RealEarth™ Product Inventory



Collection:

Alphabetic list of 541 products:
  1. 24hr Snow Depth
    ID: SNOWDEPTH24
    24hr SnowDepth (in)
  2. 24hr Snow Fall
    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 Wildfire 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 Orbit
    ID: POESNAV-AQUA
  9. Australia DNB 2019 - Dynamic
    ID: NppDynamicDnb
    Proof of concept VIIRS Day/Night Band imagery for the 2019 fires in New South Wales and Queensland, Australia. Source: NOAA CLASS.
  10. Australia DNB 2019 - HNCC
    ID: NppHnccDnb
    Proof of concept VIIRS Day/Night Band imagery for the 2019 fires in New South Wales and Queensland, Australia. Source: NOAA CLASS.
  11. Australian Soil Moisture - Root Zone
    ID: BOM-Root-Zone-Soil-Moisture
    Australian Bureau of Meteorology: Root Zone Soil Moisture is the sum of water in the AWRA-L Upper and Lower soil layers and represents the percentage of available water content in the top 1 m of the soil profile. The maximum storage within the soil layer is calculated from the depth of the soil and the relative soil water storage capacity. More info at the link below.
  12. Blended TPW GPS
    ID: NESDIS-BTPWgps
    NESDIS-BTPWgps
  13. Blended TPW Percent
    ID: NESDIS-BTPWpct
    NESDIS-BTPWpct
  14. Cladophora Classification
    ID: clad
    Estimate of 2005 algae extent along coastal Lake Michigan.
  15. Cloud Top Cooling targets
    ID: CIMSS-CTCtargets
    CIMSS-Cloud Top Cooling targets
  16. CMORPH2 1-Day Precip Accumulation
    ID: c2accum1dy
    This satellite-derived precipitation product represents global 1-day accumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree lat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include various rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS).
  17. CMORPH2 1-Hour Precip Accumulation
    ID: c2accum1hr
    This satellite-derived precipitation product represents global 1-hour accumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree lat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include various rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS).
  18. CMORPH2 7-Day Precip Accumulation
    ID: c2accum7dy
    This satellite-derived precipitation product represents global 7-day accumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree lat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include various rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS).
  19. Composite River Ice: Alaska
    ID: ICE-COMP-AP
  20. Composite River Ice: Missouri Basin
    ID: ICE-COMP-MB
  21. Composite River Ice: North Central
    ID: ICE-COMP-NC
  22. Composite River Ice: Northeast
    ID: ICE-COMP-NE
  23. Convective Outlook - Categorical
    ID: SPC-ConvOutlook-CATG
    SPC Convective Outlook - Categorical
  24. Convective Outlook - Categorical (color map)
    ID: SPC-ConvOutlook-CATG-cmap
    View of SPC-ConvOutlook-CATG
  25. Convective Outlook Day1
    ID: SPCcoday1
    Convective Outlook Day1 (Category) id=SPCcoday1
  26. Convective Outlook Day2
    ID: SPCcoday2
    Convective Outlook Day2 (Category)
  27. Convective Outlook Day3
    ID: SPCcoday3
    Convective Outlook Day3 (Categorical)
  28. 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.
  29. 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.
  30. 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.
  31. Current Large Fires
    ID: Current-Fires
    Current large fire incidents in the USA and Canada as tracked by USDA Forest Service
  32. DNB ClearView
    ID: DNB-ClearView
    DNB-ClearView
  33. DNB ClearView Monthly - Test
    ID: dnb-monthly-nightlights
  34. Earthquake Magnitude
    ID: Earthquake-mag
    Earthquake Magnitude (Past 24hr)
  35. Eclipse Path
    ID: Eclipse
    Eclipse Path
  36. 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
  37. 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
  38. 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
  39. Excessive Rainfall Threat Area Forecast
    ID: ERTAfcast
    WPC Excessive Rainfall Threat Area Forecast. In the Excessive Rainfall Outlooks, the Weather Prediction Center (WPC) forecasts the probability that rainfall will exceed flash flood guidance at a point. Gridded FFG is provided by the twelve NWS River Forecast Centers (RFCs) whose service areas cover the lower 48 states. WPC creates a national mosaic of FFG, whose 1, 3, and 6-hour values represent the amount of rainfall over those short durations which it is estimated would bring rivers and streams up to bankfull conditions. WPC estimates the likelihood that FFG will be exceeded by assessing environmental conditions (e.g. moisture content and steering winds), recognizing weather patterns commonly associated with heavy rainfall, and using a variety of deterministic and ensemble-based numerical model tools
  40. Excessive Rain Forecast
    ID: WPC-ExcessiveRain
    WPC-ExcessiveRain
  41. Fire Danger Index Africa
    ID: ZAFDI
    MODIS Fire Danger Index South Africa by CIMSS-DBCRAS
  42. Fire Danger Index ConUS
    ID: CONUSFDI
    MODIS Fire Danger Index (FDI) ConUS by CIMSS-DBCRAS
  43. Fire Hazards (Issued)
    ID: REDFLAG
    REDFLAG
  44. 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
  45. Fire Radiative Power VIIRS 375m Global
    ID: FIRMS-VIIRS-Global-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.
  46. Fire Radiative Power VIIRS I-band - GINA
    ID: AFIMG-Points-GINA
    VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software at GINA.
  47. 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.
  48. Fire Weather Outlook - Categorical
    ID: SPC-FireOutlook-CATG
    SPC Fire Weather Outlook - Categorical
  49. Fire Weather Outlook Day1
    ID: SPCfwday1
    Fire Weather Outlook Day1 (Category)
  50. Fire Weather Outlook Day2
    ID: SPCfwday2
    Fire Weather Outlook Day2 (Category)
  51. FireWork Smoke Forecast - PM2.5 Column Diff
    ID: RAQDPS-COL-Diff-25
    Canada’s Wildfire Smoke Prediction System (FireWork) produces daily smoke forecast maps. This map represents small particles (PM2.5), the whole atmosphere compressed (column), and the smoke-only after subtracting the atmospheric background (diff). Smoke from wildfires in forests and grasslands can be a major source of air pollution for Canadians. The fine particles in the smoke can be a serious risk to health, particularly for children, seniors and those with heart or lung disease. Because smoke may be carried thousands of kilometers downwind, distant locations can be affected almost as severely as areas close to the fire. To help Canadians be better prepared, wildfire smoke forecast maps are available through the Government of Canada’s FireWork system. FireWork is an air quality prediction system that indicates how smoke from wildfires is expected to move across North America over the next 72 hours.
  52. Flash Flood Hazards (Zones)
    ID: WFLASH
    Flash Flood Hazards
  53. Flood Hazards (Zones)
    ID: WWFLOOD
    Flood Watches and Warnings
  54. Flood Outlook Product
    ID: FOP
    WPC FLood Outlook Product
  55. Flood Warnings (Issued)
    ID: FLOODWARN
    Flood Warning Polygons
  56. Flood Warnings (Valid)
    ID: XFLOODWARN
    XFLOODWARN
  57. 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.
  58. Fog Hazards
    ID: WFOG
    Fog Hazards
  59. Freezing Rain Probability >= .25" Final Forecast
    ID: WPC-picezgt25
    The Probability of Freezing Rain Accumulating ≥ .25" Days 1-3 The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information. The product depicts the probability of freezing rain reaching or exceeding 0.25 inch for Days 1-3.
  60. Freezing Rain Probability >= 0.01"/24h
    ID: WPC-picez24gep01
    The 24-Hour Probability of Freezing Rain Accumulating ≥ .01" The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
  61. Freezing Rain Probability >= 0.10"/24h
    ID: WPC-picez24gep10
    The 24-Hour Probability of Freezing Rain Accumulating ≥ .10" The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
  62. Freezing Rain Probability >= 0.25"/24h
    ID: WPC-picez24gep25
    The 24-Hour Probability of Freezing Rain Accumulating ≥ .25" The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
  63. Freezing Rain Probability >=0.50"/24h
    ID: WPC-picez24gep50
    The 24-Hour Probability of Freezing Rain Accumulating ≥ .50" The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
  64. Freezing Rain Probability >=1.00"/24h
    ID: WPC-picez24ge1
    The 24-Hour Probability of Freezing Rain Accumulating ≥ 1.00" The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
  65. Fronts and Troughs
    ID: Fronts
    NCEP Frontal Analysis: fronts and troughs
  66. GLM FlashAvgArea
    ID: FlashAvgArea
    GOES-16 GLM average flash area 3-min average over flash extent density footprint
  67. GLM FlashCentroidDensity
    ID: FlashCentroidDensity
  68. GLM FlashExtentDensity
    ID: FlashExtentDensity
    GOES-16 flash extent density 3-min accumulation of footprint of all observed flashes
  69. glmgroupdensity-west
    ID: glmgroupdensity-west
  70. GLM TotalEnergy
    ID: TotalEnergy
    GOES-16 total optical energy 3-min accumulation over flash extent density footprint.
