ID: SNOWDEPTH24

24hr SnowDepth (in)

ID: SNOWFALL24

24hr SnowFall (in)

ID: wi-counties

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

ID: NAIPWI2015fp

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

ID: CSIR

Southern Africa Wild Fire targets are fires detected by the MODIS sensor on the Terra and Aqua satellites. It is produced by CSIR (The Council for Scientific and Industrial Research) and updated every 60 minutes to include any new information.

ID: AQUA-AER

MODIS: AQUA Aerosol Optical Depth (ta)

ID: aquafalsecolor

CIMSS-MODIS Satellite False Color (Aqua)

ID: POESNAV-AQUA

ID: NppDynamicDnb

Proof of concept VIIRS Day/Night Band imagery for the 2019 fires in New South Wales and Queensland, Australia. Source: NOAA CLASS.

ID: NppHnccDnb

Proof of concept VIIRS Day/Night Band imagery for the 2019 fires in New South Wales and Queensland, Australia. Source: NOAA CLASS.

ID: BOM-Root-Zone-Soil-Moisture

Australian Bureau of Meteorology: Root Zone Soil Moisture is the sum of water in the AWRA-L Upper and Lower soil layers and represents the percentage of available water content in the top 1 m of the soil profile. The maximum storage within the soil layer is calculated from the depth of the soil and the relative soil water storage capacity. More info at the link below.

ID: NESDIS-BTPWgps

NESDIS-BTPWgps

ID: NESDIS-BTPWpct

NESDIS-BTPWpct

ID: clad

Estimate of 2005 algae extent along coastal Lake Michigan.

ID: CIMSS-CTCtargets

CIMSS-Cloud Top Cooling targets

ID: c2accum1dy

This satellite-derived precipitation product represents global 1-day accumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree lat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include various rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS).

ID: c2accum1hr

This satellite-derived precipitation product represents global 1-hour accumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree lat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include various rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS).

ID: c2accum7dy

This satellite-derived precipitation product represents global 7-day accumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree lat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include various rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS).

ID: ICE-COMP-AP

ID: ICE-COMP-MB

ID: ICE-COMP-NC

ID: ICE-COMP-NE

ID: SPC-ConvOutlook-CATG

SPC Convective Outlook - Categorical

ID: SPC-ConvOutlook-CATG-cmap

View of SPC-ConvOutlook-CATG

ID: SPCcoday1

Convective Outlook Day1 (Category) id=SPCcoday1

ID: SPCcoday2

Convective Outlook Day2 (Category)

ID: SPCcoday3

Convective Outlook Day3 (Categorical)

ID: cspp-flood

Daily direct broadcast-produced flood products created by latest alpha version of the CSPP VIIRS Flood Detection software.

ID: cspp-flood-nocloud

An alternate view of the CSPP VIIRS Flood Detection product with cloud & cloud shadow pixels set to transparent.

ID: cspp-viirs-flood-globally-nocloud

Global flood products created from Suomi-NPP SDRs by the latest alpha version of the CSPP VIIRS Flood Detection software. This product has cloudy & cloud shadow pixels removed so that, in cases where granules overlap, only cloud free data points are displayed.

ID: Current-Fires

Current large fire incidents in the USA and Canada as tracked by USDA Forest Service

ID: DNB-ClearView

DNB-ClearView

ID: dnb-monthly-nightlights

ID: Earthquake-mag

Earthquake Magnitude (Past 24hr)

ID: Eclipse

Eclipse Path

ID: ERTAday1

WPC Excessive Rainfall Threat Area Day1: 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. WPC 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

ID: ERTAday2

WPC Excessive Rainfall Threat Area Day2: 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. WPC 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

ID: ERTAday3

WPC Excessive Rainfall Threat Area Day3: 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. WPC 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

ID: ERTAfcast

WPC Excessive Rainfall Threat Area Forecast. 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. WPC 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

ID: WPC-ExcessiveRain

WPC-ExcessiveRain

ID: ZAFDI

MODIS Fire Danger Index South Africa by CIMSS-DBCRAS

ID: CONUSFDI

MODIS Fire Danger Index (FDI) ConUS by CIMSS-DBCRAS

ID: REDFLAG

REDFLAG

ID: 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

ID: FIRMS-VIIRS-Alaska-ActiveFires

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.

ID: FIRMS-VIIRS-Australia-ActiveFires

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.

ID: FIRMS-VIIRS-Central-America-ActiveFires

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.

ID: FIRMS-VIIRS-Canada-ActiveFires

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.

ID: FIRMS-VIIRS-ConUS-Hawaii-ActiveFires

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.

ID: FIRMS-VIIRS-Europe-ActiveFires

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.

ID: FIRMS-VIIRS-Global-ActiveFires-Filtered

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.

ID: FIRMS-VIIRS-Global-ActiveFires-Raster

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 otherscience applications requiring improved fire mapping fidelity.

ID: FIRMS-VIIRS-Northern-Africa-ActiveFires

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.

ID: FIRMS-VIIRS-Russia-ActiveFires

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.

ID: FIRMS-VIIRS-Southern-Africa-ActiveFires

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.

ID: FIRMS-VIIRS-South-America-ActiveFires

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.

ID: FIRMS-VIIRS-SAsia-ActiveFires

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.

ID: FIRMS-VIIRS-SEAsia-ActiveFires

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 otherscience applications requiring improved fire mapping fidelity.

ID: 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.

ID: SPC-FireOutlook-CATG

SPC Fire Weather Outlook - Categorical

ID: SPCfwday1

Fire Weather Outlook Day1 (Category)

ID: SPCfwday2

Fire Weather Outlook Day2 (Category)

ID: WFLASH

Flash Flood Hazards

ID: WWFLOOD

Flood Watches and Warnings

ID: FOP

WPC FLood Outlook Product

ID: FLOODWARN

Flood Warning Polygons

ID: XFLOODWARN

XFLOODWARN

ID: 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.

ID: WFOG

Fog Hazards

ID: WPC-picezgt25

WPC-picezgt25

ID: WPC-picez24gep01

WPC-picez24gep01

ID: WPC-picez24gep10

WPC-picez24gep10

ID: WPC-picez24gep25

WPC-picez24gep25

ID: WPC-picez24gep50

WPC-picez24gep50

ID: WPC-picez24ge1

WPC-picez24ge1

ID: Fronts

NCEP Frontal Analysis: fronts and troughs

ID: FlashAvgArea

GOES-16 GLM average flash area 3-min average over flash extent density footprint

ID: FlashCentroidDensity

ID: FlashExtentDensity

GOES-16 flash extent density 3-min accumulation of footprint of all observed flashes

ID: glmgroupdensity-west

ID: TotalEnergy

GOES-16 total optical energy 3-min accumulation over flash extent density footprint.

