RealEarth™ Product Inventory



Collection:

Alphabetic list of 480 products:
  1. 24hr SnowDepth
    ID: SNOWDEPTH24
    24hr SnowDepth (in)
  2. 24hr SnowFall
    ID: SNOWFALL24
    24hr SnowFall (in)
  3. 2015 WI NAIP Counties
    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.
  4. 2015 WI NAIP DOQQs
    ID: NAIPWI2015fp
    This layer displays the coverage footprints for the 2015 Wisconsin NAIP aerial photography. Right-click probe allows downloads of source imagery.
  5. African Wild Fire targets
    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.
  6. Aqua Aerosol Optical Depth
    ID: AQUA-AER
    MODIS: AQUA Aerosol Optical Depth (ta)
  7. Aqua False Color
    ID: aquafalsecolor
    CIMSS-MODIS Satellite False Color (Aqua)
  8. Aqua Land Surface True Color
    ID: GLOBALaquatc
    MODIS: Aqua Land Surface True Color
  9. AQUA Orbit
    ID: POESNAV-AQUA
  10. Blended TPW GPS
    ID: NESDIS-BTPWgps
    NESDIS-BTPWgps
  11. Blended TPW Percent
    ID: NESDIS-BTPWpct
    NESDIS-BTPWpct
  12. Cladophora Classification
    ID: clad
    Estimate of 2005 algae extent along coastal Lake Michigan.
  13. CLAVR-x Cloud Depth
    ID: CloudDepth-CLAVRX
    CloudDepth-CLAVRX
  14. CLAVR-x Cloud Effective Radius
    ID: CloudReff-CLAVRX
    CloudReff-CLAVRX
  15. CLAVR-x Cloud Top Height
    ID: CloudHght-CLAVRX
    CloudHght-CLAVRX
  16. CLAVR-x Cloud Top Pressure
    ID: CloudPres-CLAVRX
    CloudPres-CLAVRX
  17. CLAVR-x Cloud Top Temperature
    ID: CloudTemp-CLAVRX
    CloudTemp-CLAVRX
  18. Cloud Top Cooling targets
    ID: CIMSS-CTCtargets
    CIMSS-Cloud Top Cooling targets
  19. Convective Outlook - Categorical
    ID: SPC-ConvOutlook-CATG
    SPC Convective Outlook - Categorical
  20. Convective Outlook - Categorical (color map)
    ID: SPC-ConvOutlook-CATG-cmap
    View of SPC-ConvOutlook-CATG
  21. Convective Outlook Day1
    ID: SPCcoday1
    Convective Outlook Day1 (Category) id=SPCcoday1
  22. Convective Outlook Day2
    ID: SPCcoday2
    Convective Outlook Day2 (Category)
  23. Convective Outlook Day3
    ID: SPCcoday3
    Convective Outlook Day3 (Categorical)
  24. CSPP Active Fires
    ID: csppafedr
    CSPP Active Fires
  25. CSPP VIIRS Flood Detection
    ID: cspp-flood
    Daily direct broadcast-produced flood products created by latest alpha version of the CSPP VIIRS Flood Detection software.
  26. CSPP VIIRS Flood Detection (no cloud)
    ID: cspp-flood-nocloud
    An alternate view of the CSPP VIIRS Flood Detection product with cloud & cloud shadow pixels set to transparent.
  27. CSPP VIIRS Flood Detection - Global
    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.
  28. CSPP VIIRS Flood Detection - Global (no clouds)
    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.
  29. Current Large Fires
    ID: Current-Fires
    Current Large Fires
  30. DNB-ClearView
    ID: DNB-ClearView
    DNB-ClearView
  31. dnb-monthly-nightlights
    ID: dnb-monthly-nightlights
  32. Earthquake Magnitude
    ID: Earthquake-mag
    Earthquake Magnitude (Past 24hr)
  33. Eclipse Path
    ID: Eclipse
    Eclipse Path
  34. Effective Bulk Shear
    ID: EBSPS
    ProbSevere effective bulk shear merged and smoothed
  35. Excessive Rainfall Threat Area Day1
    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
  36. Excessive Rainfall Threat Area Day2
    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
  37. Excessive Rainfall Threat Area Day3
    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
  38. Fire Danger Index Africa
    ID: ZAFDI
    MODIS Fire Danger Index South Africa by CIMSS-DBCRAS
  39. Fire Danger Index ConUS
    ID: CONUSFDI
    MODIS Fire Danger Index (FDI) ConUS by CIMSS-DBCRAS
  40. Fire Hazards (Issued)
    ID: REDFLAG
    The National Weather Service issues a variety of Weather warnings, watches and advisories. The event type is indicated on the map by different colors. This product contains Wildland Fire Weather Hazards as they are ISSUED by WSFO.
  41. Fire Hazards (Valid)
    ID: XREDFLAG
    The National Weather Service issues a variety of Weather warnings, watches and advisories. The event type is indicated on the map by different colors. This product contains Wildland Fire Weather Hazards VALID for a 48hr Window spanning from the previous 24hrs to 24hrs in the future at 1hr increments
  42. Fire Radiative Power VIIRS 375m Alaska
    ID: FIRMS-VIIRS-Alaska-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  43. Fire Radiative Power VIIRS 375m CAmerica
    ID: FIRMS-VIIRS-Central-America-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  44. Fire Radiative Power VIIRS 375m Canada
    ID: FIRMS-VIIRS-Canada-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  45. Fire Radiative Power VIIRS 375m ConUS
    ID: FIRMS-VIIRS-ConUS-Hawaii-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  46. Fire Radiative Power VIIRS 375m Global >16
    ID: FIRMS-VIIRS-Global-ActiveFires-Filtered
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  47. Fire Radiative Power VIIRS 375m Global IMG
    ID: FIRMS-VIIRS-Global-ActiveFires-Raster
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as otherscience applications requiring improved fire mapping fidelity.
  48. Fire Radiative Power VIIRS 375m SAmerica
    ID: FIRMS-VIIRS-South-America-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity.
  49. Fire Radiative Power VIIRS 375m SEAsia
    ID: FIRMS-VIIRS-SEAsia-ActiveFires
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as otherscience applications requiring improved fire mapping fidelity.
  50. Fire Radiative Power VIIRS I-band DB
    ID: AFIMG-Points
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software.