  71. Global Black Marble
    ID: VIIRS-MASK-54000x27000
    VIIRS Night Global Black Marble by NASA
  72. 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.
  73. 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.
  74. 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.
  75. 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.
  76. 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.
  77. 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.
  78. 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.
  79. Global Night Lights
    ID: NightLightsColored
    Global Night Lights (enhanced)
  80. 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.
  81. 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.
  82. 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.
  83. Global Visible - full
    ID: global1kmvisfull
  84. 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.
  85. 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.
  86. GOES-East GLM storm objects
    ID: GLMOBJ
  87. GOES-West GLM FED CONUS
    ID: GOESWestGLMFEDRadC
  88. GOES 15 ConUS IR
    ID: GOES-W-CONUS-IR
    GOES 15 ConUS IR
  89. GOES 15 ConUS LWIR
    ID: GOES-W-CONUS-LWIR
    GOES 15 ConUS LWIR
  90. GOES 15 ConUS NIR
    ID: GOES-W-CONUS-NIR
    GOES 15 ConUS NIR
  91. GOES 15 ConUS VIS
    ID: GOES-W-CONUS-VIS
    GOES 15 ConUS VIS
  92. GOES 15 ConUS WV
    ID: GOES-W-CONUS-WV
    GOES 15 ConUS WV
  93. GOES 15 Full Disk IR
    ID: GOES-W-FD-IR
    GOES 15 Full Disk IR (Infrared)
  94. GOES 15 Full Disk LWIR
    ID: GOES-W-FD-LWIR
    GOES 15 Full Disk LWIR (Long Wave Infrared)
  95. GOES 15 Full Disk NIR
    ID: GOES-W-FD-NIR
    GOES 15 Full Disk NIR (Near Infrared)
  96. GOES 15 Full Disk VIS
    ID: GOES-W-FD-VIS
    GOES 15 Full Disk VIS (Visible)
  97. GOES 15 Full Disk WV
    ID: GOES-W-FD-WV
    GOES 15 Full Disk WV (Water Vapor)
  98. GOES17 ABI CONUS B07 IR Fire contours
    ID: GOES17-ABI-CONUS-B07-ARC-FIRES
  99. GOES17 ABI CONUS B07 IR Fire enhanced
    ID: GOES17-ABI-CONUS-B07-ARC-ENH
    View of GOES17-ABI-CONUS-B07-ARCHIVE
  100. GOES CAPE
    ID: cimssdpicapeli
    CIMSS-DPI Convective Available Potential Energy (Li et al. 2008)
  101. 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.
  102. 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.
  103. 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.
  104. 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.
  105. 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.
  106. 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.
  107. 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.
  108. 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.
  109. 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.
  110. GOES East ABI ConUS FLS IFR Fog Probability
    ID: G16-ABI-CONUS-FLS-IFR
    IFR probability: Probability that IFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.
  111. 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.
  112. 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.
  113. 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.
  114. 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.
  115. 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.
  116. 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.
  117. 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.
  118. 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.
  119. 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.
  120. 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.
  121. 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.
  122. 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.
  123. 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.
  124. 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.
  125. 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.
  126. 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.
  127. 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.
  128. 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.
  129. 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.
  130. 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.
  131. 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.
  132. 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.
  133. 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.
  134. 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.
  135. 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.
  136. 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.
  137. 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.
  138. 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.
  139. 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.
  140. 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.
  141. 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.
  142. 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.
  143. 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.
  144. 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.
  145. 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 clouds. and snowstorms
  146. GOES East GLM Full Disk Group Points
    ID: glmgrouppoints
    glmgrouppoints
  147. GOES IR Aviation
    ID: conusiravn
    GOES IR Aviation
  148. GOES IR Dvorak
    ID: conusirbd
    GOES IR Dvorak
  149. GOES IR Funk Top
    ID: conusirfunk
    GOES IR Funk Top
  150. GOES IR Overshooting Tops
    ID: conusirott
    GOES IR Overshooting Tops
  151. GOES IR Rainbow
    ID: conusirnhc
    GOES IR Rainbow
  152. GOES Lifted Index
    ID: cimssdpilili
    GOES-DPI Lifted Index (Li et al. 2008)
  153. GOES Ozone
    ID: cimssdpiozli
    GOES-DPI Ozone (Li etal 2008)
  154. GOES Precipitable Water
    ID: cimssdpipwli
    CIMSS-DPI Precipitable Water (mm)
  155. 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.
  156. 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.
  157. 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.
  158. 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.
  159. 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.
  160. 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.
  161. 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.
  162. 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.
  163. 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.
  164. 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.
  165. 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.
  166. 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.
  167. 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.
  168. 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.
  169. 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.
  170. 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.
  171. 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.
  172. 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.
  173. 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.
  174. 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.
  175. 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.
  176. 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.
  177. 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.
  178. 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.
  179. 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.
  180. 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.
  181. 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.
  182. 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.
  183. 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.
  184. 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.
  185. 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.
  186. 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.
  187. 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.
  188. 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.
  189. 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.
  190. 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.
  191. 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.
  192. 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.
  193. 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
  194. Hail Outlook Day1
    ID: SPChaday1
    Hail Outlook Day1 (%)
  195. Himawari AHI Full Disk B03 Hi-Res "Red" Visible
    ID: HIMAWARI-B03
    Himawari AHI Full Disk B03 Hi-Res "Red" Visible
  196. Himawari AHI Full Disk B04 "Veggie"
    ID: HIMAWARI-B04
    Himawari AHI Full Disk B04 "Veggie"
  197. Himawari AHI Full Disk B07 "Fire"
    ID: HIMAWARI-B07
    Himawari AHI Full Disk B07 "Fire"
  198. Himawari AHI Full Disk B07 "Fire" enhanced
    ID: HIMAWARI-B07-FIRE
    View of HIMAWARI-B07
  199. Himawari AHI Full Disk B09 Mid-level Water Vapor
    ID: HIMAWARI-B09
    Himawari AHI Full Disk B09 Mid-level Water Vapor
  200. Himawari AHI Full Disk B09 Mid-level Water Vapor enhanced
    ID: HIMAWARI-B09-VAPR
    View of HIMAWARI-09
  201. Himawari AHI Full Disk B13 "Clean" Infrared
    ID: HIMAWARI-B13
    Himawari AHI Full Disk B13 "Clean" Infrared
  202. Himawari AHI Full Disk B13 "Clean" Infrared enhanced
    ID: HIMAWARI-B13-GRAD
    View of HIMAWARI-B13
  203. Himawari AHI Full Disk Day Convective Storm (ave)
    ID: H-DayConvectiveStorm-cve
    Himawari AHI Full Disk Day Convective Storm (ave)
  204. Himawari AHI Full Disk Day Microphysics (dms)
    ID: H-DayMicrophysics-dms
    Himawari AHI Full Disk Day Microphysics (dms)
  205. Himawari AHI Full Disk Dust (dst)
    ID: H-Dust-dst
    Himawari AHI Full Disk Dust (dst)
  206. Himawari AHI Full Disk Natural Color (dnc)
    ID: H-NaturalColor-dnc
    Himawari AHI Full Disk Natural Color (dnc)
  207. Himawari AHI Full Disk Night Microphysics (nms)
    ID: H-NightMicrophysics-ngt
    Himawari AHI Full Disk Night Microphysics (nms)
  208. 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.