ID: VIIRS-MASK-54000x27000

VIIRS Night Global Black Marble by NASA

ID: globalir

This product is a global composite of imagery from multiple satellites. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

ID: globalir-avn

This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

ID: globalir-bd

This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

ID: globalir-funk

This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

ID: globalir-nhc

This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

ID: globalir-rr

This product is based on a statistical relationship between cloud top temperature and observed rain rate. It is derived every hour (at about 35-minutes after the hour UTC) using the global IR composite produced by the SSEC Data Center. While it shows the most current imagery, shifting occurs along composite seams.

ID: globalir-ott

This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

ID: NightLightsColored

Global Night Lights (enhanced)

ID: globalvis

This product is a global composite of imagery from multiple satellites. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

ID: globalvis-tsp

This view is based on the global Visible composite product in which night time regions are rendered transparent. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

ID: global1kmvis

This product is a 15-minute snapshot of a global composite of imagery from multiple satellites. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

ID: global1kmvisfull

ID: globalwv

This product is a global composite of imagery from multiple satellites. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

ID: globalwv-grad

This product is an enhanced view of the global Water Vapor composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

ID: GLMOBJ

ID: GOES-W-CONUS-IR

GOES 15 ConUS IR

ID: GOES-W-CONUS-LWIR

GOES 15 ConUS LWIR

ID: GOES-W-CONUS-NIR

GOES 15 ConUS NIR

ID: GOES-W-CONUS-VIS

GOES 15 ConUS VIS

ID: GOES-W-CONUS-WV

GOES 15 ConUS WV

ID: GOES-W-FD-IR

GOES 15 Full Disk IR (Infrared)

ID: GOES-W-FD-LWIR

GOES 15 Full Disk LWIR (Long Wave Infrared)

ID: GOES-W-FD-NIR

GOES 15 Full Disk NIR (Near Infrared)

ID: GOES-W-FD-VIS

GOES 15 Full Disk VIS (Visible)

ID: GOES-W-FD-WV

GOES 15 Full Disk WV (Water Vapor)

ID: GOES17-ABI-CONUS-B07-ARC-FIRES

ID: GOES17-ABI-CONUS-B07-ARC-ENH

View of GOES17-ABI-CONUS-B07-ARCHIVE

ID: cimssdpicapeli

CIMSS-DPI Convective Available Potential Energy (Li et al. 2008)

ID: G16-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.

ID: G16-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.

ID: G16-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.

ID: G16-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.

ID: G16-ABI-CONUS-BAND07D

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.

ID: G16-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.

ID: G16-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.

ID: G16-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.

ID: G16-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.

ID: G16-ABI-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.

ID: GOES-16SandwichCONUS

A composite image of the 10.35 um IR brightness temperatures with the 0.64 micron normalized visible brightness during the day. Transitions to an IR image at night.

ID: G16-ABI-CONUS-TC

True Color Imagery gives an image that is approximately as you would see it from Outer Space. With ABI, the challenge of creating True Color arises from the the lack of a Green Band. The CIMSS Natural True Color product, approximates the green by combining Blue (0.47 µm), Red (0.64 µm) and ‘Veggie’ (0.86 µm) bands. The use of the Veggie band is important because it mimics the enhanced reflectivity present in the Green Band.

ID: G16-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.

ID: G16-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.

ID: G16-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.

ID: G16-ABI-FD-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 3.9μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.

ID: G16-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.

ID: G16-ABI-FD-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.

ID: G16-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.

ID: G16-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.

ID: G16-ABI-FD-TC

True Color Imagery gives an image that is approximately as you would see it from Outer Space. With ABI, the challenge of creating True Color arises from the the lack of a Green Band. The CIMSS Natural True Color product, approximates the green by combining Blue (0.47 µm), Red (0.64 µm) and ‘Veggie’ (0.86µm) bands. The use of the Veggie band is important because it mimics the enhanced reflectivity present in the Green Band.

ID: GOES-16-SA-Sandwich

A composite image of the 10.35 um IR brightness temperatures with the 0.64 micron normalized visible brightness temperatures during the day. Transitions to an IR image at night. Currently, this product only extends to 35 South.

ID: G16-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.

ID: G16-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.

ID: G16-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.

ID: G16-ABI-MESO1-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.

ID: G16-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.

ID: G16-ABI-MESO1-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.

ID: G16-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.

ID: G16-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.

ID: G16-ABI-MESO1-BAND13-RED

True Color Imagery gives an image that is approximately as you would see it from Outer Space. With ABI, the challenge of creating True Color arises from the the lack of a Green Band. The CIMSS Natural True Color product, approximates the green by combining Blue (0.47 µm), Red (0.64 µm) and ‘Veggie’ (0.86 µm) bands. The use of the Veggie band is important because it mimics the enhanced reflectivity present in the Green Band.

ID: GOES-16SandwichMESO1

A composite image of the 10.35 um IR A composite image of the 10.35 um IR brightness temperatures with the 0.64 micron normalized visible brightness during the day. Transitions to an IR image at night.

ID: G16-ABI-MESO1-TC

True Color Imagery gives an image that is approximately as you would see it from Outer Space. With ABI, the challenge of creating True Color arises from the the lack of a Green Band. The CIMSS Natural True Color product, approximates the green by combining Blue (0.47 µm), Red (0.64 µm) and ‘Veggie’ (0.86 µm) bands. The use of the Veggie band is important because it mimics the enhanced reflectivity present in the Green Band.

ID: G16-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.

ID: G16-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.

ID: G16-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.

ID: G16-ABI-MESO2-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.

ID: G16-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.

ID: G16-ABI-MESO2-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.

ID: G16-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.

ID: G16-ABI-MESO2-B13-CYAN

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.

ID: G16-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.

ID: GOES-16SandwichMESO2

NOTE: When MESO1 is in 30 second mode, MESO2 will not update. A composite image of the 10.35 um IR A composite image of the 10.35 um IR brightness temperatures with the 0.64 micron normalized visible brightness during the day. Transitions to an IR image at night.

ID: G16-ABI-MESO2-TC

True Color Imagery gives an image that is approximately as you would see it from Outer Space. With ABI, the challenge of creating True Color arises from the the lack of a Green Band. The CIMSS Natural True Color product, approximates the green by combining Blue (0.47 µm), Red (0.64 µm) and ‘Veggie’ (0.86 µm) bands. The use of the Veggie band is important because it mimics the enhanced reflectivity present in the Green Band.

ID: glmgroupdensity

The Geostationary Lightning Mapper, or GLM, on board Geostationary Operational Environmental Satellite– R Series spacecraft, is the first operational lightning mapper flown in geostationary orbit. GLM detects the light emitted by lightning at the tops of clouds day and night and collects information such as the frequency, location and extent of lightning discharges. The instrument measures total lightning, both in-cloud and cloud-to-ground, to aid in forecasting developing severe storms and a wide range of high-impact environmental phenomena including hailstorms, microburst winds, tornadoes, hurricanes, ash oods, snowstorms and res.

