ID: SNODAS-Thickness

SNODAS (Snow Data Assimilation System) Daily Snow Layer Thickness Imagery from the NASA MODIS instrument, courtesy NASA NSIDC DAAC.

ID: SNODAS-Accumulate

SNODAS (Snow Data Assimilation System) 24hr Snow Accumulation Imagery from the NASA MODIS instrument, courtesy NASA NSIDC DAAC.

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: GLOBALaquatc

MODIS: Aqua Land Surface True Color

ID: aquafc-day

Aqua MODIS False Color Composites (Day)

ID: aquafc-pass

Aqua MODIS False Color Swaths

ID: aquair-day

Aqua MODIS Infrared Composites (Day)

ID: aquair-night

Aqua MODIS Infrared Composites (Night)

ID: aquair-pass

Aqua MODIS Infrared Swaths

ID: aquanir-day

Aqua MODIS Near Infrared Composites (Day)

ID: aquanir-pass

Aqua MODIS Near Infrared Swaths

ID: aquaswir-day

Aqua MODIS Short Wave Infrared Composites (Day)

ID: aquaswir-night

Aqua MODIS Short Wave Infrared Composites (Night)

ID: aquaswir-pass

Aqua MODIS Short Wave Infrared Swaths

ID: aquatc-day

Aqua MODIS True Color Composites (Day)

ID: aquatc-pass

Aqua MODIS True Color Swaths

ID: aquavis-day

Aqua MODIS Visible Composites (Day)

ID: aquavis-pass

Aqua MODIS Visible Swaths

ID: aquawv-day

Aqua MODIS Water Vapor Composites (Day)

ID: aquawv-night

Aqua MODIS Water Vapor Composites (Night)

ID: aquawv-pass

Aqua MODIS Water Vapor Swaths

ID: POESNAV-AQUA

ID: NESDIS-BTPWgps

NESDIS-BTPWgps

ID: NESDIS-BTPWpct

NESDIS-BTPWpct

ID: landsat-OLI-B654-v2

landsat-OLI-B654-v2

ID: clad

Estimate of 2005 algae extent along coastal Lake Michigan.

ID: CloudDepth-CLAVRX

CloudDepth-CLAVRX

ID: CloudReff-CLAVRX

CloudReff-CLAVRX

ID: CloudHght-CLAVRX

CloudHght-CLAVRX

ID: CloudPres-CLAVRX

CloudPres-CLAVRX

ID: CloudTemp-CLAVRX

CloudTemp-CLAVRX

ID: CIMSS-CTCtargets

CIMSS-Cloud Top Cooling targets

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: csppafedr

CSPP Active Fires

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

Global flood products created from Suomi-NPP SDRs by the latest alpha version of the CSPP VIIRS Flood Detection software.

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 Fires

ID: DNB-ClearView

DNB-ClearView

ID: Earthquake-mag

Earthquake Magnitude (Past 24hr)

ID: EBSPS

ProbSevere effective bulk shear merged and smoothed

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: ZAFDI

MODIS Fire Danger Index South Africa by CIMSS-DBCRAS

ID: CONUSFDI

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

ID: REDFLAG

Red Flag Warnings and Fire Weather Watches

ID: XREDFLAG

XREDFLAG

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: snow-fraction

Global daily maps of snow fraction are produced from VIIRS data. At this time information on the presence/absence of snow in every VIIRS pixel (i.e., binary snow mask) is obtained from the IDPS Binary Snow Map product. Snow fraction is derived with a new (NDE) algorithm which estimates the area fraction of the pixel covered with snow. Within the NDE reflectance-based snow fraction algorithm the snow fraction is inferred from the the observed reflectance in the VIIRS visible band (I1). Snow fraction is assumed linearly related to the visible reflectance of the pixel. The NDE snow fraction approach is different from the one in the current IDPS algorithm where snow fraction is estimated through aggregation of the VIIRS binary snow map within 2x2 pixel blocks.

ID: Fronts

NCEP Frontal Analysis: fronts and troughs

ID: IRFIRE2

G16 ABI Derived Fire image

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: global-lake-water-trophic

The Copernicus Global Land Service – Lake Water products include an optical characterization of ~1000 of the world"s largest inland water bodies from observations by Sentinel-3 OLCI (Ocean and Land Color Instrument). This product represents estimated trophic state index (derived from phytoplankton biomass by proxy of chlorophyll-a). Production and delivery of the parameters are over 10-day intervals starting the 1st, 11th and 21st day of each month and mapped to a common global grid at 300m resolution.