  209. Himawari AHI Full Disk Snow and Fog (dsl)
    ID: H-SnowFog-dsl
    Himawari AHI Full Disk Snow and Fog (dsl)
  210. Himawari AHI Full Disk True Color (wgt)
    ID: H-TrueColor-wgt
    Himawari AHI Full Disk True Color (wgt)
  211. Himawari AHI Japan B03 Hi-Res "Red" Visible
    ID: HIMAWARI-JP-B03
    Himawari AHI Japan B03 Hi-Res "Red" Visible
  212. Himawari AHI Japan B07 "Fire"
    ID: HIMAWARI-JP-B07
    Himawari AHI Japan Bo7 "Fire"
  213. Himawari AHI Japan B07 "Fire" enhanced
    ID: HIMAWARI-JP-B07-FIRE
    View of HIMAWARI-JP-B07
  214. Himawari AHI Japan B09 Mid-level Water Vapor
    ID: HIMAWARI-JP-B09
    Himawari AHI Japan B09 Mid-level Water Vapor
  215. Himawari AHI Japan B09 Mid-level Water Vapor enhanced
    ID: HIMAWARI-JP-B09-VAPR
    View of HIMAWARI-JP-B09
  216. Himawari AHI Japan B14 Infrared
    ID: HIMAWARI-JP-B14
    Himawari AHI Japan B14 Infrared
  217. Himawari AHI Japan B14 Infrared enhanced
    ID: HIMAWARI-JP-B14-GRAD
    View of HIMAWARI-JP-B14
  218. Himawari AHI Target B03 Hi-Res "Red" Visible
    ID: HIMAWARI-T1-B03
    Himawrai AHI Target B03 Hi-Res "Red" Visible
  219. Himawari AHI Target B07 "Fire"
    ID: HIMAWARI-T1-B07
    Himawari AHI Target B07 "Fire"
  220. Himawari AHI Target B07 enhanced
    ID: HIMAWARI-T1-B07-FIRE
    View of HIMAWARI-T1-B07
  221. Himawari AHI Target B14 Infrared
    ID: HIMAWARI-T1-B14
    Himawari AHI Target B14 Infrared
  222. Himawari AHI Target B14 Infrared enhanced
    ID: HIMAWARI-T1-B14-GRAD
    Himawari AHI Target B14 Infrared enhanced
  223. Himawari AHI Target Mid-level Water Vapor
    ID: HIMAWARI-T1-B09
    Himawari AHI Target Mid-level Water Vapor
  224. Himawari AHI Target Mid-level Water Vapor enhanced
    ID: HIMAWARI-T1-B09-VAPR
    Himawari AHI Target Mid-level Water Vapor enhanced
  225. Historic Fire Scars (MTBS)
    ID: historic-fire-scars-conus
    These data come from the interagency MTBS (Monitoring Trends in Burn Severity) program through their direct download service.
  226. HRRR CONUS/AK Near Surface Smoke
    ID: HRRR-smoke-surface
    Operational model output from NECP. Developed at the NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Surface Smoke forecast model, uses VIIRS inputs.
  227. HRRR CONUS/AK Vertically Integrated Smoke
    ID: HRRR-smoke-column
    Operational smoke model output from NCEP developed at the NOAA Earth System Research Laboratory High Resolution Rapid Refresh (HRRR) Vertically Integrated Smoke forecast model, uses VIIRS inputs.
  228. HRRR ConUS Latest Freezing MASK
    ID: HRR-CONUS-FZRN-SFC
    HRRR ConUS Latest Freezing MASK
  229. HRRR ConUS Latest Ice Mask
    ID: HRR-CONUS-ICEP-SFC
    HRRR ConUS Latest Ice Mask
  230. HRRR ConUS Latest Precipitation Rate
    ID: HRR-CONUS-PCP-LATEST
    View of HRR-CONUS-PCP-SFC
  231. HRRR ConUS Latest Rain Mask
    ID: HRR-CONUS-RAIN-SFC
    HRRR ConUS Latest Rain Mask
  232. HRRR ConUS Latest Rate Mask
    ID: HRR-CONUS-PCP-SFC
    HRR-CONUS-PCP-SFC
  233. HRRR ConUS Latest Simulated Radar
    ID: HRR-CONUS-RADAR-LATEST
    View of HRR-CONUS-PCP-SFC
  234. HRRR ConUS Latest Snow Depth
    ID: HRR-CONUS-SNOD-SFC
  235. HRRR ConUS Latest Snow Mask
    ID: HRR-CONUS-SNOW-SFC
    HRRR ConUS Latest Snow Mask
  236. 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.
  237. Icing Advisory
    ID: AIRMET-ICE
    AIRMET Icing Advisory
  238. ICPRadM1
    ID: ICPRadM1
    Intense Convection Probability -- GOES East Mesoscale 1
  239. ICPRadM2
    ID: ICPRadM2
    Intense Convection Probability -- GOES East Mesoscale 2
  240. IFR Advisory
    ID: AIRMET-IFR
    AIRMET-IFR Advisory
  241. Infrared 6 inch Imagery of Madison
    ID: madisonir
    Infrared 6 inch Imagery of Madison
  242. Insolation East
    ID: InsolationEast
    This product displays daily-integrated solar radiation estimates (MJ-m-2) for the previous calendar day derived using half-hourly imagery data from GOES-EAST and GOES-WEST geostationary satellites and a simple radiative transfer model of the atmosphere. GOES-EAST data are used for the eastern half of the U.S., GOES-WEST for the western half and both these results are displayed upon initial invocation of this insolation page. Raw data values scaled by 100. Right-click on a pixel and select "probe" to view values.
  243. Insolation West
    ID: Insolation
    This product displays daily-integrated solar radiation estimates (MJ-m-2) for the previous calendar day derived using half-hourly imagery data from GOES-EAST and GOES-WEST geostationary satellites and a simple radiative transfer model of the atmosphere. GOES-EAST data are used for the eastern half of the U.S., GOES-WEST for the western half and both these results are displayed upon initial invocation of this insolation page. Raw data values scaled by 100. Right-click on a pixel and select "probe" to view values.
  244. Intense Convection Probability
    ID: ICP
    Deep learning model that predicts where "intense" convection" is present, based on features that humans associate with intense convection.
  245. IR Winds 250-100mb
    ID: AMV-ULhigh
    AMV: Upper Level IR/WV (100-250mb)
  246. IR Winds 350-251mb
    ID: AMV-ULmid
    AMV: Upper Level IR/WV (251-350mb)
  247. IR Winds 500-351mb
    ID: AMV-ULlow
    AMV: Upper Level IR/WV (351-500mb)
  248. IR Winds 599-400mb
    ID: AMV-LLhigh
    AMV: 400-599mb Low Level IR winds
  249. IR Winds 799-600mb
    ID: AMV-LLmid
    AMV: Lower Level IR (600-799mb)
  250. IR Winds 950-800mb
    ID: AMV-LLlow
    AMV: Lower Level IR (800-950mb)
  251. Landsat-7 Orbit
    ID: POESNAV-LSAT7
  252. Landsat-8 Orbit
    ID: POESNAV-LSAT8
  253. landsat-example
    ID: landsat-example
  254. 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.
  255. Landsat Look Natural Color (16 Day Global)
    ID: lsat8-llook-fc-16d
    View of lsat8-llook-fc-daily
  256. Landsat Look Natural Color (Daily)
    ID: lsat8-llook-fc-daily
    View of lsat8-llook-fc
  257. Landsat Look Natural Color (Swaths)
    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.
  258. Landsat Look Thermal IR (16 Day Global)
    ID: lsat8-llook-tir-16d
    View of lsat8-llook-tir-daily
  259. Landsat Look Thermal IR (Daily)
    ID: lsat8-llook-tir-daily
    View of lsat8-llook-tir
  260. Landsat Look Thermal IR (Swaths)
    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.
  261. Landsat Scene Footprints (WRS-2)
    ID: wrs2-scenes
  262. LaRC Cloud Phase GOESE 8km
    ID: LARC-CloudPhase-GOESE-8km
    LaRC Cloud Phase GOESE 8km
  263. LaRC Cloud Phase GOESW 8km
    ID: LARC-CloudPhase-GOESW-8km
    LaRC Cloud Phase GOESW 8km
  264. LaRC Cloud Phase HM 8km
    ID: LARC-CloudPhase-HM-8km
    LaRC Cloud Phase HM 8km
  265. LaRC Cloud Phase MET8 9km
    ID: LARC-CloudPhase-MET8-9km
    LaRC Cloud Phase MET8 9km
  266. LaRC Cloud Phase MSG 9km
    ID: LARC-CloudPhase-MSG-9km
    LaRC Cloud Phase MSG 9km
  267. LaRC Cloud Top Height GOESE 8km
    ID: LARC-CloudZtop-GOESE-8km
    LaRC Cloud Top Height GOESE 8km
  268. LaRC Cloud Top Height GOESW 8km
    ID: LARC-CloudZtop-GOESW-8km
    LaRC Cloud Top Height GOESW 8km
  269. LaRC Cloud Top Height HM 8km
    ID: LARC-CloudZtop-HM-8km
    LaRC Cloud Top Height HM 8km
  270. LaRC Cloud Top Height MET8 9km
    ID: LARC-CloudZtop-MET8-9km
    LaRC Cloud Top Height MET8 9km
  271. LaRC Cloud Top Height MSG 9km
    ID: LARC-CloudZtop-MSG-9km
    LaRC Cloud Top Height MSG 9km
  272. Low/High Pressure
    ID: HighLow
    NCEP Frontal Analysis: Highs and Lows
  273. 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/.