ID: glmgrouppoints

glmgrouppoints

ID: conusiravn

GOES IR Aviation

ID: conusirbd

GOES IR Dvorak

ID: conusirfunk

GOES IR Funk Top

ID: conusirott

GOES IR Overshooting Tops

ID: conusirnhc

GOES IR Rainbow

ID: cimssdpilili

GOES-DPI Lifted Index (Li et al. 2008)

ID: cimssdpiozli

GOES-DPI Ozone (Li etal 2008)

ID: cimssdpipwli

CIMSS-DPI Precipitable Water (mm)

ID: G17-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.

ID: G17-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.

ID: G17-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.

ID: G17-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.

ID: G17-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.

ID: G17-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.

ID: G17-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.

ID: G17-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.

ID: G17-ABI-CONUS-TC

True Color Imagery gives an image that is approximately as you would see it from Outer Space. With ABI, the challenge of creating True Color arises from the the lack of a Green Band. The CIMSS Natural True Color product, approximates the green by combining Blue (0.47 µm), Red (0.64 µm) and ‘Veggie’ (0.86 µm) bands. The use of the Veggie band is important because it mimics the enhanced reflectivity present in the Green Band.

ID: G17-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.

ID: G17-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.

ID: G17-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.

ID: G17-ABI-FD-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.

ID: G17-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.

ID: G17-ABI-FD-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.

ID: G17-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.

ID: G17-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.

ID: G17-ABI-FD-TC

True Color Imagery gives an image that is approximately as you would see it from Outer Space. With ABI, the challenge of creating True Color arises from the the lack of a Green Band. The CIMSS Natural True Color product, approximates the green by combining Blue (0.47 µm), Red (0.64 µm) and ‘Veggie’ (0.86 µm) bands. The use of the Veggie band is important because it mimics the enhanced reflectivity present in the Green Band.

ID: G17-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.

ID: G17-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.

ID: G17-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.

ID: G17-ABI-MESO1-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.

ID: G17-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.

ID: G17-ABI-MESO1-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.

ID: G17-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.

ID: G17-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.

ID: G17-ABI-MESO1-BAND13-GREEN

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.

ID: G17-ABI-MESO1-TC

True Color Imagery gives an image that is approximately as you would see it from Outer Space. With ABI, the challenge of creating True Color arises from the the lack of a Green Band. The CIMSS Natural True Color product, approximates the green by combining Blue (0.47 µm), Red (0.64 µm) and ‘Veggie’ (0.86 µm) bands. The use of the Veggie band is important because it mimics the enhanced reflectivity present in the Green Band.

ID: G17-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.

ID: G17-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.

ID: G17-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.

ID: G17-ABI-MESO2-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.

ID: G17-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.

ID: G17-ABI-MESO2-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.

ID: G17-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.

ID: G17-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.

ID: G17-ABI-MESO2-BAND13-YELLOW

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.

ID: G17-ABI-MESO2-TC

True Color Imagery gives an image that is approximately as you would see it from Outer Space. With ABI, the challenge of creating True Color arises from the the lack of a Green Band. The CIMSS Natural True Color product, approximates the green by combining Blue (0.47 µm), Red (0.64 µm) and ‘Veggie’ (0.86 µm) bands. The use of the Veggie band is important because it mimics the enhanced reflectivity present in the Green Band.

ID: GLERL-GLSEAimage

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

ID: SPChaday1

Hail Outlook Day1 (%)

ID: HIMAWARI-B03

Himawari AHI Full Disk B03 Hi-Res "Red" Visible

ID: HIMAWARI-B04

Himawari AHI Full Disk B04 "Veggie"

ID: HIMAWARI-B07

Himawari AHI Full Disk B07 "Fire"

ID: HIMAWARI-B07-FIRE

View of HIMAWARI-B07

ID: HIMAWARI-B09

Himawari AHI Full Disk B09 Mid-level Water Vapor

ID: HIMAWARI-B09-VAPR

View of HIMAWARI-09

ID: HIMAWARI-B13

Himawari AHI Full Disk B13 "Clean" Infrared

ID: HIMAWARI-B13-GRAD

View of HIMAWARI-B13

ID: H-DayConvectiveStorm-cve

Himawari AHI Full Disk Day Convective Storm (ave)

ID: H-DayMicrophysics-dms

Himawari AHI Full Disk Day Microphysics (dms)

ID: H-Dust-dst

Himawari AHI Full Disk Dust (dst)

ID: H-NaturalColor-dnc

Himawari AHI Full Disk Natural Color (dnc)

ID: H-NightMicrophysics-ngt

Himawari AHI Full Disk Night Microphysics (nms)

ID: H-24hrAirMass-arm

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 frontal boundaries and identify high-, mid-, and low- level clouds.

ID: H-SnowFog-dsl

Himawari AHI Full Disk Snow and Fog (dsl)

ID: H-TrueColor-wgt

Himawari AHI Full Disk True Color (wgt)

ID: HIMAWARI-JP-B03

Himawari AHI Japan B03 Hi-Res "Red" Visible

ID: HIMAWARI-JP-B07

Himawari AHI Japan Bo7 "Fire"

ID: HIMAWARI-JP-B07-FIRE

View of HIMAWARI-JP-B07

ID: HIMAWARI-JP-B09

Himawari AHI Japan B09 Mid-level Water Vapor

ID: HIMAWARI-JP-B09-VAPR

View of HIMAWARI-JP-B09

ID: HIMAWARI-JP-B14

Himawari AHI Japan B14 Infrared

ID: HIMAWARI-JP-B14-GRAD

View of HIMAWARI-JP-B14

ID: HIMAWARI-T1-B03

Himawrai AHI Target B03 Hi-Res "Red" Visible

ID: HIMAWARI-T1-B07

Himawari AHI Target B07 "Fire"

ID: HIMAWARI-T1-B07-FIRE

View of HIMAWARI-T1-B07

ID: HIMAWARI-T1-B14

Himawari AHI Target B14 Infrared

ID: HIMAWARI-T1-B14-GRAD

Himawari AHI Target B14 Infrared enhanced

ID: HIMAWARI-T1-B09

Himawari AHI Target Mid-level Water Vapor

ID: HIMAWARI-T1-B09-VAPR

Himawari AHI Target Mid-level Water Vapor enhanced

ID: HRRR-AK-smoke-surface

NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Surface Smoke forecast model, uses VIIRS inputs.

ID: HRRR-AK-smoke-column

NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Vertically Integrated Smoke forecast model, uses VIIRS inputs.