  51. Fire Weather Outlook - Categorical
    ID: SPC-FireOutlook-CATG
    SPC Fire Weather Outlook - Categorical
  52. Fire Weather Outlook Day1
    ID: SPCfwday1
    Fire Weather Outlook Day1 (Category)
  53. Fire Weather Outlook Day2
    ID: SPCfwday2
    Fire Weather Outlook Day2 (Category)
  54. Flash Flood Hazards (Zones)
    ID: WFLASH
    Flash Flood Hazards
  55. Flood Hazards (Zones)
    ID: WWFLOOD
    Flood Watches and Warnings
  56. Flood Outlook Product
    ID: FOP
    WPC FLood Outlook Product
  57. Flood Warnings (Issued)
    ID: FLOODWARN
    FLOODWARN
  58. Flood Warnings (Issued)
    ID: FLOODWARN
    Flood Warning Polygons
  59. Flood Warnings (Valid)
    ID: XFLOODWARN
    XFLOODWARN
  60. Flood Warnings Hydrological-VTEC (Issued)
    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.
  61. Fog Hazards
    ID: WFOG
    Fog Hazards
  62. Fractional Snow Cover
    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.
  63. Fronts and Troughs
    ID: Fronts
    NCEP Frontal Analysis: fronts and troughs
  64. G16 ABI Derived Fire image
    ID: IRFIRE2
    G16 ABI Derived Fire image
  65. GINA-FIRE-TEMP-NOAA20
    ID: GINA-FIRE-TEMP-NOAA20
  66. GINA-LAND-RGB-NOAA20
    ID: GINA-LAND-RGB-NOAA20
  67. GLM FlashAvgArea
    ID: FlashAvgArea
    GOES-16 GLM average flash area 3-min average over flash extent density footprint
  68. GLM FlashCentroidDensity
    ID: FlashCentroidDensity
  69. GLM FlashExtentDensity
    ID: FlashExtentDensity
    GOES-16 flash extent density 3-min accumulation of footprint of all observed flashes
  70. glmgroupdensity-west
    ID: glmgroupdensity-west
  71. GLM TotalEnergy
    ID: TotalEnergy
    GOES-16 total optical energy 3-min accumulation over flash extent density footprint.
  72. Global Black Marble
    ID: VIIRS-MASK-54000x27000
    VIIRS Night Global Black Marble by NASA
  73. Global Infrared
    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.
  74. Global Infrared - Aviation
    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.
  75. Global Infrared - Dvorak
    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.
  76. Global Infrared - Funk Top
    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.
  77. Global Infrared - Rainbow
    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.
  78. Global Infrared - Rain Rate
    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.
  79. Global Infrared - Tops
    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.
  80. Global Lakes - Trophic State
    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.
  81. Global Night Lights
    ID: NightLightsColored
    Global Night Lights (enhanced)
  82. Global Visible
    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.
  83. Global Visible (transparent Night)
    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.
  84. Global Visible - fill
    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.
  85. Global Visible - full
    ID: global1kmvisfull
  86. Global Water Vapor
    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.
  87. Global Water Vapor - Gradient
    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.
  88. GOES-East GLM storm objects
    ID: GLMOBJ
  89. GOES 15 Full Disk IR
    ID: GOES-W-FD-IR
    GOES 15 Full Disk IR (Infrared)
  90. GOES 15 Full Disk LWIR
    ID: GOES-W-FD-LWIR
    GOES 15 Full Disk LWIR (Long Wave Infrared)
  91. GOES 15 Full Disk NIR
    ID: GOES-W-FD-NIR
    GOES 15 Full Disk NIR (Near Infrared)
  92. GOES 15 Full Disk VIS
    ID: GOES-W-FD-VIS
    GOES 15 Full Disk VIS (Visible)
  93. GOES 15 Full Disk WV
    ID: GOES-W-FD-WV
    GOES 15 Full Disk WV (Water Vapor)
  94. GOES CAPE
    ID: cimssdpicapeli
    CIMSS-DPI Convective Available Potential Energy (Li et al. 2008)
  95. GOES East ABI ConUS B02 Hi-Res "Red" Visible
    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.
  96. GOES East ABI ConUS B03 "Veggie"
    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.
  97. GOES East ABI ConUS B07 "Fire"
    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.
  98. GOES East ABI ConUS B07 "Fire" enhanced
    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.
  99. GOES East ABI ConUS B07 "Fire" stretch
    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.
  100. GOES East ABI ConUS B09 Mid-level Water Vapor
    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.
  101. GOES East ABI ConUS B09 Mid-level Water Vapor enhanced
    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.
  102. GOES East ABI ConUS B13 "Clean" Infrared
    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.
  103. GOES East ABI ConUS B13 "Clean" Infrared enhanced
    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.
  104. GOES East ABI ConUS L2 "Sandwich"
    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.
  105. GOES East ABI ConUS RGB True Color
    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.
  106. GOES East ABI Full Disk B02 Hi-Res "Red" Visible
    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.
  107. GOES East ABI Full Disk B03 "Veggie"
    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.
  108. GOES East ABI Full Disk B07 "Fire"
    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.
  109. GOES East ABI Full Disk B07 "Fire" enhanced
    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.
  110. GOES East ABI Full Disk B09 Mid-level Water Vapor
    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.
  111. GOES East ABI Full Disk B09 Mid-level Water Vapor enhanced
    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.
  112. GOES East ABI Full Disk B13 "Clean" Infrared
    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.
  113. GOES East ABI Full Disk B13 "Clean" Infrared enhanced
    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.
  114. GOES East ABI Full Disk L2 Rain Rate QPE
    ID: G16-L2-FD-RRQPE
    The ABI rainfall rate algorithm generates the baseline rainfall rate product from ABI infrared brightness temperatures and is calibrated in real time against microwave-derived rain rates to enhance accuracy. The algorithm generates estimates of the instantaneous rainfall rate at each ABI IR pixel. The information provided by the quantitative precipitation estimation is used by forecasters and hydrologists in flood forecasting. Much of the flooding that occurs is related to some form of convective weather. The higher spatial and temporal resolution available on the GOES-R Series ABI is able to automatically resolve rainfall rates on a much finer scale, enabling weather forecasters to produce more timely and accurate flood advisories and warnings.
  115. GOES East ABI Full Disk RGB True Color
    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.
  116. GOES EAST ABI L2 South America Sandwich
    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.
  117. GOES East ABI Meso1 B02 Hi-Res "Red" Visible
    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.
  118. GOES East ABI Meso1 B03 "Veggie"
    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.
  119. GOES East ABI Meso1 B07 "Fire"
    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.
  120. GOES East ABI Meso1 B07 "Fire" enhanced
    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.
  121. GOES East ABI Meso1 B09 Mid-level Water Vapor
    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.
  122. GOES East ABI Meso1 B09 Mid-level Water Vapor enhanced
    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.
  123. GOES East ABI Meso1 B13 "Clean" Infrared
    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.
