RealEarth™ Product Inventory



Collection:

Alphabetic list of 599 products:

  1. 24hr Snow Depth
    [SNOWDEPTH24]

    24hr SnowDepth (in)

  2. 24hr Snow Fall
    [SNOWFALL24]

    24hr SnowFall (in)

  3. 2015 WI NAIP Counties
    [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
    [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
    [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
    [AQUA-AER]

    MODIS: AQUA Aerosol Optical Depth (ta)

  7. Aqua False Color
    [aquafalsecolor]

    CIMSS-MODIS Satellite False Color (Aqua)

  8. AQUA Orbit
    [POESNAV-AQUA]


  9. Australia DNB 2019 - Dynamic
    [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
    [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
    [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
    [NESDIS-BTPWgps]

    NESDIS-BTPWgps

  13. Blended TPW Percent
    [NESDIS-BTPWpct]

    NESDIS-BTPWpct

  14. CaliforniaView MODIS Terra Truecolor
    [CA-MODIS-Terra-123-True]

    CaliforniaView: True color 250m MODIS image on May 3rd, 2022

  15. Cladophora Classification
    [clad]

    Estimate of 2005 algae extent along coastal Lake Michigan.

  16. Cloud Top Cooling targets
    [CIMSS-CTCtargets]

    CIMSS-Cloud Top Cooling targets

  17. CMORPH2 1-Day Precip Accumulation
    [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 polar or "low earth" (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 1-Hour Precip Accumulation
    [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 polar or "low earth" (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. CMORPH2 7-Day Precip Accumulation
    [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 polar or "low earth" (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).

  20. COModis721
    [COView721]

    COView721

  21. Composite River Ice: Alaska
    [ICE-COMP-AP]


  22. Composite River Ice: Missouri Basin
    [ICE-COMP-MB]


  23. Composite River Ice: North Central
    [ICE-COMP-NC]


  24. Composite River Ice: Northeast
    [ICE-COMP-NE]


  25. Convective Outlook - Categorical
    [SPC-ConvOutlook-CATG]

    SPC Convective Outlook - Categorical

  26. Convective Outlook - Categorical (color map)
    [SPC-ConvOutlook-CATG-cmap]

    View of SPC-ConvOutlook-CATG

  27. Convective Outlook Day1
    [SPCcoday1]

    Convective Outlook Day1 (Category) id=SPCcoday1

  28. Convective Outlook Day2
    [SPCcoday2]

    Convective Outlook Day2 (Category)

  29. Convective Outlook Day3
    [SPCcoday3]

    Convective Outlook Day3 (Categorical)

  30. coview-721
    [coview-721]

    coview-721

  31. CSPP VIIRS Flood Detection
    [cspp-flood]

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

  32. CSPP VIIRS Flood Detection (no cloud)
    [cspp-flood-nocloud]

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

  33. CSPP VIIRS Flood Detection - Global (no clouds)
    [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.

  34. Current Large Fires
    [Current-Fires]

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

  35. DelawareView-Landsat
    [DelawareView-Landsat]

    DelawareView-Landsat

  36. DNB ClearView
    [DNB-ClearView]

    DNB-ClearView

  37. DNB ClearView Monthly - Test
    [dnb-monthly-nightlights]


  38. Earthquake Magnitude
    [Earthquake-mag]

    Earthquake Magnitude (Past 24hr)

  39. Eclipse Path
    [Eclipse]

    Eclipse Path

  40. Excessive Rainfall Outlook
    [ERTA-NCEP]

    Excessive Rainfall Outlook: In the Excessive Rainfall Outlooks, the Weather Prediction Center (WPC) forecasts the probability that rainfall will exceed flash flood guidance at a point. Gridded FFG is provided by the twelve NWS River Forecast Centers (RFCs) whose service areas cover the lower 48 states. NCEP creates a national mosaic of FFG, whose 1, 3, and 6-hour values represent the amount of rainfall over those short durations which it is estimated would bring rivers and streams up to bankfull conditions. WPC estimates the likelihood that FFG will be exceeded by assessing environmental conditions (e.g. moisture content and steering winds), recognizing weather patterns commonly associated with heavy rainfall, and using a variety of deterministic and ensemble-based numerical model tools

