water-vapors2]

G18-ABI-FD-water-vapors2

[G18-ABI-MESO1-airmass]

G18-ABI-MESO1-airmass

[G18-ABI-MESO1-ash]

G18-ABI-MESO1-ash

[G18-ABI-MESO1-cloud-phase]

G18-ABI-MESO1-cloud-phase

[G18-ABI-MESO1-convection]

G18-ABI-MESO1-convection

[G18-ABI-MESO1-day-microphysics-abi]

G18-ABI-MESO1-day-microphysics-abi

[G18-ABI-MESO1-dust]

G18-ABI-MESO1-dust

[G18-ABI-MESO1-fire-temperature-awips]

G18-ABI-MESO1-fire-temperature-awips

[G18-ABI-MESO1-fog]

G18-ABI-MESO1-fog

[G18-ABI-MESO1-geo-color]

G18-ABI-MESO1-geo-color

[G18-ABI-MESO1-ir-sandwich]

G18-ABI-MESO1-ir-sandwich

[G18-ABI-MESO1-night-microphysics]

G18-ABI-MESO1-night-microphysics

[G18-ABI-MESO1-snow-fog]

G18-ABI-MESO1-snow-fog

[G18-ABI-MESO1-so2]

G18-ABI-MESO1-so2

[G18-ABI-MESO1-true-color]

View of G18-ABI-MESO1-geo-color

[G18-ABI-MESO1-water-vapors2]

G18-ABI-MESO1-water-vapors2

[G18-ABI-MESO2-airmass]

G18-ABI-MESO2-airmass

[G18-ABI-MESO2-ash]

G18-ABI-MESO2-ash

[G18-ABI-MESO2-cloud-phase]

G18-ABI-MESO2-cloud-phase

[G18-ABI-MESO2-convection]

G18-ABI-MESO2-convection

[G18-ABI-MESO2-day-microphysics-abi]

G18-ABI-MESO2-day-microphysics-abi

[G18-ABI-MESO2-dust]

G18-ABI-MESO2-dust

[G18-ABI-MESO2-fire-temperature-awips]

G18-ABI-MESO2-fire-temperature-awips

[G18-ABI-MESO2-fog]

G18-ABI-MESO2-fog

[G18-ABI-MESO2-geo-color]

G18-ABI-MESO2-geo-color

[G18-ABI-MESO2-ir-sandwich]

G18-ABI-MESO2-ir-sandwich

[G18-ABI-MESO2-night-microphysics]

G18-ABI-MESO2-night-microphysics

[G18-ABI-MESO2-snow-fog]

G18-ABI-MESO2-snow-fog

[G18-ABI-MESO2-so2]

G18-ABI-MESO2-so2

[G18-ABI-MESO2-true-color]

View of G18-ABI-MESO2-geo-color

[G18-ABI-MESO2-water-vapors2]

G18-ABI-MESO2-water-vapors2

[nucaps-grid-ght-300]

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.

[nucaps-grid-ght-500]

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.

[nucaps-grid-ght-700]