  274. 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.
  275. Mean Snow Duration 1988-2017
    ID: mean-snow-cover-1988-2017
    mean-snow-cover-1988-2017
  276. METAR
    ID: SSEC-METAR
    Global METAR
  277. Meteosat 8 SEVIRI Full Disk B01 Vis (0.6um)
    ID: Met8-SEVIRI-FD-BAND01
    VIS0.6: Known from the Advanced Very High Resolution Radiometer (AVHRR) of the polar-orbiting NOAA satellites. It is essential for cloud detection, cloud tracking, scene identification, aerosol, and land surface and veg­ etation monitoring.
  278. Meteosat 8 SEVIRI Full Disk B04 IR Fire (3.9um)
    ID: Met8-SEVIRI-FD-BAND04
    IR3.9: Known from AVHRR. Primarily for low cloud and fog detection (Eyre et al. 1984; Lee et al. 1997). Also supports measurement of land and sea surface temperature at night and increases the low- level wind coverage from cloud tracking (Velden et al. 2001). For MSG, the spectral band has been broadened to longer wavelengths to improve signal-to-noise ratio.
  279. Meteosat 8 SEVIRI Full Disk B05 WV High (6.2um)
    ID: Met8-SEVIRI-FD-BAND05
    WV6.2: Continues mission of Meteosat broadband water vapor channel for ob­serving water vapor and winds. Enhanced to two channels peaking at different levels in the tropo­ sphere. Also supports height allocation of semitransparent clouds (Nieman et al. 1993; Schmetz et al. 1993).
  280. Meteosat 8 SEVIRI Full Disk B09 IR Clean (10.8um)
    ID: Met8-SEVIRI-FD-BAND09
    IR10.8: Well-known split window channel (e.g., AVHRR). Essential for measur­ ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata 1989).
  281. Meteosat 8 SEVIRI Full Disk B09 IR Clean (10.8um) enhanced
    ID: Met8-SEVIRI-FD-BAND09-enh
    IR10.8: Well-known split window channel (e.g., AVHRR). Essential for measur­ ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata 1989).
  282. Meteosat 8 SEVIRI Full Disk B12 Vis HRV (0.7um)
    ID: Met8-SEVIRI-HRV-BAND12
    The high-resolution visible (HRV) channel covers half of the full disk in the east–west direction and a full disk in the north–south direction (see Fig. 3). The high-resolution visible channel has a spatial resolution of 1.67 km, as the oversampling factor is 1.67 the sam­pling distance is 1 km at nadir.
  283. Meteosat 11 SEVIRI Full Disk B01 Vis (0.6um)
    ID: Met11-SEVIRI-FD-BAND01
    VIS0.6: Known from the Advanced Very High Resolution Radiometer (AVHRR) of the polar-orbiting NOAA satellites. It is essential for cloud detection, cloud tracking, scene identification, aerosol, and land surface and veg­ etation monitoring.
  284. Meteosat 11 SEVIRI Full Disk B04 IR Fire (3.9um)
    ID: Met11-SEVIRI-FD-BAND04
    IR3.9: Known from AVHRR. Primarily for low cloud and fog detection (Eyre et al. 1984; Lee et al. 1997). Also supports measurement of land and sea surface temperature at night and increases the low- level wind coverage from cloud tracking (Velden et al. 2001). For MSG, the spectral band has been broadened to longer wavelengths to improve signal-to-noise ratio.
  285. Meteosat 11 SEVIRI Full Disk B05 WV High (6.2um)
    ID: Met11-SEVIRI-FD-BAND05
    WV6.2: Continues mission of Meteosat broadband water vapor channel for ob­serving water vapor and winds. Enhanced to two channels peaking at different levels in the tropo­ sphere. Also supports height allocation of semitransparent clouds (Nieman et al. 1993; Schmetz et al. 1993).
  286. Meteosat 11 SEVIRI Full Disk B09 IR Clean (10.8um)
    ID: Met11-SEVIRI-FD-BAND09
    IR10.8: Well-known split window channel (e.g., AVHRR). Essential for measur­ ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata 1989).
  287. Meteosat 11 SEVIRI Full Disk B09 IR Clean (10.8um) enhanced
    ID: Met11-SEVIRI-FD-BAND09-enh
    IR10.8: Well-known split window channel (e.g., AVHRR). Essential for measur­ ing sea and land surface and cloud-top temperatures; also for the detection of cirrus cloud (e.g., Inoue 1987) and volcanic ash clouds (Prata 1989).
  288. Meteosat 11 SEVIRI Full Disk B12 Vis HRV (0.7um)
    ID: Met11-SEVIRI-HRV-BAND12
    The high-resolution visible (HRV) channel covers half of the full disk in the east–west direction and a full disk in the north–south direction (see Fig. 3). The high-resolution visible channel has a spatial resolution of 1.67 km, as the oversampling factor is 1.67 the sam­pling distance is 1 km at nadir.
  289. Midwest Winter Road Conditions
    ID: ROADS-IADOT
    Conditions are updated every 10 minutes during the winter season (October 15 to April 15) and on an as-needed basis during the non-winter months. Layer and service is maintained by the Iowa DOT GIS team on behalf of the Office of Traffic Operations. This data is provided as is through this value added REST service. All conditions have been remapped to the best of our ability to meet the condition reporting criteria as defined by the Iowa DOT. Some discrepancies may appear. This data service should only be used for reference only. For the most accurate information, please utilize the authoritative state 511 sites below. State 511 Sites 511 Vendor Disclaimers North Dakota Iteris The data is provided as is and without liability from the North Dakota Department of Transportation (NDDOT). The NDDOT does not guarantee this data to be free from errors, or inaccuracies, and disclaims any responsibility or liability for interpretations or decisions based on this data.
  290. MIMIC Total Precip Water v2
    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.
  291. MIRS 90Ghz Brightness Temperature
    ID: MIRS-BT90
    MIRS 90Ghz Brightness Temperature
  292. MIRS Rain Rate
    ID: MIRS-RainRate
    MIRS Rain Rate
  293. Mountains Obscured Advisory
    ID: AIRMET-MTN
    AIRMET-Mountain Obscured Advisory
  294. MRMS MergedReflectivity
    ID: MERGEDREF
    Multi-Radar/Multi-Sensor MergedReflectivityQCComposite
  295. NAIP WI
    ID: NAIPWI
    National Agricultural Imagery Program aerial photography from the Wisconsin Farm Service Agency (WI-FSA) of the USDA.
  296. 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)
  297. NAM-CONUS-PRAT-SFC
    ID: NAM-CONUS-PRAT-SFC
    NAM-CONUS-PRAT-SFC
  298. National Reflectivity MRMS Composite mask
    ID: nexrrain
    The Multi-Radar Multi-Sensor (MRMS) system is now operational at the National Centers for Environmental Prediction (NCEP). The MRMS system consists of the Warning Decision Support System–Integrated Information suite of severe weather and aviation products and the quantitative precipitation estimation (QPE) products created by the National Mosaic and Multi-Sensor QPE system. The MRMS system provides operational guidance for severe convective weather, QPE, and aviation hazards on a seamless three-dimensional grid that is created at a spatial resolution of 0.01° latitude × 0.01° longitude, with 33 vertical levels, every 2 min over the conterminous United States (CONUS) and southern Canada.