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: goes8b1

ID: cimssdpicapeli

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

ID: FLSgeocphase

FLS: GOES Cloud Phase

ID: FLSgeocthick

FLS: GOES Cloud Thickness

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: 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: FLSgeolifr

FLS: GOES Low IFR

ID: FLSgeoifr

FLS: GOES IFR

ID: conusir

ConUS Infrared 1km (15 min, GEO) In the infrared (IR) channel, the satellite senses energy as heat. The earth’s surface absorbs about half of the incoming solar energy. Clouds and the atmosphere absorb a much smaller amount. The earth’s surface, clouds, and the atmosphere then re-emit part of this absorbed solar energy as heat. The infrared channel senses this re-emitted radiation. A major advantage of the IR channel is that it can sense energy at night, so this imagery is available 24 hours a day. This is a disadvantage of the visible channel, which requires daylight and cannot "see" after dark.

ID: conusir2

ConUS Infrared 4km - Temp

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: conusvis

ConUS Visible 1km (15 min, GEO) Visible satellite imagery is produced when the satellite radiometer collects reflected energy in the visible part of the electromagnetic spectrum. It is only available during daylight as it relies on reflected solar radiation. Highly reflective surfaces like clouds, snow cover, sea ice and desert sand show up as bright white. Bodies of water like lakes, rivers and oceans reflect less sunlight, appearing darker. Land surface, vegetated ground and soil display as dark gray.

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: HRR-CONUS-PCP-LATEST

View of HRR-CONUS-PCP-SFC

ID: HRR-CONUS-RADAR-LATEST

View of HRR-CONUS-PCP-SFC

ID: HRRR-smoke-surface-2

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

ID: HRRR-smoke-column

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: ICING-BASE

ICING: ConUS Base Altitude (kft)

ID: ICING-THREAT

ICING: ConUS Threat Potential (Cat)

ID: ICING-TOP

ICING: ConUS Top Altitude (kft)

ID: AIRMET-IFR

AIRMET-IFR Advisory

ID: madisonir

Infrared 6 inch Imagery of Madison

ID: madisonir

Infrared 6 inch Imagery of Madison

ID: InsolationEast

Insolation East data. Raw data values scaled by 100

ID: Insolation

Insolation West data. Raw data values scaled by 100

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: 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

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: 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: MESHaccum

ID: MESO1

ID: SSEC-METAR

Global METAR

ID: MIMICTPWHRE

MIMIC-TPW2 Hi-Res 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 Hi-Res Version is interpolated and smoothed from the MIMIC-TPW2 product to 2 km resolution.

ID: MIMICTPWHR

MIMIC-TPW2 Hi-Res 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 Hi-Res Version is interpolated and smoothed from the MIMIC-TPW2 product to 2 km resolution.

ID: MIMICTPW

The MIMIC-TPW product presents total precipitable water over the ocean, retrieved from SSMI and AMSR-E microwave sensors. The final product is an hourly composite of many swaths of TPW retrievals, advected to the required time using 1000-600 hPa winds from the GFS model.

ID: MIMICTPW2E

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: 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: MUCAPEPS

ProbSevere merged smoothed MUCAPE [J/kg]