  124. GOES East ABI Meso1 B13 "Clean" Infrared enhanced
    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.
  125. GOES East ABI Meso1 B13 "Clean" Infrared red
    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.
  126. GOES East ABI Meso1 L2 "Sandwich"
    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.
  127. GOES East ABI Meso1 RGB True Color
    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.
  128. GOES East ABI Meso2 B02 Hi-Res "Red" Visible
    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.
  129. GOES East ABI Meso2 B03 "Veggie"
    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.
  130. GOES East ABI Meso2 B07 "Fire"
    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.
  131. GOES East ABI Meso2 B07 "Fire" enhanced
    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.
  132. GOES East ABI Meso2 B09 Mid-level Water Vapor
    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.
  133. GOES East ABI Meso2 B09 Mid-level Water Vapor enhanced
    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.
  134. GOES East ABI Meso2 B13 "Clean" Infrared
    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.
  135. GOES East ABI Meso2 B13 "Clean" Infrared blue
    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.
  136. GOES East ABI Meso2 B13 "Clean" Infrared enhanced
    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.
  137. GOES East ABI Meso2 L2 "Sandwich"
    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.
  138. GOES East ABI Meso2 RGB True Color
    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.
  139. GOES East GLM Full Disk Group Density
    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.
  140. GOES IR Aviation
    ID: conusiravn
    GOES IR Aviation
  141. GOES IR Dvorak
    ID: conusirbd
    GOES IR Dvorak
  142. GOES IR Funk Top
    ID: conusirfunk
    GOES IR Funk Top
  143. GOES IR Overshooting Tops
    ID: conusirott
    GOES IR Overshooting Tops
  144. GOES IR Rainbow
    ID: conusirnhc
    GOES IR Rainbow
  145. GOES Lifted Index
    ID: cimssdpilili
    GOES-DPI Lifted Index (Li et al. 2008)
  146. GOES Ozone
    ID: cimssdpiozli
    GOES-DPI Ozone (Li etal 2008)
  147. GOES Precipitable Water
    ID: cimssdpipwli
    CIMSS-DPI Precipitable Water (mm)
  148. GOES West ABI ConUS B02 Hi-Res "Red" Visible
    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.
  149. GOES West ABI ConUS B03 "Veggie"
    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.
  150. GOES West ABI ConUS B07 "Fire"
    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.
  151. GOES West ABI ConUS B07 "Fire" enhanced
    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.
  152. GOES West ABI ConUS B09 Mid-level Water Vapor
    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.
  153. GOES West ABI ConUS B09 Mid-level Water Vapor enhanced
    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.
  154. GOES West ABI ConUS B13 "Clean" Infrared
    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.
  155. GOES West ABI ConUS B13 "Clean" Infrared enhanced
    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.
  156. GOES West ABI ConUS RGB True Color
    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.
  157. GOES West ABI Full Disk B02 Hi-Res "Red" Visible
    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.
  158. GOES West ABI Full Disk B03 "Veggie"
    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.
  159. GOES West ABI Full Disk B07 "Fire"
    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.
  160. GOES West ABI Full Disk B07 "Fire" enhanced
    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.
  161. GOES West ABI Full Disk B09 Mid-level Water Vapor
    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.
  162. GOES West ABI Full Disk B09 Mid-level Water Vapor enhanced
    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.
  163. GOES West ABI Full Disk B13 "Clean" Infrared
    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.
  164. GOES West ABI Full Disk B13 "Clean" Infrared enhanced
    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.
  165. GOES West ABI Full Disk RGB True Color
    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.
  166. GOES West ABI Meso1 B02 Hi-Res "Red" Visible
    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.
  167. GOES West ABI Meso1 B03 "Veggie"
    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.
  168. GOES West ABI Meso1 B07 "Fire"
    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.
  169. GOES West ABI Meso1 B07 "Fire" enhanced
    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.
  170. GOES West ABI Meso1 B09 Mid-level Water Vapor
    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.
  171. GOES West ABI Meso1 B09 Mid-level Water Vapor enhanced
    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.
  172. GOES West ABI Meso1 B13 "Clean" Infrared
    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.
  173. GOES West ABI Meso1 B13 "Clean" infrared enhanced
    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.
  174. GOES West ABI Meso1 B13 "Clean" Infrared green
    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.
  175. GOES West ABI Meso1 RGB True Color
    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.
  176. GOES West ABI Meso2 B02 Hi-Res "Red" Visible
    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.
  177. GOES West ABI Meso2 B03 "Veggie"
    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.
  178. GOES West ABI Meso2 B07 "Fire"
    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.
  179. GOES West ABI Meso2 B07 "Fire" enhanced
    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.
  180. GOES West ABI Meso2 B09 Mid-level Water Vapor
    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.
  181. GOES West ABI Meso2 B09 Mid-level Water Vapor enhanced
    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.
  182. GOES West ABI Meso2 B13 "Clean" Infrared
    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.
  183. GOES West ABI Meso2 B13 "Clean" Infrared enhanced
    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.
  184. GOES West ABI Meso2 B13 "Clean" Infrared yellow
    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.
  185. GOES West ABI Meso2 RGB True Color
    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.
  186. Great Lakes Surface Environmental Analysis
    ID: GLERL-GLSEAimage
    Great Lakes Surface Environmental Analysis (GLSEA) from GLERL. For more info see: http://coastwatch.glerl.noaa.gov/glsea/doc
  187. Hail Outlook Day1
    ID: SPChaday1
    Hail Outlook Day1 (%)
  188. Himawari AHI Full Disk B03 Hi-Res "Red" Visible
    ID: HIMAWARI-B03
    Himawari AHI Full Disk B03 Hi-Res "Red" Visible
  189. Himawari AHI Full Disk B04 "Veggie"
    ID: HIMAWARI-B04
    Himawari AHI Full Disk B04 "Veggie"
  190. Himawari AHI Full Disk B07 "Fire"
    ID: HIMAWARI-B07
    Himawari AHI Full Disk B07 "Fire"
  191. Himawari AHI Full Disk B07 "Fire" enhanced
    ID: HIMAWARI-B07-FIRE
    View of HIMAWARI-B07
  192. Himawari AHI Full Disk B09 Mid-level Water Vapor
    ID: HIMAWARI-B09
    Himawari AHI Full Disk B09 Mid-level Water Vapor
  193. Himawari AHI Full Disk B09 Mid-level Water Vapor enhanced
    ID: HIMAWARI-B09-VAPR
    View of HIMAWARI-09
  194. Himawari AHI Full Disk B13 "Clean" Infrared
    ID: HIMAWARI-B13
    Himawari AHI Full Disk B13 "Clean" Infrared
  195. Himawari AHI Full Disk B13 "Clean" Infrared enhanced
    ID: HIMAWARI-B13-GRAD
    View of HIMAWARI-B13
  196. Himawari AHI Full Disk Day Convective Storm (ave)
    ID: H-DayConvectiveStorm-cve
    Himawari AHI Full Disk Day Convective Storm (ave)
  197. Himawari AHI Full Disk Day Microphysics (dms)
    ID: H-DayMicrophysics-dms
    Himawari AHI Full Disk Day Microphysics (dms)
  198. Himawari AHI Full Disk Dust (dst)
    ID: H-Dust-dst
    Himawari AHI Full Disk Dust (dst)
  199. Himawari AHI Full Disk Natural Color (dnc)
    ID: H-NaturalColor-dnc
    Himawari AHI Full Disk Natural Color (dnc)
  200. Himawari AHI Full Disk Night Microphysics (nms)
    ID: H-NightMicrophysics-ngt
    Himawari AHI Full Disk Night Microphysics (nms)
  201. Himawari AHI Full Disk RGB Air Mass (arm)
    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.