  41. Excessive Rain Forecast
    [WPC-ExcessiveRain]

    WPC-ExcessiveRain

  42. Fire Danger Index Africa
    [ZAFDI]

    MODIS Fire Danger Index South Africa by CIMSS-DBCRAS

  43. Fire Danger Index ConUS
    [CONUSFDI]

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

  44. Fire Hazards (Issued)
    [REDFLAG]

    REDFLAG

  45. Fire Hazards (Valid)
    [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

  46. Fire Radiative Power VIIRS I-band - GINA
    [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
    [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 (color map)
    [SPC-FireOutlook-CATG-cmap]

    The Fire Weather Outlooks are intended to delineate areas of the continental U.S. where pre-existing fuel conditions, combined with forecast weather conditions, will result in a significant threat for the ignition and/or spread of wildfires. This product is designed for use in the NWS, as well as other federal, state, and local government agencies.

  49. Fire Weather Outlook Day1
    [SPCfwday1]

    Fire Weather Outlook Day1 (Category)

  50. Fire Weather Outlook Day2
    [SPCfwday2]

    Fire Weather Outlook Day2 (Category)

  51. Flash Flood Hazards (Zones)
    [WFLASH]

    Flash Flood Hazards

  52. Flood Hazards (Zones)
    [WWFLOOD]

    Flood Watches and Warnings

  53. Flood Outlook Product
    [FOP]

    WPC FLood Outlook Product

  54. Flood Warnings (Issued)
    [FLOODWARN]

    Flood Warning Polygons

  55. Flood Warnings (Valid)
    [XFLOODWARN]

    XFLOODWARN

  56. Flood Warnings Hydrological-VTEC (Issued)
    [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.

  57. Fog Hazards
    [WFOG]

    Fog Hazards

  58. Freezing Rain Probability >= .25" Final Forecast
    [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.

  59. Freezing Rain Probability >= 0.01"/24h
    [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.

  60. Freezing Rain Probability >= 0.10"/24h
    [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.

  61. Freezing Rain Probability >= 0.25"/24h
    [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.

  62. Freezing Rain Probability >=0.50"/24h
    [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.

  63. Freezing Rain Probability >=1.00"/24h
    [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.

  64. Fronts and Troughs
    [Fronts]

    NCEP Frontal Analysis: fronts and troughs

  65. glmgroupdensity-west
    [glmgroupdensity-west]


  66. Global Black Marble
    [VIIRS-MASK-54000x27000]

    VIIRS Night Global Black Marble by NASA

  67. Global Infrared
    [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.

  68. Global Infrared - Aviation
    [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.

  69. Global Infrared - Dvorak
    [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.

  70. Global Infrared - Funk Top
    [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.

  71. Global Infrared - Rainbow
    [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.

  72. Global Infrared - Rain Rate
    [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.

  73. Global Infrared - Tops
    [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.

  74. Global Night Lights
    [NightLightsColored]

    Global Night Lights (enhanced)

  75. Global Visible
    [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.

  76. Global Visible (transparent Night)
    [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.

  77. Global Visible - fill
    [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.

  78. Global Visible - full
    [global1kmvisfull]


  79. Global Water Vapor
    [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.

  80. Global Water Vapor - Gradient
    [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.

  81. GOES-East FED (deprecated)
    [FlashExtentDensity]

    GOES-East flash extent density 5-min accumulation of footprint of all observed flashes

  82. GOES-East GLM FED CONUS
    [GOESEastGLMFEDRadC]

    GOES-East flash-extent density, a 5-min accumulation of flashes at each point.

  83. GOES-East GLM MFA CONUS
    [GOESEastGLMMFARadC]

    GOES-East minimum flash density

  84. GOES-East GLM TOE CONUS
    [GOESEastGLMTOERadC]

    GOES-East total optical energy, in femto Joules (fJ).

  85. GOES-West GLM FED CONUS
    [GOESWestGLMFEDRadC]

    GOES-West flash-extent density, a 5-min accumulation of flashes at each point.