  299. NEXRAD Alaska Base Reflectivity
    ID: NEXRAD-Alaska
    NEXRAD-Alaska
  300. NEXRAD CanAm Precipitation Phase
    ID: nexrphase
    NEXRAD CanAm Precipitation Phase
  301. NEXRAD ConUS Hybrid Reflectivity mask
    ID: nexrhres
    NEXRADConUS Hybrid Reflectivity mask
  302. NEXRAD ConUS Storm Total Precipitation
    ID: nexrstorm
    NEXRAD ConUS Storm Total Precipitation
  303. NEXRAD Guam Base Reflectivity
    ID: NEXRAD-Guam
    NEXRAD Guam Base Reflectivity
  304. NEXRAD Hawaii Base Reflectivity
    ID: NEXRAD-Hawaii
    NEXRAD Hawaii Base Reflectivity
  305. NEXRAD Puerto Rico Base Reflectivity
    ID: NEXRAD-PuertoRico
    NEXRAD Puerto Rico Base Reflectivity
  306. NOAA-15 Orbit
    ID: POESNAV-N15
  307. NOAA-18 Orbit
    ID: POESNAV-N18
  308. NOAA-20 Orbit
    ID: POESNAV-N20
  309. NOAA-20 VIIRS Daily DNB (Adaptive)
    ID: j01-viirs-adaptive-dnb-daily
    j01-viirs-adaptive-dnb-daily
  310. NOAA-20 VIIRS Daily I02
    ID: j01-viirs-i02-daily
    j01-viirs-i02-daily
  311. NOAA-20 VIIRS Daily I05
    ID: j01-viirs-i05-daily
    j01-viirs-i05-daily
  312. NOAA-20 VIIRS Daily I05 (tops)
    ID: j01-viirs-i05-daily-tops
    View of j01-viirs-i05-daily
  313. NOAA-20 VIIRS DNB (Swaths)
    ID: j01-viirs-dnb-swath
    View of j01-viirs-bands-night-swath
  314. NOAA-20 VIIRS False Color (Daily Composite)
    ID: j01-viirs-false-color-daily
    View of j01-viirs-false-color-swath
  315. NOAA-20 VIIRS False Color (Hourly Composite)
    ID: j01-viirs-false-color-hourly
    View of j01-viirs-false-color
  316. NOAA-20 VIIRS False Color (Swaths)
    ID: j01-viirs-false-color-swath
    View of j01-viirs-false-color
  317. NOAA-20 VIIRS Hourly DNB (Adaptive)
    ID: j01-viirs-adaptive-dnb
    j01-viirs-adaptive-dnb
  318. NOAA-20 VIIRS Hourly I02
    ID: j01-viirs-i02
    j01-viirs-i02
  319. NOAA-20 VIIRS Hourly I05
    ID: j01-viirs-i05
    j01-viirs-i05
  320. NOAA-20 VIIRS Hourly I05 (tops)
    ID: j01-viirs-i05-tops
    View of j01-viirs-i05
  321. NOAA-20 VIIRS M-Band Fire RGB (Swaths)
    ID: j01-viirs-swath-fire-color
    This image is made by on-the-fly combining VIIRS bands M11 (2.25um) as red, M7 (8.66um) as green, and M4 (5.55) as blue. Because the M11 shortwave infrared band is sensitive to bright fires, it highlights active especially hot fires in red while preserving a natural color appearance in the rest of the image.
  322. NOAA-20 VIIRS M-Band Fire Temp (Swaths)
    ID: j01-viirs-swath-fire-temp
    On-the-fly combination of bands 11, 10, 12.
  323. NOAA-20 VIIRS True Color (Daily Composite)
    ID: j01-viirs-true-color-daily
    View of j01-viirs-true-color-swath
  324. NOAA-20 VIIRS True Color (Hourly Composite)
    ID: j01-viirs-true-color-hourly
    View of j01-viirs-true-color
  325. NOAA-20 VIIRS True Color (Swaths)
    ID: j01-viirs-true-color-swath
    View of j01-viirs-true-color
  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. NUCAPS CAA Temp 180mb
    ID: NUCAPS-CAA-temp-180mb
  334. NUCAPS CAA Temp 200mb
    ID: NUCAPS-CAA-temp-200mb
  335. NUCAPS CAA Temp 235mb
    ID: NUCAPS-CAA-temp-235mb
  336. NUCAPS CAA Temp 260mb
    ID: NUCAPS-CAA-temp-260mb
  337. NUCAPS CAA Temp 286mb
    ID: NUCAPS-CAA-temp-286mb
  338. NWS Alerts (All)
    ID: NWS-Alerts-All
    All NWS Alerts
  339. NWS County Warning Areas
    ID: NWSCWA
    NWS County Warning Areas
  340. NWSWARNS12Z12Z
    ID: NWSWARNS12Z12Z
    NWSWARNS12Z12Z (Severe and Tornado. No SVSs)
  341. NWS Watches and Warnings
    ID: NWS-Alerts-Warnings
    Watches and warnings from NWS
  342. OPC & TAFB Offshore Zones
    ID: OFFSHOREZONES
  343. Overshooting Tops targets
    ID: CIMSS-OTtargets
    Cloud OverShooting Tops targets
  344. Pilot Reports
    ID: PIREP
    PIREP
  345. PLTG GOES-East CONUS
    ID: PLTGGOESEastRadC
    Probability of lightning in the next 60 min
  346. PLTG GOES-East FD (OCONUS)
    ID: PLTGGOESEastRadF
  347. PLTG GOES-East MESO1
    ID: PLTGGOESEastRadM1
    Probability of lightning in the next 60 min
  348. PLTG GOES-East MESO2
    ID: PLTGGOESEastRadM2
    Probability of lightning in the next 60 min
  349. PLTG GOES-East RadC Alabama
    ID: PLTGGOESEastRadCAL
  350. PLTG GOES-West CONUS
    ID: PLTGGOESWestRadC
  351. PLTG GOES-West MESO1
    ID: PLTGGOESWestRadM1
  352. PLTG GOES-West MESO2
    ID: PLTGGOESWestRadM2
  353. PNPOINTALL
    ID: PNPOINTALL
    PNPOINTALL
  354. PNTRACKALL
    ID: PNTRACKALL
    PNTRACKALL
  355. 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.
  356. PROBSEVACCUM
    ID: PROBSEVACCUM
    ≥ 50%
  357. ProbSevere
    ID: ProbSevere
    ProbSevere
  358. ProbSevere (version2)
    ID: PROBSEVERE
    The probability of any severe is the max(ProbHail,ProbWind,ProbTor).
  359. ProbSevere (version 3)
    ID: PROBSEVEREV3
    PSv3 models use a machine-learning model called gradient-boosted decision trees.
  360. ProbSevere Accumulation 20% to 49%
    ID: PROBSEVACCUMLOW
    ProbSevere Accumulation 20% to 49%
  361. PROBSEVTESTACCUM
    ID: PROBSEVTESTACCUM
  362. PROBSEVTESTACCUMLOW
    ID: PROBSEVTESTACCUMLOW
  363. PROBTOR
    ID: PROBTOR
  364. PROBTORACCUM
    ID: PROBTORACCUM
  365. PSNCO
    ID: PSNCO
  366. PSNSSL
    ID: PSNSSL
  367. Quantitative Precip Forecast
    ID: QPF6hr
    WPC 6hr Quantitative Precip Forecast QPF (in)
  368. Quantitative Precipitation Forecast
    ID: WPC-QPF
    WPC-QPF
  369. RAP ConUS Latest Simulated Radar
    ID: RAP-CONUS-PRAT-SFC-DBZ
    View of RAP-CONUS-PRAT-SFC
  370. RAP North America Near Surface Smoke
    ID: RAP-smoke-surface
    The Rapid Refresh (RAP) is the continental-scale NOAA hourly-updated assimilation/modeling system operational at NCEP. RAP covers North America and is comprised primarily of a numerical forecast model and an analysis/assimilation system to initialize that model. The RAP has a resolution of 13.5km and includes smoke forecast variables derived in part from VIIRS satellite inputs. RAP is complemented by the higher-resolution 3km High-Resolution Rapid Refresh (HRRR) model, which is also updated hourly and covering a smaller geographic domain.
  371. RAP North America Vertically Integrated Smoke
    ID: RAP-smoke-column
    The Rapid Refresh (RAP) is the continental-scale NOAA hourly-updated assimilation/modeling system operational at NCEP. RAP covers North America and is comprised primarily of a numerical forecast model and an analysis/assimilation system to initialize that model. The RAP has a resolution of 13.5km and includes smoke forecast variables derived in part from VIIRS satellite inputs. RAP is complemented by the higher-resolution 3km High-Resolution Rapid Refresh (HRRR) model, which is also updated hourly and covering a smaller geographic domain.