  202. Himawari AHI Full Disk Snow and Fog (dsl)
    ID: H-SnowFog-dsl
    Himawari AHI Full Disk Snow and Fog (dsl)
  203. Himawari AHI Full Disk True Color (wgt)
    ID: H-TrueColor-wgt
    Himawari AHI Full Disk True Color (wgt)
  204. Himawari AHI Japan B03 Hi-Res "Red" Visible
    ID: HIMAWARI-JP-B03
    Himawari AHI Japan B03 Hi-Res "Red" Visible
  205. Himawari AHI Japan B07 "Fire"
    ID: HIMAWARI-JP-B07
    Himawari AHI Japan Bo7 "Fire"
  206. Himawari AHI Japan B07 "Fire" enhanced
    ID: HIMAWARI-JP-B07-FIRE
    View of HIMAWARI-JP-B07
  207. Himawari AHI Japan B09 Mid-level Water Vapor
    ID: HIMAWARI-JP-B09
    Himawari AHI Japan B09 Mid-level Water Vapor
  208. Himawari AHI Japan B09 Mid-level Water Vapor enhanced
    ID: HIMAWARI-JP-B09-VAPR
    View of HIMAWARI-JP-B09
  209. Himawari AHI Japan B14 Infrared
    ID: HIMAWARI-JP-B14
    Himawari AHI Japan B14 Infrared
  210. Himawari AHI Japan B14 Infrared enhanced
    ID: HIMAWARI-JP-B14-GRAD
    View of HIMAWARI-JP-B14
  211. Himawari AHI Target B03 Hi-Res "Red" Visible
    ID: HIMAWARI-T1-B03
    Himawrai AHI Target B03 Hi-Res "Red" Visible
  212. Himawari AHI Target B07 "Fire"
    ID: HIMAWARI-T1-B07
    Himawari AHI Target B07 "Fire"
  213. Himawari AHI Target B07 enhanced
    ID: HIMAWARI-T1-B07-FIRE
    View of HIMAWARI-T1-B07
  214. Himawari AHI Target B14 Infrared
    ID: HIMAWARI-T1-B14
    Himawari AHI Target B14 Infrared
  215. Himawari AHI Target B14 Infrared enhanced
    ID: HIMAWARI-T1-B14-GRAD
    Himawari AHI Target B14 Infrared enhanced
  216. Himawari AHI Target Mid-level Water Vapor
    ID: HIMAWARI-T1-B09
    Himawari AHI Target Mid-level Water Vapor
  217. Himawari AHI Target Mid-level Water Vapor enhanced
    ID: HIMAWARI-T1-B09-VAPR
    Himawari AHI Target Mid-level Water Vapor enhanced
  218. HRRR Alaska Near Surface Smoke
    ID: HRRR-AK-smoke-surface
    NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Surface Smoke forecast model, uses VIIRS inputs.
  219. HRRR Alaska Vertically Integrated Smoke
    ID: HRRR-AK-smoke-column
    NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Vertically Integrated Smoke forecast model, uses VIIRS inputs.
  220. HRRR ConUS Latest Precipitation Rate
    ID: HRR-CONUS-PCP-LATEST
    View of HRR-CONUS-PCP-SFC
  221. HRRR ConUS Latest Simulated Radar
    ID: HRR-CONUS-RADAR-LATEST
    View of HRR-CONUS-PCP-SFC
  222. HRRR ConUS Near Surface Smoke
    ID: HRRR-smoke-surface-2
    NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Surface Smoke forecast model, uses VIIRS inputs.
  223. HRRR ConUS Vertically Integrated Smoke
    ID: HRRR-smoke-column
    NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Vertically Integrated Smoke forecast model, uses VIIRS inputs.
  224. Hydro Estimator Rainfall
    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.
  225. Icing Advisory
    ID: AIRMET-ICE
    AIRMET Icing Advisory
  226. Icing Base Altitude
    ID: ICING-BASE
    ICING: ConUS Base Altitude (kft)
  227. Icing Threat Potential
    ID: ICING-THREAT
    ICING: ConUS Threat Potential (Cat)
  228. Icing Top Altitude
    ID: ICING-TOP
    ICING: ConUS Top Altitude (kft)
  229. IFR Advisory
    ID: AIRMET-IFR
    AIRMET-IFR Advisory
  230. Infrared 6 inch Imagery of Madison
    ID: madisonir
    Infrared 6 inch Imagery of Madison
  231. Infrared 6 inch Imagery of Madison
    ID: madisonir
    Infrared 6 inch Imagery of Madison
  232. InsolationEast
    ID: InsolationEast
    Insolation East data. Raw data values scaled by 100
  233. InsolationWest
    ID: Insolation
    Insolation West data. Raw data values scaled by 100
  234. IR Winds 250-100mb
    ID: AMV-ULhigh
    AMV: Upper Level IR/WV (100-250mb)
  235. IR Winds 350-251mb
    ID: AMV-ULmid
    AMV: Upper Level IR/WV (251-350mb)
  236. IR Winds 500-351mb
    ID: AMV-ULlow
    AMV: Upper Level IR/WV (351-500mb)
  237. IR Winds 599-400mb
    ID: AMV-LLhigh
    AMV: 400-599mb Low Level IR winds
  238. IR Winds 799-600mb
    ID: AMV-LLmid
    AMV: Lower Level IR (600-799mb)
  239. IR Winds 950-800mb
    ID: AMV-LLlow
    AMV: Lower Level IR (800-950mb)
  240. Lake Michigan Surface Currents
    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
  241. Landsat-7 Orbit
    ID: POESNAV-LSAT7
  242. Landsat-8 Orbit
    ID: POESNAV-LSAT8
  243. landsat-example
    ID: landsat-example
  244. Landsat Footprints (WRS-2)
    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.