  86. GOES 15 ConUS IR
    [GOES-W-CONUS-IR]

    GOES 15 ConUS IR

  87. GOES 15 ConUS LWIR
    [GOES-W-CONUS-LWIR]

    GOES 15 ConUS LWIR

  88. GOES 15 ConUS NIR
    [GOES-W-CONUS-NIR]

    GOES 15 ConUS NIR

  89. GOES 15 ConUS VIS
    [GOES-W-CONUS-VIS]

    GOES 15 ConUS VIS

  90. GOES 15 ConUS WV
    [GOES-W-CONUS-WV]

    GOES 15 ConUS WV

  91. GOES 15 Full Disk IR
    [GOES-W-FD-IR]

    GOES 15 Full Disk IR (Infrared)

  92. GOES 15 Full Disk LWIR
    [GOES-W-FD-LWIR]

    GOES 15 Full Disk LWIR (Long Wave Infrared)

  93. GOES 15 Full Disk NIR
    [GOES-W-FD-NIR]

    GOES 15 Full Disk NIR (Near Infrared)

  94. GOES 15 Full Disk VIS
    [GOES-W-FD-VIS]

    GOES 15 Full Disk VIS (Visible)

  95. GOES 15 Full Disk WV
    [GOES-W-FD-WV]

    GOES 15 Full Disk WV (Water Vapor)

  96. GOES17 ABI CONUS B07 IR Fire contours
    [GOES17-ABI-CONUS-B07-ARC-FIRES]


  97. GOES17 ABI CONUS B07 IR Fire enhanced
    [GOES17-ABI-CONUS-B07-ARC-ENH]

    View of GOES17-ABI-CONUS-B07-ARCHIVE

  98. GOES CAPE
    [cimssdpicapeli]

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

  99. GOES East ABI ConUS B02 Hi-Res "Red" Visible
    [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.

  100. GOES East ABI ConUS B03 "Veggie"
    [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.

  101. GOES East ABI ConUS B07 "Fire"
    [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.

  102. GOES East ABI ConUS B07 "Fire" enhanced
    [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.

  103. GOES East ABI ConUS B07 "Fire" stretch
    [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.

  104. GOES East ABI ConUS B09 Mid-level Water Vapor
    [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.

  105. GOES East ABI ConUS B09 Mid-level Water Vapor enhanced
    [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.

  106. GOES East ABI ConUS B13 "Clean" Infrared
    [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.

  107. GOES East ABI ConUS B13 "Clean" Infrared enhanced
    [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.

  108. GOES East ABI ConUS L2 "Sandwich"
    [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.

  109. GOES East ABI ConUS RGB True Color
    [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.

  110. GOES East ABI FD FLS Cloud Thickness
    [G16-ABI-FD-FLS-Thickness]

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

  111. GOES East ABI FD FLS IFR Fog Probability
    [G16-ABI-FD-FLS-IFR]

    IFR probability: Probability that IFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.

  112. GOES East ABI FD FLS LIFR Fog Probability
    [G16-ABI-FD-FLS-LIFR]

    LIFR probability: Probability that LIFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.

  113. GOES East ABI FD FLS MVFR Fog Probability
    [G16-ABI-FD-FLS-MVFR]

    MVFR probability: Probability that MVFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.

  114. GOES East ABI Full Disk B02 Hi-Res "Red" Visible
    [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.

  115. GOES East ABI Full Disk B03 "Veggie"
    [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.

  116. GOES East ABI Full Disk B07 "Fire"
    [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.

  117. GOES East ABI Full Disk B07 "Fire" enhanced
    [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.

  118. GOES East ABI Full Disk B09 Mid-level Water Vapor
    [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.

  119. GOES East ABI Full Disk B09 Mid-level Water Vapor enhanced
    [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.