ID: HRR-CONUS-FZRN-SFC

HRRR ConUS Latest Freezing MASK

ID: HRR-CONUS-ICEP-SFC

HRRR ConUS Latest Ice Mask

ID: HRR-CONUS-PCP-LATEST

View of HRR-CONUS-PCP-SFC

ID: HRR-CONUS-RAIN-SFC

HRRR ConUS Latest Rain Mask

ID: HRR-CONUS-PCP-SFC

HRR-CONUS-PCP-SFC

ID: HRR-CONUS-RADAR-LATEST

View of HRR-CONUS-PCP-SFC

ID: HRR-CONUS-SNOD-SFC

ID: HRR-CONUS-SNOW-SFC

HRRR ConUS Latest Snow Mask

ID: 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.

ID: 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.

ID: NESDIS-GHE-HourlyRainfall

The HE algorithm uses infrared (IR) brightness temperatures to identify regions of rainfall and retrieve rainfall rate, while using National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model fields to account for the effects of moisture availability, evaporation, orographic modulation, and thermodynamic profile effects. Estimates of rainfall from satellites can provide critical rainfall information in regions where data from gauges or radar are unavailable or unreliable, such as over oceans or sparsely populated regions.

ID: AIRMET-ICE

AIRMET Icing Advisory

ID: ICPRadM1

Intense Convection Probability -- GOES East Mesoscale 1

ID: ICPRadM2

Intense Convection Probability -- GOES East Mesoscale 2

ID: AIRMET-IFR

AIRMET-IFR Advisory

ID: madisonir

Infrared 6 inch Imagery of Madison

ID: InsolationEast

This product displays daily-integrated solar radiation estimates (MJ-m-2) for the previous calendar day derived using half-hourly imagery data from GOES-EAST and GOES-WEST geostationary satellites and a simple radiative transfer model of the atmosphere. GOES-EAST data are used for the eastern half of the U.S., GOES-WEST for the western half and both these results are displayed upon initial invocation of this insolation page. Raw data values scaled by 100. Right-click on a pixel and select "probe" to view values.

ID: Insolation

This product displays daily-integrated solar radiation estimates (MJ-m-2) for the previous calendar day derived using half-hourly imagery data from GOES-EAST and GOES-WEST geostationary satellites and a simple radiative transfer model of the atmosphere. GOES-EAST data are used for the eastern half of the U.S., GOES-WEST for the western half and both these results are displayed upon initial invocation of this insolation page. Raw data values scaled by 100. Right-click on a pixel and select "probe" to view values.

ID: ICP

Deep learning model that predicts where "intense" convection" is present, based on features that humans associate with intense convection.

ID: AMV-ULhigh

AMV: Upper Level IR/WV (100-250mb)

ID: AMV-ULmid

AMV: Upper Level IR/WV (251-350mb)

ID: AMV-ULlow

AMV: Upper Level IR/WV (351-500mb)

ID: AMV-LLhigh

AMV: 400-599mb Low Level IR winds

ID: AMV-LLmid

AMV: Lower Level IR (600-799mb)

ID: AMV-LLlow

AMV: Lower Level IR (800-950mb)

ID: glofsnowcast

Water currents speed and direction of the top level in Lake Michigan from The Great Lakes Operational Forecast System (GLOFS), uint: m/s

ID: POESNAV-LSAT7

ID: POESNAV-LSAT8

ID: landsat-example

ID: 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.

ID: 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.

ID: lsat8-llook-fc-daily

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ID: lsat8-llook-fc-daily

View of lsat8-llook-fc

ID: lsat8-llook-fc

LandsatLook images are full resolution files derived from Landsat Level 1 data products. The images are compressed and stretched to create an image optimized for image selection and visual interpretation. "Natural Color" is a false color composite that minimizes haze by combining bands 6 (1.57 - 1.65µ), 5 (0.85 - 0.88µ), and 4 (0.64 - 0.67µ) as Red, Green, and Blue.

ID: lsat8-llook-fc

LandsatLook images are full resolution files derived from Landsat Level 1 data products. The images are compressed and stretched to create an image optimized for image selection and visual interpretation. "Natural Color" is a false color composite that minimizes haze by combining bands 6 (1.57 - 1.65µ), 5 (0.85 - 0.88µ), and 4 (0.64 - 0.67µ) as Red, Green, and Blue.

ID: lsat8-llook-tir-daily

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ID: lsat8-llook-tir-daily

View of lsat8-llook-tir

ID: lsat8-llook-tir

The LandsatLook "Thermal" image is a one-band gray scale .jpg image made to display thermal properties of the scene. The image is made from band 10 (10.60 - 11.19µ) with darker values representing colder temperatures.

ID: lsat8-llook-tir

The LandsatLook "Thermal" image is a one-band gray scale .jpg image made to display thermal properties of the scene. The image is made from band 10 (10.60 - 11.19µ) with darker values representing colder temperatures.

ID: wrs2-scenes

ID: wrs2-scenes

ID: LARC-CloudPhase-GOESE-8km

LaRC Cloud Phase GOESE 8km

ID: LARC-CloudPhase-GOESW-8km

LaRC Cloud Phase GOESW 8km

ID: LARC-CloudPhase-HM-8km

LaRC Cloud Phase HM 8km

ID: LARC-CloudPhase-MET8-9km

LaRC Cloud Phase MET8 9km

ID: LARC-CloudPhase-MSG-9km

LaRC Cloud Phase MSG 9km

ID: LARC-CloudZtop-GOESE-8km

LaRC Cloud Top Height GOESE 8km

ID: LARC-CloudZtop-GOESW-8km

LaRC Cloud Top Height GOESW 8km

ID: LARC-CloudZtop-HM-8km

LaRC Cloud Top Height HM 8km

ID: LARC-CloudZtop-MET8-9km

LaRC Cloud Top Height MET8 9km

ID: LARC-CloudZtop-MSG-9km

LaRC Cloud Top Height MSG 9km

ID: HighLow

NCEP Frontal Analysis: Highs and Lows

ID: MADIS-dewt

The MADIS Surface Dewpoint uses a 2-dimensional boxcar spatial convolution to smooth hourly average surface observations from the NCEP Meteorological Assimilation Data Ingest System (MADIS) to a grid resolution of 0.7 degree latitude/longitude. The source data is obtained in near-real time from https://madis.ncep.noaa.gov/.

ID: Max-Arctic-Sea-Ice-Extent

This product represents a single date selected by researchers to show the maximum extent of Arctic sea ice in recent years. These data are from the U.S. National Ice Center (NIC), a multi-agency center operated by the United States Navy, the National Oceanic and Atmospheric Administration, and the United States Coast Guard.

ID: mean-snow-cover-1988-2017

mean-snow-cover-1988-2017

ID: SSEC-METAR

Global METAR

ID: Met8-SEVIRI-FD-BAND01

VIS0.6: Known from the Advanced Very High Resolution Radiometer (AVHRR) of the polar-orbiting NOAA satellites. It is essential for cloud detection, cloud tracking, scene identification, aerosol, and land surface and veg­ etation monitoring.