  245. Landsat Look Natural Color
    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.
  246. Landsat Look Thermal IR
    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.
  247. LaRC Cloud Phase GOESE 8km
    ID: LARC-CloudPhase-GOESE-8km
    LaRC Cloud Phase GOESE 8km
  248. LaRC Cloud Phase GOESW 8km
    ID: LARC-CloudPhase-GOESW-8km
    LaRC Cloud Phase GOESW 8km
  249. LaRC Cloud Phase HM 8km
    ID: LARC-CloudPhase-HM-8km
    LaRC Cloud Phase HM 8km
  250. LaRC Cloud Phase MET8 9km
    ID: LARC-CloudPhase-MET8-9km
    LaRC Cloud Phase MET8 9km
  251. LaRC Cloud Phase MSG 9km
    ID: LARC-CloudPhase-MSG-9km
    LaRC Cloud Phase MSG 9km
  252. LaRC Cloud Top Height GOESE 8km
    ID: LARC-CloudZtop-GOESE-8km
    LaRC Cloud Top Height GOESE 8km
  253. LaRC Cloud Top Height GOESW 8km
    ID: LARC-CloudZtop-GOESW-8km
    LaRC Cloud Top Height GOESW 8km
  254. LaRC Cloud Top Height HM 8km
    ID: LARC-CloudZtop-HM-8km
    LaRC Cloud Top Height HM 8km
  255. LaRC Cloud Top Height MET8 9km
    ID: LARC-CloudZtop-MET8-9km
    LaRC Cloud Top Height MET8 9km
  256. LaRC Cloud Top Height MSG 9km
    ID: LARC-CloudZtop-MSG-9km
    LaRC Cloud Top Height MSG 9km
  257. Low/High Pressure
    ID: HighLow
    NCEP Frontal Analysis: Highs and Lows
  258. MADIS Surface DewPoint
    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/.
  259. Maximum Arctic Sea Ice Extent
    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.
  260. Mean Snow Duration 1988-2017
    ID: mean-snow-cover-1988-2017
    mean-snow-cover-1988-2017
  261. MESHaccum
    ID: MESHaccum
  262. MESO1
    ID: MESO1
  263. METAR
    ID: SSEC-METAR
    Global METAR
  264. MIMIC Total Precip Water Hi-Res color
    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.
  265. MIMIC Total Precip Water Hi-Res gray
    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.
  266. MIMIC Total Precip Water v1 color
    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.
  267. MIMIC Total Precip Water v2 color
    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.
  268. MIMIC Total Precip Water v2 gray
    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.
  269. MIRS 90Ghz Brightness Temperature
    ID: MIRS-BT90
    MIRS 90Ghz Brightness Temperature
  270. MIRS Rain Rate
    ID: MIRS-RainRate
    MIRS Rain Rate
  271. MODIS Active Fires ConUS and Hawaii
    ID: FIRMS-MODIS-ConUS-Hawaii-ActiveFires
    FIRMS-MODIS-ConUS-Hawaii-ActiveFires
  272. Mountains Obscured Advisory
    ID: AIRMET-MTN
    AIRMET-Mountain Obscured Advisory
  273. MRMS MergedReflectivity
    ID: MERGEDREF
    Multi-Radar/Multi-Sensor MergedReflectivityQCComposite
  274. MUCAPE
    ID: MUCAPEPS
    ProbSevere merged smoothed MUCAPE [J/kg]
  275. NAIP WI
    ID: NAIPWI
    National Agricultural Imagery Program aerial photography from the Wisconsin Farm Service Agency (WI-FSA) of the USDA.
  276. NAIP WI Color Infrared
    ID: NAIPWICIR
    National Agricultural Imagery Program aerial photography from the Wisconsin Farm Service Agency (WI-FSA) of the USDA (Color Infrared)
  277. NAM-CONUS-PRAT-SFC
    ID: NAM-CONUS-PRAT-SFC
    NAM-CONUS-PRAT-SFC
  278. NEXRAD-Alaska
    ID: NEXRAD-Alaska
    NEXRAD-Alaska
  279. NEXRAD Alaska Base Reflectivity
    ID: NEXRAD-Alaska
    NEXRAD-Alaska
  280. NEXRAD antenna coverage
    ID: NEXRADrange
    NEXRAD: 124NMI coverage
  281. NEXRAD antenna locations
    ID: NEXRADsite
    NEXRAD sites and status
  282. NEXRAD CanAm Base Reflectivity
    ID: nexrcomp
    NEXRAD CanAm Base Reflectivity
  283. NEXRAD CanAm Base Reflectivity mask
    ID: nexrrain
    NEXRAD CanAm base Reflectivity mask
  284. NEXRAD CanAm Precipitation Phase
    ID: nexrphase
    NEXRAD CanAm Precipitation Phase
  285. NEXRAD ConUS 1hr Precipitation Total
    ID: nexr1hpcp
    NEXRAD ConUS 1hr Precipitation Total
  286. NEXRAD ConUS Digital Integrated Liquid
    ID: nexrdvl
    NEXRAD ConUS Digital Integrated Liquid
  287. NEXRAD ConUS Enhanced Echo Tops
    ID: nexreet
    NEXRAD ConUS Enhanced Echo Tops
  288. NEXRAD ConUS Hybrid Hydrometeor Class
    ID: nexrhhc
    NEXRAD ConUS Hybrid Hydrometeor Class
  289. NEXRAD ConUS Hybrid Reflectivity
    ID: nexrdhr
    NEXRAD ConUS Hybrid Reflectivity
  290. NEXRAD ConUS Hybrid Reflectivity mask
    ID: nexrhres
    NEXRADConUS Hybrid Reflectivity mask
  291. NEXRAD ConUS Storm Total Precipitation
    ID: nexrstorm
    NEXRAD ConUS Storm Total Precipitation
  292. NEXRAD Guam Base Reflectivity