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: NEXRADrange

NEXRAD: 124NMI coverage

ID: NEXRADsite

NEXRAD sites and status

ID: nexrcomp

NEXRAD CanAm Base Reflectivity

ID: nexrrain

NEXRAD CanAm base Reflectivity mask

ID: nexrphase

NEXRAD CanAm Precipitation Phase

ID: nexr1hpcp

NEXRAD ConUS 1hr Precipitation Total

ID: nexrdvl

NEXRAD ConUS Digital Integrated Liquid

ID: nexreet

NEXRAD ConUS Enhanced Echo Tops

ID: nexrhhc

NEXRAD ConUS Hybrid Hydrometeor Class

ID: nexrdhr

NEXRAD ConUS Hybrid Reflectivity

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: conusfog

Continental US Night Fog 4km - IR/SWIR Tdiff

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-true-color-daily

j01-viirs-true-color-daily

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-true-color

j01-viirs-true-color

ID: nppaod

NPP Aerosol Optical Depth

ID: nppadpam

NPP Day/Night AM Composite - Adaptive

ID: npphstam

NPP Day/Night AM Composite - Histogram

ID: nppdnbdyn-hnl

NPP Day/Night Band (DNB) - Honolulu DB

ID: nppdnbdyn-msn

NPP Day/Night Band (DNB) - Madison DB

ID: nppdnbdyn-mia

NPP Day/Night Band (DNB) - Miami DB

ID: nppdnbdyn-upr

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

ID: nppdnb

NPP Day/Night Band - Dynamic

ID: nppfc

NPP False Color

ID: nppfc-msn

NPP False Color (FC) - Madison DB

ID: POESNAV-NPP

ID: nppsst

NPP Sea Surface Temperature

ID: nppsst-msn

NPP Sea Surface Temperature (SST) - Madison DB

ID: GLOBALnpptc

NPP True Color (TC) - Global

ID: npptc-hnl

NPP True Color (TC) - Honolulu DB

ID: npptc-msn

NPP True Color (TC) - Madison DB

ID: npptc-mia

NPP True Color (TC) - Miami DB

ID: npptc-upr

NPP True Color (TC) - Puerto Rico DB

ID: nppdnbada-day

NPP VIIRS Day/Night Band Adaptive Equalized Histogram Composites (Day)

ID: nppdnbada-night

NPP VIIRS Day/Night Band Adaptive Equalized Composites (Night)

ID: nppdnbada-pass

NPP VIIRS Day/Night Band Adaptive Equalized Swaths

ID: nppdnbdyn-day

NPP VIIRS Day/Night Band ERF Equalized Composites (Day)

ID: nppdnbdyn-night

NPP VIIRS Day/Night Band ERF Equalized Composites (Night)

ID: nppdnbdyn-pass

NPP VIIRS Day/Night Band ERF Equalized Swaths

ID: nppfc-day

NPP VIIRS False Color Composites (Day)

ID: nppfc-pass

NPP VIIRS False Color Swaths

ID: npplwir-day

NPP VIIRS Long Wave Infrared Composites (Day)

ID: npplwir-night

NPP VIIRS Long Wave Infrared Composites (Night)

ID: npplwir-pass

NPP VIIRS Long Wave Infrared Swaths

ID: nppnir-day

NPP VIIRS Near Infrared Composites (Day)

ID: nppnir-pass

NPP VIIRS Near Infrared Swaths

ID: nppswir-day

NPP VIIRS Short Wave Infrared Composites (Day)

ID: nppswir-night

NPP VIIRS Short Wave Infrared Composites (Night)

ID: nppswir-pass

NPP VIIRS Short Wave Infrared Swaths

ID: npptc-day

NPP VIIRS True Color Composites (Day)

ID: npptc-pass

NPP VIIRS True Color Swaths

ID: nppvis1-day

NPP VIIRS Visible-1 Composites (Day)

ID: nppvis1-pass

NPP VIIRS Visible-1 Swaths

ID: nppvis2-day

NPP VIIRS Visible-2 Composites (Day)

ID: nppvis2-pass

NPP VIIRS Visible-2 Swaths

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: 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

Pilot Reports: Symbols

ID: SFCCON-PMSL

Surface Contours: Sea Level Pressure (ConUS)

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: PROBSEVACCUMLOW

ProbSevere Accumulation 20% to 49%

ID: ProbSevRT

View of ProbSevere

ID: PROBSEVTESTACCUM

ID: PROBSEVTESTACCUMLOW

ID: PROBTOR

ID: PROBTORACCUM

ID: QPF6hr

WPC 6hr Quantitative Precip Forecast QPF (in)

ID: RAP-CONUS-PRAT-SFC-DBZ

View of RAP-CONUS-PRAT-SFC

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-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-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-FLDglobal-enh

CIMSS hosts a flood product developed at George Mason 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, enhanced color table) Quick guide

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: RVER-ICEX-AP

ID: RVER-ICEX-MB

ID: RVER-ICEX-NC

ID: RVER-ICEX-NE

ID: RVER-ICEX-NW

ID: SSD-SMOKE

Product shows the detected smoke plumes indicating possible fire locations. This is a blended product using algorithms for the GOES Imager, the POES AVHRR and MODIS. Significant smoke plumes that are detected by the satellites are outlined by the analyst as well with an estimate of the smoke concentration provided. This product is created and updated as needed between 1 PM and 11 PM Eastern time. The graphical HMS product is finalized once daily.

ID: SSD-FIRE

Product shows the detected hot spots indicating possible fire locations. This is a blended product using algorithms for the GOES Imager, the POES AVHRR, SNPP VIIRS and MODIS. A quality control procedure is performed by an analyst on the automated fire detections. This product is created and updated as needed between 1 PM and 11 PM Eastern time. After 11 PM the analysis is fine-tuned as time permits. The graphical HMS product is finalized once daily.