  372. 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
  373. 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)
  374. 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)
  375. 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)
  376. River Flood: 1 day VIIRS composite
    ID: RIVER-FLDglobal-composite1
    CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 1 day. For more information visit: Here
  377. River Flood: 5 day VIIRS composite
    ID: RIVER-FLDglobal-composite
    CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 5 days. For more information visit: Here
  378. 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
  379. 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
  380. 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
  381. 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
  382. River Flood: 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
  383. 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
  384. 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
  385. 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
  386. River Flood: Joint ABI/VIIRS
    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
  387. River Flood: Joint AHI/VIIRS
    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
  388. 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
  389. 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
  390. 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
  391. 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
  392. 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
  393. 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
  394. 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
  395. 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
  396. 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
  397. 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
  398. 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
  399. 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
  400. 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
  401. 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
  402. 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
  403. 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
  404. 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
  405. 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)
  406. 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)
  407. 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)
  408. 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.
  409. Sea Surface Temperature
    ID: NESDIS-SST
    NESDIS: Hi-Res Sea Surface Temperature
  410. SENTINEL 2A Orbit
    ID: POESNAV-SEN2A
    POESNAV-SEN2A
  411. SENTINEL 2B Orbit
    ID: POESNAV-SEN2B
    POESNAV-SEN2B
  412. Severe Weather Outlook Day2
    ID: SPCsvday2
    Severe Weather Outlook Day2
  413. Severe Weather Outlook Day3
    ID: SPCsvday3
    Severe Weather Outlook Day3
  414. Severe Weather Outlook Day4
    ID: SPCsvday4
    Severe Weather Outlook Day4
  415. Severe Weather Outlook Day5
    ID: SPCsvday5
    Severe Weather Outlook Day5
  416. Severe Weather Warning Outlines
    ID: SevereOutl
    Tornado, Thunderstorm, Flash Flood and Marine Warnings (outlines only, no fill)
  417. Severe Weather Warnings
    ID: Severe
    Tornado, Thunderstorm, Flash Flood and Marine Warning polygons.
  418. Severe Weather Warning Vectors
    ID: SevereVect
    Tornado and Thunderstorm Warning Vectors
  419. Severe Weather Watch Box
    ID: SAW
    Severe Weather Watch Box - Aviation
  420. Severe Wind Outlook Day1
    ID: SPCwnday1
    Severe Wind Outlook Day1 (%)
  421. Ship & Buoy
    ID: SSEC-ShipBuoy
    Global Ship & Buoy
  422. Snow Depth (SNODAS)
    ID: SNODAS-Thickness
    SNODAS (SNOw Data Assimilation System) is a modeling and data assimilation system developed by NOAA National Weather Service"s NOHRSC (National Operational Hydrologic Remote Sensing Center) to provide the best possible estimates of snow cover and associated parameters to support hydrologic modeling and analysis. The aim of SNODAS is to provide a physically consistent framework to integrate snow data from satellite, airborne platforms, and ground stations with model estimates of snow cover. The 24hr Snow Thickness is a daily snapshot of snow thickness at 0600hr UTC.
  423. Snowfall Probability >= 4" Final Forecast
    ID: WPC-psnowgt04
    WPC-psnowgt04
  424. Snowfall Probability >= 8" Final Forecast
    ID: WPC-psnowgt08
    WPC-psnowgt08
  425. Snowfall Probability >= 12" Final Forecast
    ID: WPC-psnowgt12
    WPC-psnowgt12
  426. Snow Fall Rate
    ID: NESDIS-SnowFallRate
    AMSU Snow Fall Rate Global by NOAA-NESDIS
  427. Snowfall Reports - 6hr
    ID: lsr-snow
    NWS reported 6hr Snowfall Totals (inches).
  428. Snowfall Total - 24hr (SNODAS)
    ID: SNODAS-Accumulate
    SNODAS (SNOw Data Assimilation System) is a modeling and data assimilation system developed by NOAA National Weather Service"s NOHRSC (National Operational Hydrologic Remote Sensing Center) to provide the best possible estimates of snow cover and associated parameters to support hydrologic modeling and analysis. The aim of SNODAS is to provide a physically consistent framework to integrate snow data from satellite, airborne platforms, and ground stations with model estimates of snow cover. 24hr Snow Fall Total is calculated every 24 hours at 0600hr UTC and posted shortly thereafter.
  429. Snow Probability >= 0.1"/24h
    ID: WPC-psnow24gep1
    24Hour Probability of Snow Accumulating ≥.1" The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
  430. Snow Probability >= 1.0"/24h
    ID: WPC-psnow24ge1
    24Hour Probability of Snow Accumulating ≥1" The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
  431. Snow Probability >= 2.0"/24h
    ID: WPC-psnow24ge2
    24Hour Probability of Snow Accumulating ≥2" The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
  432. Snow Probability >= 4.0"/24h
    ID: WPC-psnow24ge4
    24Hour Probability of Snow Accumulating ≥4" The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
  433. Snow Probability >= 6.0"/24h
    ID: WPC-psnow24ge6
    24Hour Probability of Snow Accumulating ≥6" The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
  434. Snow Probability >= 8.0"/24h
    ID: WPC-psnow24ge8
    24Hour Probability of Snow Accumulating ≥8" The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
  435. Snow Probability >= 12.0"/24h
    ID: WPC-psnow24ge12p0
    24Hour Probability of Snow Accumulating ≥12" The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
  436. Snow Probability >= 18.0"/24h
    ID: WPC-psnow24ge18p0
    24Hour Probability of Snow Accumulating ≥18" The operational WPC Winter Weather Desk (WWD) creates 24-h forecasts of snowfall and freezing rain accumulations for each of three consecutive 24-h periods (days) extending 72 hours into the future. These products are shared with the NWS Weather Forecast Offices (WFO) in a collaborative process resulting in refinement of the accumulation forecasts. After the 24-h snowfall and freezing rain accumulation forecasts are finalized, the WWD issues its public products: a limited suite of probabilistic winter weather forecasts. These probabilistic forecasts are computed based on the deterministic accumulation forecasts combined with ensemble information.
  437. SNPP Day/Night AM Composite - Adaptive
    ID: nppadpam
    NPP Day/Night AM Composite - Adaptive
  438. SNPP Day/Night Band (DNB) - Honolulu DB
    ID: nppdnbdyn-hnl
    NPP Day/Night Band (DNB) - Honolulu DB
  439. SNPP Day/Night Band (DNB) - Madison DB
    ID: nppdnbdyn-msn
    Suomi NPP Day/Night Band (DNB) imagery received and processed by the SSEC UW-Madison direct reception facility by Direct Broadcast from the satellite.
  440. SNPP Day/Night Band (DNB) - Puerto Rico DB
    ID: nppdnbdyn-upr
    NPP Day/Night Band (DNB) - Puerto Rico DB
  441. SNPP Day/Night Band - Dynamic
    ID: nppdnb
    NPP Day/Night Band - Dynamic
  442. SNPP False Color
    ID: nppfc
    NPP False Color
  443. SNPP NUCAPS CO-MR-496mb
    ID: CO-MR-496mb
    This a proof of concept example of NUCAPS from Suomi NPP CrIS/ATMS data, converted to a gridded NetCDF.