ID: Met8-SEVIRI-FD-BAND04

IR3.9: Known from AVHRR. Primarily for low cloud and fog detection (Eyre et al. 1984; Lee et al. 1997). Also supports measurement of land and sea surface temperature at night and increases the low- level wind coverage from cloud tracking (Velden et al. 2001). For MSG, the spectral band has been broadened to longer wavelengths to improve signal-to-noise ratio.

ID: Met8-SEVIRI-FD-BAND05

WV6.2: Continues mission of Meteosat broadband water vapor channel for ob­serving water vapor and winds. Enhanced to two channels peaking at different levels in the tropo­ sphere. Also supports height allocation of semitransparent clouds (Nieman et al. 1993; Schmetz et al. 1993).

ID: Met8-SEVIRI-FD-BAND09

IR10.8: Well-known split window channel (e.g., AVHRR). Essential for measur­ ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata 1989).

ID: Met8-SEVIRI-FD-BAND09-enh

IR10.8: Well-known split window channel (e.g., AVHRR). Essential for measur­ ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata 1989).

ID: Met8-SEVIRI-HRV-BAND12

The high-resolution visible (HRV) channel covers half of the full disk in the east–west direction and a full disk in the north–south direction (see Fig. 3). The high-resolution visible channel has a spatial resolution of 1.67 km, as the oversampling factor is 1.67 the sam­pling distance is 1 km at nadir.

ID: Met11-SEVIRI-FD-BAND01

VIS0.6: Known from the Advanced Very High Resolution Radiometer (AVHRR) of the polar-orbiting NOAA satellites. It is essential for cloud detection, cloud tracking, scene identification, aerosol, and land surface and veg­ etation monitoring.

ID: Met11-SEVIRI-FD-BAND04

IR3.9: Known from AVHRR. Primarily for low cloud and fog detection (Eyre et al. 1984; Lee et al. 1997). Also supports measurement of land and sea surface temperature at night and increases the low- level wind coverage from cloud tracking (Velden et al. 2001). For MSG, the spectral band has been broadened to longer wavelengths to improve signal-to-noise ratio.

ID: Met11-SEVIRI-FD-BAND05

WV6.2: Continues mission of Meteosat broadband water vapor channel for ob­serving water vapor and winds. Enhanced to two channels peaking at different levels in the tropo­ sphere. Also supports height allocation of semitransparent clouds (Nieman et al. 1993; Schmetz et al. 1993).

ID: Met11-SEVIRI-FD-BAND09

IR10.8: Well-known split window channel (e.g., AVHRR). Essential for measur­ ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata 1989).

ID: Met11-SEVIRI-FD-BAND09-enh

IR10.8: Well-known split window channel (e.g., AVHRR). Essential for measur­ ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata 1989).

ID: Met11-SEVIRI-HRV-BAND12

The high-resolution visible (HRV) channel covers half of the full disk in the east–west direction and a full disk in the north–south direction (see Fig. 3). The high-resolution visible channel has a spatial resolution of 1.67 km, as the oversampling factor is 1.67 the sam­pling distance is 1 km at nadir.

ID: ROADS-IADOT

Conditions are updated every 10 minutes during the winter season (October 15 to April 15) and on an as-needed basis during the non-winter months. Layer and service is maintained by the Iowa DOT GIS team on behalf of the Office of Traffic Operations. This data is provided as is through this value added REST service. All conditions have been remapped to the best of our ability to meet the condition reporting criteria as defined by the Iowa DOT. Some discrepancies may appear. This data service should only be used for reference only. For the most accurate information, please utilize the authoritative state 511 sites below. State 511 Sites 511 Vendor Disclaimers North Dakota Iteris The data is provided as is and without liability from the North Dakota Department of Transportation (NDDOT). The NDDOT does not guarantee this data to be free from errors, or inaccuracies, and disclaims any responsibility or liability for interpretations or decisions based on this data.

ID: MIMICTPW2

MIMIC-TPW2 is an experimental global product of total precipitable water (TPW), using morphological compositing of the MIRS retrieval from several available operational microwave-frequency sensors. MIMIC stands for "Morphed Integrated Microwave Imagery at CIMSS." The specific technique used here was initially described in a 2010 paper by Wimmers and Velden. This Version 2 is developed from an older method (still running in real-time) that uses simpler, but more limited TPW retrievals and advection calculations.

ID: MIRS-BT90

MIRS 90Ghz Brightness Temperature

ID: MIRS-RainRate

MIRS Rain Rate

ID: AIRMET-MTN

AIRMET-Mountain Obscured Advisory

ID: MERGEDREF

Multi-Radar/Multi-Sensor MergedReflectivityQCComposite

ID: NAIPWI

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

ID: NAIPWICIR

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

ID: NAM-CONUS-PRAT-SFC

NAM-CONUS-PRAT-SFC

ID: NEXRAD-Alaska

NEXRAD-Alaska

ID: nexrrain

NEXRAD CanAm base Reflectivity mask

ID: nexrphase

NEXRAD CanAm Precipitation Phase

ID: nexrhres

NEXRADConUS Hybrid Reflectivity mask

ID: nexrstorm

NEXRAD ConUS Storm Total Precipitation

ID: NEXRAD-Guam

NEXRAD Guam Base Reflectivity

ID: NEXRAD-Hawaii

NEXRAD Hawaii Base Reflectivity

ID: NEXRAD-PuertoRico

NEXRAD Puerto Rico Base Reflectivity

ID: POESNAV-N15

ID: POESNAV-N18

ID: POESNAV-N20

ID: j01-viirs-adaptive-dnb-daily

j01-viirs-adaptive-dnb-daily

ID: j01-viirs-i02-daily

j01-viirs-i02-daily

ID: j01-viirs-i05-daily

j01-viirs-i05-daily

ID: j01-viirs-i05-daily-tops

View of j01-viirs-i05-daily

ID: j01-viirs-false-color-daily

View of j01-viirs-false-color-swath

ID: j01-viirs-false-color-hourly

View of j01-viirs-false-color

ID: j01-viirs-false-color-swath

View of j01-viirs-false-color

ID: j01-viirs-adaptive-dnb

j01-viirs-adaptive-dnb

ID: j01-viirs-i02

j01-viirs-i02

ID: j01-viirs-i05

j01-viirs-i05

ID: j01-viirs-i05-tops

View of j01-viirs-i05

ID: j01-viirs-swath-fire-color

This image is made by on-the-fly combining VIIRS bands M11 (2.25um) as red, M7 (8.66um) as green, and M4 (5.55) as blue. Because the M11 shortwave infrared band is sensitive to bright fires, it highlights active especially hot fires in red while preserving a natural color appearance in the rest of the image.