  120. GOES East ABI Full Disk B13 "Clean" Infrared
    [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.

  121. GOES East ABI Full Disk B13 "Clean" Infrared enhanced
    [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.

  122. GOES East ABI Full Disk RGB True Color
    [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.

  123. GOES EAST ABI L2 South America Sandwich
    [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.

  124. GOES East ABI Meso1 B02 Hi-Res "Red" Visible
    [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.

  125. GOES East ABI Meso1 B03 "Veggie"
    [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.

  126. GOES East ABI Meso1 B07 "Fire"
    [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.

  127. GOES East ABI Meso1 B07 "Fire" enhanced
    [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.

  128. GOES East ABI Meso1 B09 Mid-level Water Vapor
    [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.

  129. GOES East ABI Meso1 B09 Mid-level Water Vapor enhanced
    [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.

  130. GOES East ABI Meso1 B13 "Clean" Infrared
    [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.

  131. GOES East ABI Meso1 B13 "Clean" Infrared enhanced
    [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.

  132. GOES East ABI Meso1 B13 "Clean" Infrared red
    [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.

  133. GOES East ABI Meso1 L2 "Sandwich"
    [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.

  134. GOES East ABI Meso1 RGB True Color
    [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.

  135. GOES East ABI Meso2 B02 Hi-Res "Red" Visible
    [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.

  136. GOES East ABI Meso2 B03 "Veggie"
    [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.

  137. GOES East ABI Meso2 B07 "Fire"
    [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.

  138. GOES East ABI Meso2 B07 "Fire" enhanced
    [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.

  139. GOES East ABI Meso2 B09 Mid-level Water Vapor
    [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.

  140. GOES East ABI Meso2 B09 Mid-level Water Vapor enhanced
    [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.

  141. GOES East ABI Meso2 B13 "Clean" Infrared
    [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.

  142. GOES East ABI Meso2 B13 "Clean" Infrared blue
    [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.

  143. GOES East ABI Meso2 B13 "Clean" Infrared enhanced
    [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.

  144. GOES East ABI Meso2 L2 "Sandwich"
    [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.

  145. GOES East ABI Meso2 RGB True Color
    [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.

  146. GOES East GLM Full Disk Group Density
    [glmgroupdensity]

    The Geostationary Lightning Mapper, or GLM, on board Geostationary Operational Environmental Satellite– R Series spacecraft, is the first operational lightning mapper flown on a geostationary satellite. 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.

  147. GOES East GLM Full Disk Group Points
    [glmgrouppoints]

    The Geostationary Lightning Mapper, or GLM, on board Geostationary Operational Environmental Satellite– R Series spacecraft, is the first operational lightning mapper flown on a geostationary satellite. 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.

  148. GOES IR Aviation
    [conusiravn]

    GOES IR Aviation

  149. GOES IR Dvorak
    [conusirbd]

    GOES IR Dvorak

  150. GOES IR Funk Top
    [conusirfunk]

    GOES IR Funk Top

  151. GOES IR Overshooting Tops
    [conusirott]

    GOES IR Overshooting Tops

  152. GOES IR Rainbow
    [conusirnhc]

    GOES IR Rainbow

  153. GOES Lifted Index
    [cimssdpilili]

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

  154. GOES Ozone
    [cimssdpiozli]

    GOES-DPI Ozone (Li etal 2008)

  155. GOES Precipitable Water
    [cimssdpipwli]

    CIMSS-DPI Precipitable Water (mm)

  156. GOES West ABI ConUS B02 Hi-Res "Red" Visible
    [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.

  157. GOES West ABI ConUS B03 "Veggie"
    [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.

  158. GOES West ABI ConUS B07 "Fire"
    [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.

  159. GOES West ABI ConUS B07 "Fire" enhanced
    [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.

  160. GOES West ABI ConUS B09 Mid-level Water Vapor
    [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.

  161. GOES West ABI ConUS B09 Mid-level Water Vapor enhanced
    [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.

  162. GOES West ABI ConUS B13 "Clean" Infrared
    [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.

  163. GOES West ABI ConUS B13 "Clean" Infrared enhanced
    [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.

  164. GOES West ABI ConUS RGB True Color
    [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.

  165. GOES West ABI FD FLS Cloud Thickness
    [G17-ABI-FD-FLS-Thickness]

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

  166. GOES West ABI FD FLS IFR Fog Probability
    [G17-ABI-FD-FLS-IFR]

    IFR probability: Probability that IFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.

  167. GOES West ABI FD FLS LIFR Fog Probability
    [G17-ABI-FD-FLS-LIFR]

    LIFR probability: Probability that LIFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.