  444. SNPP Orbit
    ID: POESNAV-NPP
  445. SNPP Sea Surface Temperature
    ID: nppsst
    NPP Sea Surface Temperature
  446. SNPP Sea Surface Temperature (SST) - Madison DB
    ID: nppsst-msn
    NPP Sea Surface Temperature (SST) - Madison DB
  447. SNPP True Color (TC) - Honolulu DB
    ID: npptc-hnl
    NPP True Color (TC) - Honolulu DB
  448. SNPP True Color (TC) - Puerto Rico DB
    ID: npptc-upr
    NPP True Color (TC) - Puerto Rico DB
  449. SNPP VIIRS Daily DNB (Adaptive)
    ID: npp-viirs-adaptive-dnb-daily
    npp-viirs-adaptive-dnb-daily
  450. SNPP VIIRS Daily I02
    ID: npp-viirs-i02-daily
    npp-viirs-i02-daily
  451. SNPP VIIRS Daily I05
    ID: npp-viirs-i05-daily
    npp-viirs-i05-daily
  452. SNPP VIIRS Daily I05 (tops)
    ID: npp-viirs-i05-daily-tops
    View of npp-viirs-i05-daily
  453. SNPP VIIRS DNB (Swaths)
    ID: npp-viirs-dnb-swath
    View of npp-viirs-bands-night-swath
  454. SNPP VIIRS DNB (Swaths)
    ID: npp-viirs-dnb-swath
    View of npp-viirs-bands-night-swath
  455. SNPP VIIRS False Color (Daily Composite)
    ID: npp-viirs-false-color-daily
    View of npp-viirs-false-color-swath
  456. SNPP VIIRS False Color (Daily Composite)
    ID: npp-viirs-false-color-daily
    View of npp-viirs-false-color-swath
  457. SNPP VIIRS False Color (Hourly Composite)
    ID: npp-viirs-false-color-hourly
    View of npp-viirs-false-color
  458. SNPP VIIRS False Color (Hourly Composite)
    ID: npp-viirs-false-color-hourly
    View of npp-viirs-false-color
  459. SNPP VIIRS False Color (Swaths)
    ID: npp-viirs-false-color-swath
    View of npp-viirs-false-color
  460. SNPP VIIRS False Color (Swaths)
    ID: npp-viirs-false-color-swath
    View of npp-viirs-false-color
  461. SNPP VIIRS False Color - Madison DB
    ID: nppfc-msn
    Suomi-NPP VIIRS False Color imagery received and processed by the SSEC UW-Madison direct reception facility by Direct Broadcast from the satellite.
  462. SNPP VIIRS Fire RGB (Swaths)
    ID: npp-viirs-swath-fire-color
    View of npp-viirs-bands-day-swath
  463. SNPP VIIRS Fire RGB (Swaths)
    ID: npp-viirs-swath-fire-color
    View of npp-viirs-bands-day-swath
  464. SNPP VIIRS Fire Temp (Swaths)
    ID: npp-viirs-swath-fire-temp
    View of npp-viirs-bands-day-swath
  465. SNPP VIIRS Fire Temp (Swaths)
    ID: npp-viirs-swath-fire-temp
    View of npp-viirs-bands-day-swath
  466. SNPP VIIRS Hourly DNB (Adaptive)
    ID: npp-viirs-adaptive-dnb
    npp-viirs-adaptive-dnb
  467. SNPP VIIRS Hourly I02
    ID: npp-viirs-i02
    npp-viirs-i02
  468. SNPP VIIRS Hourly I05
    ID: npp-viirs-i05
    npp-viirs-i05
  469. SNPP VIIRS Hourly I05 (tops)
    ID: npp-viirs-i05-tops
    View of npp-viirs-i05
  470. SNPP VIIRS True Color (Daily Composite)
    ID: npp-viirs-true-color-daily
    View of npp-viirs-true-color-swath
  471. SNPP VIIRS True Color (Daily Composite)
    ID: npp-viirs-true-color-daily
    View of npp-viirs-true-color-swath
  472. SNPP VIIRS True Color (Hourly Composite)
    ID: npp-viirs-true-color-hourly
    View of npp-viirs-true-color
  473. SNPP VIIRS True Color (Hourly Composite)
    ID: npp-viirs-true-color-hourly
    View of npp-viirs-true-color
  474. SNPP VIIRS True Color (Swaths)
    ID: npp-viirs-true-color-swath
    View of npp-viirs-true-color
  475. SNPP VIIRS True Color (Swaths)
    ID: npp-viirs-true-color-swath
    View of npp-viirs-true-color
  476. SNPP VIIRS True Color - Madison DB
    ID: npptc-msn
    Suomi-NPP VIIRS True Color imagery received and processed by the SSEC UW-Madison direct reception facility by Direct Broadcast from the satellite.
  477. SPC reports 12Z to 12Z
    ID: SPCREPS12Z12Z
    SPCREPS12Z12Z
  478. 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
  479. Storm Cell Id and Tracking - Track
    ID: SCIT
    Storm Cell Id and Tracking - Track
  480. Storm Reports 3hrs
    ID: StormReports
    Storm Reports (last 3hrs)
  481. Storm Reports 24hrs
    ID: StormReports24
    Storm Reports (last 24hrs)
  482. Stroke Density XP
    ID: XLSD
    XLSD - Experimental product, Restricted to SSEC internal use only!
  483. SVRWARNS12Z12Z
    ID: SVRWARNS12Z12Z
  484. Terminal Area Forecasts
    ID: TAF
    Terminal Aerodrome Forecast (TAF)
  485. Terra Aerosol Optical Depth
    ID: TERRA-AER
    MODIS: TERRA Aerosol Optical Depth (ta)
  486. Terra False Color
    ID: terrafalsecolor
    CIMSS-MODIS Satellite False Color (Terra)
  487. Terra Land Surface True Color
    ID: GLOBALterratc
    MODIS: Terra land Surface True Color composite
  488. TERRA Orbit
    ID: POESNAV-TERRA
  489. Terra True Color
    ID: terratruecolor
    CIMSS-MODIS Satellite True Color (Terra)
  490. TEST
    ID: TEST
  491. TESTGRBRADF
    ID: TESTGRBRADF
    TESTGRBRADF
  492. Thunderstorm Watches/Warnings
    ID: WWSEVTRW
    Thunderstorm Watches and Warnings
  493. Tornado Outlook Day1
    ID: SPCtnday1
    Tornado Outlook Day1 (%)
  494. Tornado Watches/Warnings
    ID: WWTORNADO
    Tornado Watches and Warnings
  495. Tornado Watches and Warnings
    ID: WWTOR
    Tornado Watches and Warnings
  496. TORWARNS12Z12Z
    ID: TORWARNS12Z12Z
  497. Total Column Sulphur Dioxide
    ID: AURA-SO2
    AURA - OMI Total Column Sulphur Dioxide (SO2)
  498. Tropical Storm & Hurricane Forecast
    ID: TSFCST
    National Hurricane Center Tropical Storm & Hurricane Forecast
  499. True Color Clear View
    ID: BRDF
    MODIS Clear View ConUS Composite. BRDF (Bidirectional Reluctance Distribution Function) is a 16-day cloud-free composite.
  500. TS HDOB - Atlantic points
    ID: TSHDOBATLparm
    TS HDOB - Atlantic points
  501. TS HDOB - Atlantic winds
    ID: TSHDOBATL
    TS HDOB - Atlantic winds
  502. TS HDOB - EPacific points
    ID: TSHDOBEPACparm
    TS HDOB - EPacific points
  503. TS HDOB - EPacific winds
    ID: TSHDOBEPAC
    TS HDOB - EPacific winds
  504. Turbulence Advisory
    ID: AIRMET-TURB
    AIRMET-Turlulence Advisory
  505. US Landsat Analysis Ready Data (ARD) Grids
    ID: usgs-ard-grid
    This product shows the Analysis Ready Data (ARD) grids for Landsat satellite data. It includes all three grids for the contiguous United States (CONUS), Alaska, and Hawaii. Landsat data have been produced, archived, and distributed by the U.S. Geological Survey (USGS) since 1972. Users rely upon these data for conducting historical studies of land surface change, but they have shouldered the burden of post-production processing to create application-ready datasets. To alleviate this burden on the user, the USGS has initiated an effort to produce a collection of Landsat Science Products to support land surface change studies. The effort involves re-gridding Landsat imagery in regular 150km x 150km squares using an Albers Equal Area projection.
  506. VIIRS Aerosol Optical Depth (AOD) - GINA
    ID: AOD-RGB-GINA
    Aerosol optical depth is a measure of the extinction of the solar beam by dust and haze. In other words, particles in the atmosphere (dust, smoke, pollution) can block sunlight by absorbing or by scattering light. AOD tells us how much direct sunlight is prevented from reaching the ground by these aerosol particles. It is a dimensionless number that is related to the amount of aerosol in the vertical column of atmosphere over the observation location. A value of 0.01 corresponds to an extremely clean atmosphere, and a value of 0.4 would correspond to a very hazy condition. An average aerosol optical depth for the U.S. is 0.1 to 0.15.
  507. VIIRS Fire RGB - CIRA
    ID: VIIRS-Fire-RGB-CIRA
    This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University.
  508. VIIRS Fire RGB - GINA
    ID: DayLandCloudFire-RGB-GINA
    This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic Information Network of Alaska (GINA).