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: gpw-v4-population-count

ID: gpw-v4-population-density

gpw-v4-population-density

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

SSEC - ShipBuoy

ID: SIGMET-Convective

SIGMET-Convective

ID: SIGMET-Outlook

SIGMET-Outlook

ID: SKITrails

SKITrails

ID: NESDIS-SnowFallRate

AMSU Snow Fall Rate Global by NOAA-NESDIS

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-true-color-daily

npp-viirs-true-color-daily

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

npp-viirs-true-color

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: NEXRAD-ARX-SRVEL1

NEXRAD: Storm Relative Velocity ARX (kts)

ID: NEXRAD-GRB-SRVEL1

NEXRAD Storm Relative Velocity GRB

ID: NEXRAD-MKX-SRVEL1

NEXRAD: Storm Relative Velocity MKX (kts)

ID: NEXRAD-site1-SRVEL1

NEXRAD: Storm Relative Velocity SITE1 (kts)

ID: NEXRAD-site2-SRVEL1

NEXRAD: Storm Relative Velocity SITE2 (kts)

ID: NEXRAD-site3-SRVEL1

NEXRAD: Storm Relative Velocity SITE3 (kts)

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: sfcTemp

Surface Contours: Air Temperature (Regional)

ID: SFCCON-T

Surface Contours: Air Temperature (ConUS)

ID: SFCEURO-T

SFCON: Surface Air Temperature (ConEU)

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: terrafc-day

Terra MODIS False Color Composites (Day)

ID: terrafc-pass

Terra MODIS False Color Swaths

ID: terrair-day

Terra MODIS Infrared Composites (Day)

ID: terrair-night

Terra MODIS Infrared Composites (Night)

ID: terrair-pass

Terra MODIS Infrared Swaths

ID: terranir-day

Terra MODIS Near Infrared Composites (Day)

ID: terranir-pass

Terra MODIS Near Infrared Swaths

ID: terraswir-day

Terra MODIS Short Wave Infrared Composites (Day)

ID: terraswir-night

Terra MODIS Short Wave Infrared Composites (Night)

ID: terraswir-pass

Terra MODIS Short Wave Infrared Swaths

ID: terratc-day

Terra MODIS True Color Composites (Day)

ID: terratc-pass

Terra MODIS True Color Swaths

ID: terravis-day

Terra MODIS Visible Composites (Day)

ID: terravis-pass

Terra MODIS Visible Swaths

ID: terrawv-day

Terra MODIS Water Vapor Composites (Day)

ID: terrawv-night

Terra MODIS Water Vapor Composites (Night)

ID: terrawv-pass

Terra MODIS Water Vapor Swaths

ID: POESNAV-TERRA

ID: terratruecolor

CIMSS-MODIS Satellite True Color (Terra)

ID: TESTGRBRADF

TESTGRBRADF

ID: WWSEVTRW

Thunderstorm Watches and Warnings

ID: SPCtnday1

Tornado Outlook Day1 (%)

ID: WWTORNADO

Tornado Watches and Warnings

ID: TORPATHS

ID: TORWARNS12Z12Z

ID: AURA-SO2

AURA - OMI Total Column Sulphur Dioxide (SO2)

ID: BRDF

MODIS Clear View ConUS Composite. BRDF (Bidirectional Reflectance 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: PNPOINTALL

TS Points - CPacific and WPacific

ID: TSTRACKALL

TS Tracks - Atlantic and EPacific

ID: PNTRACKALL

TS Tracks - CPacific and WPacific

ID: AIRMET-TURB

AIRMET-Turlulence Advisory

ID: CapStone-sites

Zach Olson"s GIS-Certificate Program capstone project.

ID: conusndvi

NSSL Normalized Difference Vegetation Index

ID: viirs-toc-ndvi

viirs-toc-ndvi

ID: FIRMS-VIIRSActiveFires

FIRMS: VIIRS I-Band 375 m Active Fire Locations

ID: FIRMS-VIIRSActiveFires-Raster

View of FIRMS-VIIRSActiveFires

ID: VIIRS-AOD

VIIRS Aerosol Optical Depth

ID: VIIRS-COT

VIIRS Cloud Optical Thickness

ID: VIIRS-Fire

VIIRS Satellite Detected Fire Locations

ID: VIIRS-TC

VIIRS True Color

ID: AMV-VISmid

AMV: Middle Level Visible (700-800mb)

ID: AMV-VISlow

AMV: Lower Level Visible (801-925mb)

ID: Volcano

Volcanic Ash Advisories: Source Volcano

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: ROADS

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

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: 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: 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.