ID: j01-viirs-swath-fire-temp

On-the-fly combination of bands 11, 10, 12.

ID: j01-viirs-true-color-daily

View of j01-viirs-true-color-swath

ID: j01-viirs-true-color-hourly

View of j01-viirs-true-color

ID: j01-viirs-true-color-swath

View of j01-viirs-true-color

ID: ROADS

NorthWest Winter Road Conditions (WRC) decoded from state DOT text.

ID: NUCAPS-MADIS-SBCAPE

The MADIS-NUCAPS Surface-Based CAPE merges hourly average surface observations from the NCEP Meteorological Assimilation Data Ingest System (MADIS) with NOAA NUCAPS soundings from the most recent overpass of operational meteorological satellites (SNPP, METOP, or NOAA-20). The SB-CAPE is computed using the SHARPYpy software derived from software used by the NWS Storm Prediction Center (SPC). The satellite data are obtained using the SSEC direct broadcast antennae, processed using CSPP software in near-real time, and displayed in near-real time using SSEC"s RealEarth.

ID: NUCAPS-MADIS-MLCAPE

NUCAPS-MADIS-MLCAPE

ID: NUCAPS-MADIS-MLCIN

NUCAPS-MADIS-MLCIN

ID: NUCAPS-MADIS-MLLI

NUCAPS-MADIS-MLLI

ID: MADIS-NUCAPS-Surface-CAPE

The MADIS-NUCAPS Surface-Based CAPE merges hourly average surface observations from the NCEP Meteorological Assimilation Data Ingest System (MADIS) with NOAA NUCAPS soundings from the most recent overpass of operational meteorological satellites (SNPP, METOP, or NOAA-20). The SB-CAPE is computed using the SHARPYpy software derived from software used by the NWS Storm Prediction Center (SPC). The satellite data are obtained using the SSEC direct broadcast antennae, processed using CSPP software in near-real time, and displayed in near-real time using SSEC"s RealEarth.

ID: NUCAPS-MADIS-SBCIN

NUCAPS-MADIS-SBCIN

ID: NUCAPS-MADIS-SBLI

NUCAPS-MADIS-SBLI

ID: NUCAPS-CAA-temp-180mb

ID: NUCAPS-CAA-temp-200mb

ID: NUCAPS-CAA-temp-235mb

ID: NUCAPS-CAA-temp-260mb

ID: NUCAPS-CAA-temp-286mb

ID: NWS-AK-TPCP-1DAY

NWS-AK-TPCP-1DAY

ID: NWS-CONUS-TPCP-1DAY

NWS-CONUS-TPCP-1DAY

ID: NWSCWA

NWS County Warning Areas

ID: NWSWARNS12Z12Z

NWSWARNS12Z12Z (Severe and Tornado. No SVSs)

ID: CIMSS-OTtargets

Cloud OverShooting Tops targets

ID: PIREP

PIREP

ID: PLTGGOESEastRadC

Probability of lightning in the next 60 min

ID: PLTGGOESEastRadM1

Probability of lightning in the next 60 min

ID: PLTGGOESEastRadM2

Probability of lightning in the next 60 min

ID: PLTGGOESWestRadC

ID: PNPOINTALL

PNPOINTALL

ID: PNTRACKALL

PNTRACKALL

ID: PQPF6hr

WPC 6hr Probabilistic Precip PQPF .01in (%) Purpose – The probabilistic quantitative precipitation forecast (PQPF) guidance is used by forecasters and hydrologists to determine the probability of any rainfall amount at a given location. The PQPF can be used to assist forecasters in the issuance of flash flood and flood watches at an WFO or RFC.

ID: PROBSEVACCUM

≥ 50%

ID: ProbSevere

ProbSevere

ID: PROBSEVERE

The probability of any severe is the max(ProbHail,ProbWind,ProbTor).

ID: PROBSEVEREV3

PSv3 models use a machine-learning model called gradient-boosted decision trees.

ID: PROBSEVACCUMLOW

ProbSevere Accumulation 20% to 49%

ID: PROBSEVTESTACCUM

ID: PROBSEVTESTACCUMLOW

ID: PROBTOR

ID: PROBTORACCUM

ID: PSNCO

ID: PSNSSL

ID: QPF6hr

WPC 6hr Quantitative Precip Forecast QPF (in)

ID: WPC-QPF

WPC-QPF

ID: RAP-CONUS-PRAT-SFC-DBZ

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ID: 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.

ID: 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.

ID: RVER-ICEC-AP

CIMSS hosts a flood product developed at a river ice product developed at City College of New York (CCNY) derived from VIIRS. The CCNY algorithm produces an enhanced river ice mapping product with river ice concentration. Products are generated with direct broadcast VIIRS data in near real-time. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Algorithm Version5.1, Alaska region

ID: RVER-ICEC-MB

CIMSS hosts a flood product developed at a river ice product developed at City College of New York (CCNY) derived from VIIRS. The CCNY algorithm produces an enhanced river ice mapping product with river ice concentration. Products are generated with direct broadcast VIIRS data in near real-time. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Algorithm Version5.1, Missouri Basin (product off-line in summer)

ID: RVER-ICEC-NC

CIMSS hosts a flood product developed at a river ice product developed at City College of New York (CCNY) derived from VIIRS. The CCNY algorithm produces an enhanced river ice mapping product with river ice concentration. Products are generated with direct broadcast VIIRS data in near real-time. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Algorithm Version5.1, North Central Basin (product off-line in summer)

ID: RVER-ICEC-NE

CIMSS hosts a flood product developed at a river ice product developed at City College of New York (CCNY) derived from VIIRS. The CCNY algorithm produces an enhanced river ice mapping product with river ice concentration. Products are generated with direct broadcast VIIRS data in near real-time. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Algorithm Version5.1, North East Basin (product off-line in summer)

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: 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

ID: RIVER-FLDtsp-US

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. US(Transparent flood-free land) Quick guide

ID: 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

ID: 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

ID: RIVER-ICE-AP

CIMSS hosts a flood product developed at a river ice product developed at City College of New York (CCNY) derived from VIIRS. The CCNY algorithm produces an enhanced river ice mapping product with river ice extent. Products are generated with direct broadcast VIIRS data in near real-time. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Algorithm Version5.1, Alaska

ID: RIVER-ICE-MB

CIMSS hosts a flood product developed at a river ice product developed at City College of New York (CCNY) derived from VIIRS. The CCNY algorithm produces an enhanced river ice mapping product with river ice extent. Products are generated with direct broadcast VIIRS data in near real-time. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Algorithm Version5.1, Missouri Basin (product off-line in summer)

ID: RIVER-ICE-NC

CIMSS hosts a flood product developed at a river ice product developed at City College of New York (CCNY) derived from VIIRS. The CCNY algorithm produces an enhanced river ice mapping product with river ice extent. Products are generated with direct broadcast VIIRS data in near real-time. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Algorithm Version5.1, North Central Basin (product off-line in summer)

ID: RIVER-ICE-NE

CIMSS hosts a flood product developed at a river ice product developed at City College of New York (CCNY) derived from VIIRS. The CCNY algorithm produces an enhanced river ice mapping product with river ice extent. Products are generated with direct broadcast VIIRS data in near real-time. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Algorithm Version5.1, Northeast Basin (product off-line in summer)

ID: NPP-SIC-ENH

The Sea Ice Concentration product is based on NOAA Enterprise Algorithm. The original spatial resolution is 750 m as the data input are VIIRS M band at 750 m resolution. It is regridded to the original resolution to 1 km EASE2-Grid. For the reference, you can refer to Liu, Y., Key, J., & Mahoney, R. (2016). Sea and freshwater ice concentration from VIIRS on Suomi NPP and the future JPSS satellites. Remote Sensing, 8(6), 523.