  509. VIIRS Fire Temp RGB - CIRA
    ID: VIIRS-Fire-Temp-RGB-CIRA
    This RGB is created by assigning the VIIRS 3.74um channel to red, 2.25um channel to green, and the 1.61um channel to blue. It is used to assess fire intensity and size, with fires ranging from red (smallest/lowest intensity) to yellow to white (hottest or most intense). These data are produced by the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University.
  510. VIIRS Fire Temp RGB - GINA
    ID: FireTemperature-RGB-GINA
    This RGB is created by assigning the VIIRS 3.74um channel to red, 2.25um channel to green, and the 1.61um channel to blue. It is used to assess fire intensity and size, with fires ranging from red (lowest) to yellow to white (hottest or biggest). These data are produced by the Geographic Information Network of Alaska (GINA).
  511. VIIRS Fire Temp RGB 375m CIRA
    ID: VIIRS-Fire-Temp-RGB-375-CIRA
    This RGB is created by assigning the VIIRS 3.74um channel to red, 2.25um channel to green, and the 1.61um channel to blue. It is used to assess fire intensity and size, with fires ranging from red (smallest/lowest intensity) to yellow to white (hottest or most intense). These data are produced by the Cooperative Institute for Research in the Atmosphere (CIRA) at Colorado State University.
  512. VIIRS Floodwater Depth
    ID: VIIRS-3Dflood
    VIIRS downscaling software is designed to downscale the VIIRS 375-m flood products to 30-m flood products. The software uses Suomi-NPP & NOAA-20 VIIRS floodwater fraction product and the 30-m SRTM/DEM in the CONUS with a series of ancillary datasets including SRTM Water Body Dataset (SWBD), 30-m CONUS Land Cover Dataset, 30-m CONUS Canopy Dataset, and NHDPlus Version 2 river lines and water shed datasets to derive the vertical inundation information. The process is done based on each level-4 hydrologic unit (HUC-4) and the final outputs cover the flooded regions with 30-m floodwater depth.
  513. VIIRS i04 - GINA
    ID: VIIRS-i04-GINA
    This is the VIIRS 3.74um single channel i-band with 376 m resolution. It is an IR channel that is very sensitive to fires and hot spots and is available day or night. A special colormap is used to enhance the warm-hot pixels. The sensors can become saturated by very intense fires and daytime radiance values can affected by reflected sunlight. These data are produced by the Geographic Information Network of Alaska (GINA).
  514. VIIRS NDVI 16-day Composite
    ID: NDVI-16day-before
    This CONUS NDVI product is clipped from the global VIIRS composite product VNP13A1-001 using AppEEARS at the NASA LPDAAC. The spatial resolution is 500m. It is made with in alternating 8-day cycles from best available pixels. See link below for more information.
  515. VIIRS Snowmelt - GINA
    ID: VIIRS-Snowmelt-GINA
    This RGB is created by assigning the VIIRS 1.61um channel to red, 1.24um channel to green, and the 0.64um channel to blue. The blue shades identify snow cover characteristics. Darker blue shows wetter or older snow and lighter blues show drier or newer snow. These data are produced by the Geographic Information Network of Alaska (GINA).
  516. Vis Winds 800-700mb
    ID: AMV-VISmid
    AMV: Middle Level Visible (700-800mb)
  517. Vis Winds 925-801mb
    ID: AMV-VISlow
    AMV: Lower Level Visible (801-925mb)
  518. Volcanic Ash Adv plumes
    ID: VAA
    Volcanic Ash Advisories: Ash Clouds
  519. 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.
  520. WI Coastal LiDAR
    ID: WIcoastallidar
    WI Coastal LiDAR
  521. WI Coastal Shaded Relief
    ID: WIcoastalshdrlf
    WI coastal shaded relief map generated from LiDAR data.
  522. 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).
  523. Williams-Flats-DNB
    ID: Willams-Flats-DNB
    Night time image from the DNB (Day/Night Band) of the VIIRS sensor on S-NPP and NOAA-20 that represents panchromatic spectra covering a wavelength range of 500–900 nm.
  524. Williams Flats Fire VIIRS False Color
    ID: Willams-Flats-fc
    A false color composite from the JPSS-VIIRS sensor on S-NPP and NOAA-20 using IR, Near-IR, and Red for RGB. 375m resolution.
  525. Williams Flats Fire VIIRS True Color
    ID: Willams-Flats
    A true color composite from the JPSS-VIIRS sensor on S-NPP and NOAA-20 using Red, Green, and Blue as RGB. 375m resolution.
  526. 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.
  527. WI Nordic Ski Trails
    ID: SKITrails
    SKITrails
  528. 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.
  529. 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.
  530. 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.
  531. Wisconsin Counties
    ID: wi-counties-basic
  532. Wisconsin in 3D (SRTM)
    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).
  533. 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.
  534. 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.
  535. WSSI Blowing Snow
    ID: WPC-WSSI-BlowingSnow
    The WSSI is created by screening the official National Weather Service gridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or static information datasets (e.g., climatology, land-use, urban areas). This process creates a graphical depiction of anticipated overall impacts to society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts. Blowing Snow Index Indicates the potential disruption due to blowing and drifting snow. This index accounts for land use type. For example, more densely forested areas will show less blowing snow than open grassland areas.
  536. WSSI Flash Freeze
    ID: WPC-WSSI-FlashFreeze
    The WSSI is created by screening the official National Weather Service gridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or static information datasets (e.g., climatology, land-use, urban areas). This process creates a graphical depiction of anticipated overall impacts to society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts. Flash Freeze Index Indicates the potential impacts of flash freezing (temperatures starting above freezing and quickly dropping below freezing) during or after precipitation events
  537. WSSI Ground Blizzard
    ID: WPC-WSSI-Blizzard
    The WSSI is created by screening the official National Weather Service gridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or static information datasets (e.g., climatology, land-use, urban areas). This process creates a graphical depiction of anticipated overall impacts to society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts. Ground Blizzard indicates the potential travel-related impacts of strong winds interacting with pre-existing snow cover. This is the only sub-component that does not require snow to be forecast in order for calculations to be made. The NOHRSC snow cover data along with forecast winds are used to model the ground blizzard.
  538. WSSI Ice Accumulation
    ID: WPC-WSSI-IceAccum
    The WSSI is created by screening the official National Weather Service gridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or static information datasets (e.g., climatology, land-use, urban areas). This process creates a graphical depiction of anticipated overall impacts to society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts. Ice Accumulation indicates potential infrastructure impacts (e.g. roads/bridges) due to combined effects and severity of ice and wind. Designated urban areas are also weighted a little more than non-urban areas. Please note that not all NWS offices provide ice accumulation information into the NDFD. In those areas, the ice accumulation is not calculated.
  539. WSSI Overall Impact
    ID: WPC-WSSI
    The WSSI is created by screening the official National Weather Service gridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or static information datasets (e.g., climatology, land-use, urban areas). This process creates a graphical depiction of anticipated overall impacts to society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts. The Overall WSSI Impact value is the maximum value from all the sub-components. The specific sub-components are: ● Snow Load Index ● Snow Amount Index ● Ice Accumulation ● Blowing Snow Index ● Flash Freeze Index ● Ground Blizzard
  540. WSSI Snow Amount
    ID: WPC-WSSI-SnowAmount
    The WSSI is created by screening the official National Weather Service gridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or static information datasets (e.g., climatology, land-use, urban areas). This process creates a graphical depiction of anticipated overall impacts to society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts. Snow Amount indicates potential impacts due to the total amount of snow or the snow accumulation rate. This index also normalizes for climatology, such that regions of the country that experience, on average, less snowfall will show a higher level of severity for the same amount of snow that is forecast across a region that experiences more snowfall on average.
  541. WSSI Snow Load
    ID: WPC-WSSI-SnowLoad
    The WSSI is created by screening the official National Weather Service gridded forecasts from the National Digital Forecast Database (NDFD) for winter weather elements and combining those data with non-meteorological or static information datasets (e.g., climatology, land-use, urban areas). This process creates a graphical depiction of anticipated overall impacts to society due to winter weather. NWS has implemented the WSSI to provide the public with a tool that attempts to convey the complexities and hazards associated with winter storms as they relate to potential societal impacts. Snow Load indicates potential infrastructure impacts due to the weight of the snow. This index accounts for the land cover type. For example, more forested and urban areas will show increased severity versus the same snow conditions in grasslands.