  168. GOES West ABI FD FLS MVFR Fog Probability
    [G17-ABI-FD-FLS-MVFR]

    MVFR probability: Probability that MVFR (or lower) flight rule category is present for a given GOES satellite pixel determined by fusing satellite and NWP model data using a naive Bayesian probabilistic model and classifier.

  169. GOES West ABI Full Disk B02 Hi-Res "Red" Visible
    [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.

  170. GOES West ABI Full Disk B03 "Veggie"
    [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.

  171. GOES West ABI Full Disk B07 "Fire"
    [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.

  172. GOES West ABI Full Disk B07 "Fire" enhanced
    [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.

  173. GOES West ABI Full Disk B09 Mid-level Water Vapor
    [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.

  174. GOES West ABI Full Disk B09 Mid-level Water Vapor enhanced
    [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.

  175. GOES West ABI Full Disk B13 "Clean" Infrared
    [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.

  176. GOES West ABI Full Disk B13 "Clean" Infrared enhanced
    [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.

  177. GOES West ABI Full Disk RGB True Color
    [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.

  178. GOES West ABI Meso1 B02 Hi-Res "Red" Visible
    [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.

  179. GOES West ABI Meso1 B03 "Veggie"
    [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.

  180. GOES West ABI Meso1 B07 "Fire"
    [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.

  181. GOES West ABI Meso1 B07 "Fire" enhanced
    [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.

  182. GOES West ABI Meso1 B09 Mid-level Water Vapor
    [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.

  183. GOES West ABI Meso1 B09 Mid-level Water Vapor enhanced
    [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.

  184. GOES West ABI Meso1 B13 "Clean" Infrared
    [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.

  185. GOES West ABI Meso1 B13 "Clean" infrared enhanced
    [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.

  186. GOES West ABI Meso1 B13 "Clean" Infrared green
    [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.

  187. GOES West ABI Meso1 RGB True Color
    [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.

  188. GOES West ABI Meso2 B02 Hi-Res "Red" Visible
    [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.

  189. GOES West ABI Meso2 B03 "Veggie"
    [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.

  190. GOES West ABI Meso2 B07 "Fire"
    [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.

  191. GOES West ABI Meso2 B07 "Fire" enhanced
    [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.

  192. GOES West ABI Meso2 B09 Mid-level Water Vapor
    [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.

  193. GOES West ABI Meso2 B09 Mid-level Water Vapor enhanced
    [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.

  194. GOES West ABI Meso2 B13 "Clean" Infrared
    [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.

  195. GOES West ABI Meso2 B13 "Clean" Infrared enhanced
    [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.

  196. GOES West ABI Meso2 B13 "Clean" Infrared yellow
    [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.

  197. GOES West ABI Meso2 RGB True Color
    [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.

  198. Great Lakes Surface Environmental Analysis
    [GLERL-GLSEAimage]

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

  199. Hail Outlook Day1
    [SPChaday1]

    Hail Outlook Day1 (%)

  200. Himawari AHI Full Disk B03 Hi-Res "Red" Visible
    [HIMAWARI-B03]

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

  201. Himawari AHI Full Disk B04 "Veggie"
    [HIMAWARI-B04]

    Himawari AHI Full Disk B04 "Veggie"

  202. Himawari AHI Full Disk B07 "Fire"
    [HIMAWARI-B07]

    Himawari AHI Full Disk B07 "Fire"

  203. Himawari AHI Full Disk B07 "Fire" enhanced
    [HIMAWARI-B07-FIRE]

    View of HIMAWARI-B07

  204. Himawari AHI Full Disk B09 Mid-level Water Vapor
    [HIMAWARI-B09]

    Himawari AHI Full Disk B09 Mid-level Water Vapor

  205. Himawari AHI Full Disk B09 Mid-level Water Vapor enhanced
    [HIMAWARI-B09-VAPR]

    View of HIMAWARI-09

  206. Himawari AHI Full Disk B13 "Clean" Infrared
    [HIMAWARI-B13]

    Himawari AHI Full Disk B13 "Clean" Infrared

  207. Himawari AHI Full Disk B13 "Clean" Infrared enhanced
    [HIMAWARI-B13-GRAD]

    View of HIMAWARI-B13

  208. Himawari AHI Full Disk Day Convective Storm (ave)
    [H-DayConvectiveStorm-cve]

    Himawari AHI Full Disk Day Convective Storm (ave)