ID: NESDIS-SST

NESDIS: Hi-Res Sea Surface Temperature

ID: POESNAV-SEN2A

POESNAV-SEN2A

ID: POESNAV-SEN2B

POESNAV-SEN2B

ID: SPCsvday2

Severe Weather Outlook Day2

ID: SPCsvday3

Severe Weather Outlook Day3

ID: SPCsvday4

Severe Weather Outlook Day4

ID: SPCsvday5

Severe Weather Outlook Day5

ID: SevereOutl

Tornado, Thunderstorm, Flash Flood and Marine Warnings (outlines only, no fill)

ID: Severe

Tornado, Thunderstorm, Flash Flood and Marine Warning polygons.

ID: SevereVect

Tornado and Thunderstorm Warning Vectors

ID: SAW

Severe Weather Watch Box - Aviation

ID: SPCwnday1

Severe Wind Outlook Day1 (%)

ID: SSEC-ShipBuoy

Global Ship & Buoy

ID: SNODAS-Thickness

SNODAS (SNOw Data Assimilation System) is a modeling and data assimilation system developed by NOAA National Weather Service"s NOHRSC (National Operational Hydrologic Remote Sensing Center) to provide the best possible estimates of snow cover and associated parameters to support hydrologic modeling and analysis. The aim of SNODAS is to provide a physically consistent framework to integrate snow data from satellite, airborne platforms, and ground stations with model estimates of snow cover. The 24hr Snow Thickness is a daily snapshot of snow thickness at 0600hr UTC.

ID: NESDIS-SnowFallRate

AMSU Snow Fall Rate Global by NOAA-NESDIS

ID: lsr-snow

Snowfall Reports

ID: SNODAS-Accumulate

SNODAS (SNOw Data Assimilation System) is a modeling and data assimilation system developed by NOAA National Weather Service"s NOHRSC (National Operational Hydrologic Remote Sensing Center) to provide the best possible estimates of snow cover and associated parameters to support hydrologic modeling and analysis. The aim of SNODAS is to provide a physically consistent framework to integrate snow data from satellite, airborne platforms, and ground stations with model estimates of snow cover. 24hr Snow Fall Total is calculated every 24 hours at 0600hr UTC and posted shortly thereafter.

ID: WPC-psnowgt04

WPC-psnowgt04

ID: WPC-psnowgt08

WPC-psnowgt08

ID: WPC-psnowgt12

WPC-psnowgt12

ID: WPC-psnow24gep1

WPC-psnow24gep1

ID: WPC-psnow24ge1

WPC-psnow24ge1

ID: WPC-psnow24ge2

WPC-psnow24ge2

ID: WPC-psnow24ge4

WPC-psnow24ge4

ID: WPC-psnow24ge6

WPC-psnow24ge6

ID: WPC-psnow24ge8

WPC-psnow24ge8

ID: WPC-psnow24ge12p0

WPC-psnow24ge12p0

ID: WPC-psnow24ge18p0

WPC-psnow24ge18p0

ID: nppadpam

NPP Day/Night AM Composite - Adaptive

ID: nppdnbdyn-hnl

NPP Day/Night Band (DNB) - Honolulu DB

ID: nppdnbdyn-msn

Suomi NPP Day/Night Band (DNB) imagery received and processed by the SSEC UW-Madison direct reception facility by Direct Broadcast from the satellite.

ID: nppdnbdyn-upr

NPP Day/Night Band (DNB) - Puerto Rico DB

ID: nppdnb

NPP Day/Night Band - Dynamic

ID: nppfc

NPP False Color

ID: CO-MR-496mb

This a proof of concept example of NUCAPS from Suomi NPP CrIS/ATMS data, converted to a gridded NetCDF.

ID: POESNAV-NPP

ID: nppsst

NPP Sea Surface Temperature

ID: nppsst-msn

NPP Sea Surface Temperature (SST) - Madison DB

ID: npptc-hnl

NPP True Color (TC) - Honolulu DB

ID: npptc-upr

NPP True Color (TC) - Puerto Rico DB

ID: npp-viirs-adaptive-dnb-daily

npp-viirs-adaptive-dnb-daily

ID: npp-viirs-i02-daily

npp-viirs-i02-daily

ID: npp-viirs-i05-daily

npp-viirs-i05-daily

ID: npp-viirs-i05-daily-tops

View of npp-viirs-i05-daily

ID: npp-viirs-false-color-daily

View of npp-viirs-false-color-swath

ID: npp-viirs-false-color-daily

View of npp-viirs-false-color-swath

ID: npp-viirs-false-color-hourly

View of npp-viirs-false-color

ID: npp-viirs-false-color-hourly

View of npp-viirs-false-color

ID: npp-viirs-false-color-swath

View of npp-viirs-false-color

ID: npp-viirs-false-color-swath

View of npp-viirs-false-color

ID: nppfc-msn

Suomi-NPP VIIRS False Color imagery received and processed by the SSEC UW-Madison direct reception facility by Direct Broadcast from the satellite.

ID: npp-viirs-swath-fire-color

View of npp-viirs-bands-day-swath

ID: npp-viirs-swath-fire-color

View of npp-viirs-bands-day-swath

ID: npp-viirs-swath-fire-temp

View of npp-viirs-bands-day-swath

ID: npp-viirs-swath-fire-temp

View of npp-viirs-bands-day-swath

ID: npp-viirs-adaptive-dnb

npp-viirs-adaptive-dnb

ID: npp-viirs-i02

npp-viirs-i02

ID: npp-viirs-i05

npp-viirs-i05

ID: npp-viirs-i05-tops

View of npp-viirs-i05

ID: npp-viirs-true-color-daily

View of npp-viirs-true-color-swath

ID: npp-viirs-true-color-daily

View of npp-viirs-true-color-swath

ID: npp-viirs-true-color-hourly

View of npp-viirs-true-color

ID: npp-viirs-true-color-hourly

View of npp-viirs-true-color

ID: npp-viirs-true-color-swath

View of npp-viirs-true-color

ID: npp-viirs-true-color-swath

View of npp-viirs-true-color

ID: npptc-msn

Suomi-NPP VIIRS True Color imagery received and processed by the SSEC UW-Madison direct reception facility by Direct Broadcast from the satellite.