    ID: NEXRAD-Guam
    NEXRAD Guam Base Reflectivity
  293. NEXRAD Hawaii Base Reflectivity
    ID: NEXRAD-Hawaii
    NEXRAD Hawaii Base Reflectivity
  294. NEXRAD Puerto Rico Base Reflectivity
    ID: NEXRAD-PuertoRico
    NEXRAD Puerto Rico Base Reflectivity
  295. NOAA-15 Orbit
    ID: POESNAV-N15
  296. NOAA-18 Orbit
    ID: POESNAV-N18
  297. NOAA-20 Orbit
    ID: POESNAV-N20
  298. NOAA-20 VIIRS Daily DNB (Adaptive)
    ID: j01-viirs-adaptive-dnb-daily
    j01-viirs-adaptive-dnb-daily
  299. NOAA-20 VIIRS Daily I02
    ID: j01-viirs-i02-daily
    j01-viirs-i02-daily
  300. NOAA-20 VIIRS Daily I05
    ID: j01-viirs-i05-daily
    j01-viirs-i05-daily
  301. NOAA-20 VIIRS Daily I05 (tops)
    ID: j01-viirs-i05-daily-tops
    View of j01-viirs-i05-daily
  302. NOAA-20 VIIRS Daily True Color
    ID: j01-viirs-true-color-daily
    j01-viirs-true-color-daily
  303. NOAA-20 VIIRS Hourly DNB (Adaptive)
    ID: j01-viirs-adaptive-dnb
    j01-viirs-adaptive-dnb
  304. NOAA-20 VIIRS Hourly I02
    ID: j01-viirs-i02
    j01-viirs-i02
  305. NOAA-20 VIIRS Hourly I05
    ID: j01-viirs-i05
    j01-viirs-i05
  306. NOAA-20 VIIRS Hourly I05 (tops)
    ID: j01-viirs-i05-tops
    View of j01-viirs-i05
  307. NOAA-20 VIIRS Hourly True Color
    ID: j01-viirs-true-color
    j01-viirs-true-color
  308. NPP Aerosol Optical Depth
    ID: nppaod
    NPP Aerosol Optical Depth
  309. NPP Day/Night AM Composite - Adaptive
    ID: nppadpam
    NPP Day/Night AM Composite - Adaptive
  310. NPP Day/Night AM Composite - Histogram
    ID: npphstam
    NPP Day/Night AM Composite - Histogram
  311. NPP Day/Night Band (DNB) - Honolulu DB
    ID: nppdnbdyn-hnl
    NPP Day/Night Band (DNB) - Honolulu DB
  312. NPP Day/Night Band (DNB) - Madison DB
    ID: nppdnbdyn-msn
    NPP Day/Night Band (DNB) - Madison DB
  313. NPP Day/Night Band (DNB) - Miami DB
    ID: nppdnbdyn-mia
    NPP Day/Night Band (DNB) - Miami DB
  314. NPP Day/Night Band (DNB) - Puerto Rico DB
    ID: nppdnbdyn-upr
    NPP Day/Night Band (DNB) - Puerto Rico DB
  315. NPP Day/Night Band - Dynamic
    ID: nppdnb
    NPP Day/Night Band - Dynamic
  316. NPP False Color
    ID: nppfc
    NPP False Color
  317. NPP False Color (FC) - Madison DB
    ID: nppfc-msn
    NPP False Color (FC) - Madison DB
  318. NPP Orbit
    ID: POESNAV-NPP
  319. NPP Sea Surface Temperature
    ID: nppsst
    NPP Sea Surface Temperature
  320. NPP Sea Surface Temperature (SST) - Madison DB
    ID: nppsst-msn
    NPP Sea Surface Temperature (SST) - Madison DB
  321. NPP True Color (TC) - Global
    ID: GLOBALnpptc
    NPP True Color (TC) - Global
  322. NPP True Color (TC) - Honolulu DB
    ID: npptc-hnl
    NPP True Color (TC) - Honolulu DB
  323. NPP True Color (TC) - Madison DB
    ID: npptc-msn
    NPP True Color (TC) - Madison DB
  324. NPP True Color (TC) - Miami DB
    ID: npptc-mia
    NPP True Color (TC) - Miami DB
  325. NPP True Color (TC) - Puerto Rico DB
    ID: npptc-upr
    NPP True Color (TC) - Puerto Rico DB
  326. NUCAPS-MADIS-SBCAPE
    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.
  327. NUCAPS-MADIS Mean Layer CAPE
    ID: NUCAPS-MADIS-MLCAPE
    NUCAPS-MADIS-MLCAPE
  328. NUCAPS-MADIS Mean Layer CIN
    ID: NUCAPS-MADIS-MLCIN
    NUCAPS-MADIS-MLCIN
  329. NUCAPS-MADIS Mean Layer LI
    ID: NUCAPS-MADIS-MLLI
    NUCAPS-MADIS-MLLI
  330. NUCAPS-MADIS Surface CAPE
    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.
  331. NUCAPS-MADIS Surface CIN
    ID: NUCAPS-MADIS-SBCIN
    NUCAPS-MADIS-SBCIN
  332. NUCAPS-MADIS Surface LI
    ID: NUCAPS-MADIS-SBLI
    NUCAPS-MADIS-SBLI
  333. NWS-AK-TPCP-1DAY
    ID: NWS-AK-TPCP-1DAY
    NWS-AK-TPCP-1DAY
  334. NWS-CONUS-TPCP-1DAY
    ID: NWS-CONUS-TPCP-1DAY
    NWS-CONUS-TPCP-1DAY
  335. NWS County Warning Areas
    ID: NWSCWA
    NWS County Warning Areas
  336. NWSWARNS12Z12Z
    ID: NWSWARNS12Z12Z
    NWSWARNS12Z12Z (Severe and Tornado. No SVSs)
  337. Overshooting Tops targets
    ID: CIMSS-OTtargets
    Cloud OverShooting Tops targets
  338. Pilot Reports
    ID: PIREP
    Pilot Reports: Symbols
  339. Pressure contours ConUS
    ID: SFCCON-PMSL
    Surface Contours: Sea Level Pressure (ConUS)
  340. Probabilistic Precip Forecast
    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.
  341. PROBSEVACCUM
    ID: PROBSEVACCUM
    ≥ 50%
  342. ProbSevere
    ID: ProbSevere
    ProbSevere
  343. ProbSevere (version2)
    ID: PROBSEVERE
    The probability of any severe is the max(ProbHail,ProbWind,ProbTor).