ID: SPCREPS12Z12Z

SPCREPS12Z12Z

ID: SCIT-PNT

Storm Cell Identification and Tracking (SCIT) Filters CELL | Cell Id SITE | NEXRAD Site Id TVS | Tornado Vortex Signature MDA | Mesocyclone

ID: SCIT

Storm Cell Id and Tracking - Track

ID: StormReports

Storm Reports (last 3hrs)

ID: StormReports24

Storm Reports (last 24hrs)

ID: XLSD

XLSD - Experimental product, Restricted to SSEC internal use only!

ID: SVRWARNS12Z12Z

ID: TAF

Terminal Aerodrome Forecast (TAF)

ID: TERRA-AER

MODIS: TERRA Aerosol Optical Depth (ta)

ID: terrafalsecolor

CIMSS-MODIS Satellite False Color (Terra)

ID: GLOBALterratc

MODIS: Terra land Surface True Color composite

ID: POESNAV-TERRA

ID: terratruecolor

CIMSS-MODIS Satellite True Color (Terra)

ID: TEST

ID: TESTGRBRADF

TESTGRBRADF

ID: WWSEVTRW

Thunderstorm Watches and Warnings

ID: SPCtnday1

Tornado Outlook Day1 (%)

ID: WWTORNADO

Tornado Watches and Warnings

ID: WWTOR

Tornado Watches and Warnings

ID: TORWARNS12Z12Z

ID: AURA-SO2

AURA - OMI Total Column Sulphur Dioxide (SO2)

ID: BRDF

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

ID: TSCONEALL

TS Cones - Atlantic and EPacific

ID: PNCONEALL

TS Cones - CPacific and WPacific

ID: TSHDOBATLparm

TS HDOB - Atlantic points

ID: TSHDOBATL

TS HDOB - Atlantic winds

ID: TSHDOBEPACparm

TS HDOB - EPacific points

ID: TSHDOBEPAC

TS HDOB - EPacific winds

ID: TSPOINTALL

TS Points - Atlantic and EPacific

ID: TSTRACKALL

TS Tracks - Atlantic and EPacific

ID: AIRMET-TURB

AIRMET-Turlulence Advisory

ID: usgs-ard-grid

This product shows the Analysis Ready Data (ARD) grids for Landsat satellite data. It includes all three grids for the contiguous United States (CONUS), Alaska, and Hawaii. Landsat data have been produced, archived, and distributed by the U.S. Geological Survey (USGS) since 1972. Users rely upon these data for conducting historical studies of land surface change, but they have shouldered the burden of post-production processing to create application-ready datasets. To alleviate this burden on the user, the USGS has initiated an effort to produce a collection of Landsat Science Products to support land surface change studies. The effort involves re-gridding Landsat imagery in regular 150km x 150km squares using an Albers Equal Area projection.

ID: VIIRS-Fire-RGB-CIRA

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 Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University.

ID: 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).

ID: VIIRS-Fire-Temp-RGB-CIRA

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 (smallest/lowest intensity) to yellow to white (hottest or most intense). These data are produced by the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University.

ID: 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).

ID: VIIRS-i04-GINA

This is the VIIRS 3.74um single channel i-band with 376 m resolution. It is an IR channel that is very sensitive to fires and hot spots and is available day or night. A special colormap is used to enhance the warm-hot pixels. The sensors can become saturated by very intense fires and daytime radiance values can affected by reflected sunlight. These data are produced by the Geographic Information Network of Alaska (GINA).

ID: NDVI-16day-before

This CONUS NDVI product is clipped from the global VIIRS composite product VNP13A1-001 using AppEEARS at the NASA LPDAAC. The spatial resolution is 500m. It is made with in alternating 8-day cycles from best available pixels. See link below for more information.

ID: VIIRS-Snowmelt-GINA

This RGB is created by assigning the VIIRS 1.61um channel to red, 1.24um channel to green, and the 0.64um channel to blue. The blue shades identify snow cover characteristics. Darker blue shows wetter or older snow and lighter blues show drier or newer snow. These data are produced by the Geographic Information Network of Alaska (GINA).

ID: AMV-VISmid

AMV: Middle Level Visible (700-800mb)

ID: AMV-VISlow

AMV: Lower Level Visible (801-925mb)

ID: VAA

Volcanic Ash Advisories: Ash Clouds

ID: WICoast

WI Coastal Imagery displays aerial photographs of the Lake Michigan coast of Wisconsin from 2007. The images are being used to monitor cladophora algae growth.

ID: WIcoastallidar

WI Coastal LiDAR

ID: WIcoastalshdrlf

WI coastal shaded relief map generated from LiDAR data.

ID: LakesTSI

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

ID: WWIND

Wind Hazards is a collection of alerts associated with all types of Wind related events. These Hazards are issued by the NWS WSFOs as Advisories, Watches and Warnings. WindEvents include Wind, LakeWind and HighWind categories. Click on objects to get a detailed description of the specific hazard.

ID: SKITrails

SKITrails

ID: WPC-WSSI

ID: WWINTER

Winter Weather is a collection of Hazards associated with all types of Winter precip and conditions. Hazards are issued by the NWS WSFOs as Advisories, Watches and Warnings. SnowEvents include SnowStorm, WinterStorm, Snow, HeavySnow, LakeEffectSnow and BlowingSnow. IceEvents include Sleet, HeavySleet, FreezingRain, IceStorm and FreezingFog. Click on objects to get a detailed description of the specific hazard.

ID: XWINTER

The National Weather Service issues a variety of Winter Weather warnings, watches and advisories. The event type is indicated on the map by different colors. This product contains Winter Weather Hazards VALID for a 48hr Window spanning from the previous 24hrs to 24hrs in the future at 1hr increments.

ID: 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.

ID: wi-counties-basic

ID: wisc-3d

The Space Shuttle Endeavour collected data to produce a digital elevation model of the Earth during the Shuttle Radar Topography Mission (SRTM), flown from February 11-22, 2000. Researchers clipped Wisconsin from this data to produce this 3D anaglyph. To see the 3D effect, use Red-Blue 3D glasses (red over left eye).

ID: 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.

ID: 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.

ID: WPC-WSSI-BlowingSnow

ID: WPC-WSSI-FlashFreeze

ID: WPC-WSSI-Blizzard

ID: WPC-WSSI-IceAccum

ID: WPC-WSSI-SnowAmount

ID: WPC-WSSI-SnowLoad