  344. ProbSevere Accumulation 20% to 49%
    ID: PROBSEVACCUMLOW
    ProbSevere Accumulation 20% to 49%
  345. ProbSevRT
    ID: ProbSevRT
    View of ProbSevere
  346. PROBSEVTESTACCUM
    ID: PROBSEVTESTACCUM
  347. PROBSEVTESTACCUMLOW
    ID: PROBSEVTESTACCUMLOW
  348. PROBTOR
    ID: PROBTOR
  349. PROBTORACCUM
    ID: PROBTORACCUM
  350. PSNSSL
    ID: PSNSSL
  351. Quantitative Precip Forecast
    ID: QPF6hr
    WPC 6hr Quantitative Precip Forecast QPF (in)
  352. RAP ConUS Latest Simulated Radar
    ID: RAP-CONUS-PRAT-SFC-DBZ
    View of RAP-CONUS-PRAT-SFC
  353. RAP North America Near Surface Smoke
    ID: RAP-smoke-surface
    RAP-smoke-surface
  354. RAP North America Vertically Integrated Smoke
    ID: RAP-smoke-column
    RAP-smoke-column
  355. RIVER-FLD-AHI
    ID: RIVER-FLD-AHI
    CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 10-min imagery since sunrise on the given day. These products are expected to be most useful in mid- and low-latitude locations. For more information visit: Here
  356. RIVER-FLD-joint-ABI
    ID: RIVER-FLD-joint-ABI
    CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available ABI-full disk imagery and VIIRS imagery since sunrise on the given day. For more information visit: Here
  357. RIVER-FLD-joint-AHI
    ID: RIVER-FLD-joint-AHI
    CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available AHI-full disk imagery and VIIRS imagery since sunrise on the given day. For more information visit: Here
  358. RIVER-FLDglobal-composite
    ID: RIVER-FLDglobal-composite
    CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 5 days. For more information visit: Here
  359. RIVER-FLDglobal-composite1
    ID: RIVER-FLDglobal-composite1
    CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 1 day. For more information visit: Here
  360. River-ICE-CONCENTRATION: Alaska
    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
  361. RIVER-ICE-CONCENTRATION: Missouri Basin
    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)
  362. River-ICE-CONCENTRATION: North Central Basin
    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)
  363. River-ICE-CONCENTRATION: North East Basin
    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)
  364. River Flood: ABI-daily
    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
  365. River Flood: ABI-daily (tsp)
    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
  366. River Flood: ABI-hourly
    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
  367. River Flood: ABI-hourly (tsp)
    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
  368. River Flood: Alaska
    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
  369. River Flood: Alaska (transparent)
    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
  370. River Flood: Global
    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
  371. River Flood: Missouri Basin
    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
  372. River Flood: Missouri Basin (transparent)
    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
  373. River Flood: North Central Basin
    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
  374. River Flood: North Central Basin (transparent)
    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
  375. River Flood: North East Basin
    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
  376. River Flood: North East Basin (transparent)
    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
  377. River Flood: North West
    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
  378. River Flood: North West (transparent)
    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
  379. River Flood: South East
    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
  380. River Flood: South East (transparent)
    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
  381. River Flood: South West
    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
  382. River Flood: South West (tsp)
    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
  383. River Flood: US
    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
  384. River Flood: US (transparent)
    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
  385. River Flood: West Gulf Basin
    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
  386. River Flood: West Gulf Basin (transparent)
    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
  387. River Ice: Alaska
    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
  388. River Ice: Missouri Basin
    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)
  389. River Ice: North Central Basin
    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)
  390. River Ice: North East Basin
    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)
  391. Sea Ice Concentration
    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.
  392. Sea Surface Temperature
    ID: NESDIS-SST
    NESDIS: Hi-Res Sea Surface Temperature
  393. SENTINEL 2A Orbit
    ID: POESNAV-SEN2A
    POESNAV-SEN2A
  394. SENTINEL 2B Orbit
    ID: POESNAV-SEN2B
    POESNAV-SEN2B
  395. Severe Weather Outlook Day2
    ID: SPCsvday2
    Severe Weather Outlook Day2
  396. Severe Weather Outlook Day3
    ID: SPCsvday3
    Severe Weather Outlook Day3
  397. Severe Weather Outlook Day4
    ID: SPCsvday4
    Severe Weather Outlook Day4
  398. Severe Weather Outlook Day5
    ID: SPCsvday5
    Severe Weather Outlook Day5
  399. Severe Weather Warning Outlines
    ID: SevereOutl
    Tornado, Thunderstorm, Flash Flood and Marine Warnings (outlines only, no fill)
  400. Severe Weather Warnings
    ID: Severe
    Tornado, Thunderstorm, Flash Flood and Marine Warning polygons.
  401. Severe Weather Warning Vectors
    ID: SevereVect
    Tornado and Thunderstorm Warning Vectors
  402. Severe Weather Watch Box
    ID: SAW
    Severe Weather Watch Box - Aviation
  403. Severe Wind Outlook Day1
    ID: SPCwnday1
    Severe Wind Outlook Day1 (%)
  404. Ship & Buoy
    ID: SSEC-ShipBuoy
    Global Ship & Buoy
  405. SIGMET Convective
    ID: SIGMET-Convective
    SIGMET-Convective
  406. SIGMET Outlook
    ID: SIGMET-Outlook
    SIGMET-Outlook
  407. Snow Fall Rate
    ID: NESDIS-SnowFallRate
    AMSU Snow Fall Rate Global by NOAA-NESDIS
  408. SNPP VIIRS Daily DNB (Adaptive)
    ID: npp-viirs-adaptive-dnb-daily
    npp-viirs-adaptive-dnb-daily
  409. SNPP VIIRS Daily I02
    ID: npp-viirs-i02-daily
    npp-viirs-i02-daily
  410. SNPP VIIRS Daily I05
    ID: npp-viirs-i05-daily
    npp-viirs-i05-daily
  411. SNPP VIIRS Daily I05 (tops)
    ID: npp-viirs-i05-daily-tops
    View of npp-viirs-i05-daily
  412. SNPP VIIRS Daily True Color
    ID: npp-viirs-true-color-daily
    npp-viirs-true-color-daily
  413. SNPP VIIRS Hourly DNB (Adaptive)
    ID: npp-viirs-adaptive-dnb
    npp-viirs-adaptive-dnb
  414. SNPP VIIRS Hourly I02
    ID: npp-viirs-i02
    npp-viirs-i02
  415. SNPP VIIRS Hourly I05
    ID: npp-viirs-i05
    npp-viirs-i05
  416. SNPP VIIRS Hourly I05 (tops)
    ID: npp-viirs-i05-tops
    View of npp-viirs-i05
  417. SNPP VIIRS Hourly True Color
    ID: npp-viirs-true-color
    npp-viirs-true-color
  418. SPC reports 12Z to 12Z
    ID: SPCREPS12Z12Z
    SPCREPS12Z12Z
  419. Storm Cell Id and Tracking - Point
    ID: SCIT-PNT
    Storm Cell Identification and Tracking (SCIT) Filters CELL | Cell Id SITE | NEXRAD Site Id TVS | Tornado Vortex Signature MDA | Mesocyclone
  420. Storm Cell Id and Tracking - Track
    ID: SCIT
    Storm Cell Id and Tracking - Track
  421. Storm Relative Velocity ARX
    ID: NEXRAD-ARX-SRVEL1
    NEXRAD: Storm Relative Velocity ARX (kts)
  422. Storm Relative Velocity GRB
    ID: NEXRAD-GRB-SRVEL1
    NEXRAD Storm Relative Velocity GRB
  423. Storm Relative Velocity MKX
    ID: NEXRAD-MKX-SRVEL1
    NEXRAD: Storm Relative Velocity MKX (kts)
  424. Storm Relative Velocity site1
    ID: NEXRAD-site1-SRVEL1
    NEXRAD: Storm Relative Velocity SITE1 (kts)
  425. Storm Relative Velocity site2
    ID: NEXRAD-site2-SRVEL1
    NEXRAD: Storm Relative Velocity SITE2 (kts)
  426. Storm Relative Velocity site3
    ID: NEXRAD-site3-SRVEL1
    NEXRAD: Storm Relative Velocity SITE3 (kts)
  427. Storm Reports 3hrs
    ID: StormReports
    Storm Reports (last 3hrs)
  428. Storm Reports 24hrs
    ID: StormReports24
    Storm Reports (last 24hrs)
  429. Stroke Density XP
    ID: XLSD
    XLSD - Experimental product, Restricted to SSEC internal use only!
  430. SVRWARNS12Z12Z
    ID: SVRWARNS12Z12Z
  431. Temperature analysis
    ID: sfcTemp
    Surface Contours: Air Temperature (Regional)
  432. Temperature contours ConUS
    ID: SFCCON-T
    Surface Contours: Air Temperature (ConUS)
  433. Temperature contours Europe
    ID: SFCEURO-T
    SFCON: Surface Air Temperature (ConEU)
  434. Terminal Area Forecasts
    ID: TAF
    Terminal Aerodrome Forecast (TAF)
  435. Terra Aerosol Optical Depth
    ID: TERRA-AER
    MODIS: TERRA Aerosol Optical Depth (ta)
  436. Terra False Color
    ID: terrafalsecolor
    CIMSS-MODIS Satellite False Color (Terra)
  437. Terra Land Surface True Color
    ID: GLOBALterratc
    MODIS: Terra land Surface True Color composite
  438. TERRA Orbit
    ID: POESNAV-TERRA
  439. Terra True Color
    ID: terratruecolor
    CIMSS-MODIS Satellite True Color (Terra)
  440. TESTGRBRADF
    ID: TESTGRBRADF
    TESTGRBRADF
  441. Thunderstorm Watches/Warnings
    ID: WWSEVTRW
    Thunderstorm Watches and Warnings
  442. Tornado Outlook Day1
    ID: SPCtnday1
    Tornado Outlook Day1 (%)
  443. Tornado Watches/Warnings
    ID: WWTORNADO
    Tornado Watches and Warnings
  444. TORPATHS
    ID: TORPATHS
  445. TORWARNS12Z12Z
    ID: TORWARNS12Z12Z
  446. Total Column Sulphur Dioxide
    ID: AURA-SO2
    AURA - OMI Total Column Sulphur Dioxide (SO2)
  447. True Color Clear View
    ID: BRDF
    MODIS Clear View ConUS Composite. BRDF (Bidirectional Reflectance Distribution Function) is a 16-day cloud-free composite.
  448. TS Cones - Atlantic and EPacific
    ID: TSCONEALL
    TS Cones - Atlantic and EPacific
  449. TS Cones - CPacific and WPacific
    ID: PNCONEALL
    TS Cones - CPacific and WPacific
  450. TS HDOB - Atlantic points
    ID: TSHDOBATLparm
    TS HDOB - Atlantic points
  451. TS HDOB - Atlantic winds
    ID: TSHDOBATL
    TS HDOB - Atlantic winds
  452. TS HDOB - EPacific points
    ID: TSHDOBEPACparm
    TS HDOB - EPacific points
  453. TS HDOB - EPacific winds
    ID: TSHDOBEPAC
    TS HDOB - EPacific winds
  454. TS Points - Atlantic and EPacific
    ID: TSPOINTALL
    TS Points - Atlantic and EPacific
  455. TS Points - CPacific and WPacific
    ID: PNPOINTALL
    TS Points - CPacific and WPacific
  456. TS Tracks - Atlantic and EPacific
    ID: TSTRACKALL
    TS Tracks - Atlantic and EPacific
  457. TS Tracks - CPacific and WPacific
    ID: PNTRACKALL
    TS Tracks - CPacific and WPacific
  458. Turbulence Advisory
    ID: AIRMET-TURB
    AIRMET-Turlulence Advisory
  459. Urban Land Cover Sites
    ID: CapStone-sites
    Zach Olson"s GIS-Certificate Program capstone project.
  460. Vegetation Index
    ID: conusndvi
    NSSL Normalized Difference Vegetation Index
  461. viirs-toc-ndvi
    ID: viirs-toc-ndvi
    viirs-toc-ndvi
  462. VIIRS Cloud Optical Thickness
    ID: VIIRS-COT
    VIIRS Cloud Optical Thickness
  463. Vis Winds 800-700mb
    ID: AMV-VISmid
    AMV: Middle Level Visible (700-800mb)
  464. Vis Winds 925-801mb
    ID: AMV-VISlow
    AMV: Lower Level Visible (801-925mb)
  465. Volcanic Ash Advisory
    ID: Volcano
    Volcanic Ash Advisories: Source Volcano
  466. Volcanic Ash Adv plumes
    ID: VAA
    Volcanic Ash Advisories: Ash Clouds
  467. WI Coastal Imagery
    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.
  468. WI Coastal LiDAR
    ID: WIcoastallidar
    WI Coastal LiDAR
  469. WI Coastal Shaded Relief
    ID: WIcoastalshdrlf
    WI coastal shaded relief map generated from LiDAR data.
  470. WI Lake Clarity
    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).
  471. Wind Hazards
    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.
  472. WI Nordic Ski Trails
    ID: SKITrails
    SKITrails
  473. Winter Road Conditions
    ID: ROADS
    Northern Tier Winter Road Conditions (WRC) decoded from state DOT text.
  474. Winter Weather Hazards (Issued)
    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.
  475. Winter Weather Hazards (Valid)
    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.
  476. WISCLAND 1993
    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.
  477. Wisconsin Counties
    ID: wi-counties-basic
  478. Wisconsin in 3D
    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).
  479. Wisconsin LIDAR Hillshade
    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.
  480. WI USGS Landsat Poster
    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.