  209. Himawari AHI Full Disk Day Microphysics (dms)
    [H-DayMicrophysics-dms]

    Himawari AHI Full Disk Day Microphysics (dms)

  210. Himawari AHI Full Disk Dust (dst)
    [H-Dust-dst]

    Himawari AHI Full Disk Dust (dst)

  211. Himawari AHI Full Disk Natural Color (dnc)
    [H-NaturalColor-dnc]

    Himawari AHI Full Disk Natural Color (dnc)

  212. Himawari AHI Full Disk Night Microphysics (nms)
    [H-NightMicrophysics-ngt]

    Himawari AHI Full Disk Night Microphysics (nms)

  213. Himawari AHI Full Disk RGB Air Mass (arm)
    [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.

  214. Himawari AHI Full Disk Snow and Fog (dsl)
    [H-SnowFog-dsl]

    Himawari AHI Full Disk Snow and Fog (dsl)

  215. Himawari AHI Full Disk True Color (wgt)
    [H-TrueColor-wgt]

    Himawari AHI Full Disk True Color (wgt)

  216. Himawari AHI Japan B03 Hi-Res "Red" Visible
    [HIMAWARI-JP-B03]

    Himawari AHI Japan B03 Hi-Res "Red" Visible

  217. Himawari AHI Japan B07 "Fire"
    [HIMAWARI-JP-B07]

    Himawari AHI Japan Bo7 "Fire"

  218. Himawari AHI Japan B07 "Fire" enhanced
    [HIMAWARI-JP-B07-FIRE]

    View of HIMAWARI-JP-B07

  219. Himawari AHI Japan B09 Mid-level Water Vapor
    [HIMAWARI-JP-B09]

    Himawari AHI Japan B09 Mid-level Water Vapor

  220. Himawari AHI Japan B09 Mid-level Water Vapor enhanced
    [HIMAWARI-JP-B09-VAPR]

    View of HIMAWARI-JP-B09

  221. Himawari AHI Japan B14 Infrared
    [HIMAWARI-JP-B14]

    Himawari AHI Japan B14 Infrared

  222. Himawari AHI Japan B14 Infrared enhanced
    [HIMAWARI-JP-B14-GRAD]

    View of HIMAWARI-JP-B14

  223. Himawari AHI Target B03 Hi-Res "Red" Visible
    [HIMAWARI-T1-B03]

    Himawrai AHI Target B03 Hi-Res "Red" Visible

  224. Himawari AHI Target B07 "Fire"
    [HIMAWARI-T1-B07]

    Himawari AHI Target B07 "Fire"

  225. Himawari AHI Target B07 enhanced
    [HIMAWARI-T1-B07-FIRE]

    View of HIMAWARI-T1-B07

  226. Himawari AHI Target B14 Infrared
    [HIMAWARI-T1-B14]

    Himawari AHI Target B14 Infrared

  227. Himawari AHI Target B14 Infrared enhanced
    [HIMAWARI-T1-B14-GRAD]

    Himawari AHI Target B14 Infrared enhanced

  228. Himawari AHI Target Mid-level Water Vapor
    [HIMAWARI-T1-B09]

    Himawari AHI Target Mid-level Water Vapor

  229. Himawari AHI Target Mid-level Water Vapor enhanced
    [HIMAWARI-T1-B09-VAPR]

    Himawari AHI Target Mid-level Water Vapor enhanced

  230. Historic Fire Scars (MTBS)
    [historic-fire-scars-conus]

    These data come from the interagency MTBS (Monitoring Trends in Burn Severity) program through their direct download service.

  231. HRRR CONUS/AK Near Surface Smoke
    [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.

  232. HRRR CONUS/AK Vertically Integrated Smoke
    [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.

  233. HRRR ConUS Latest Freezing MASK
    [HRR-CONUS-FZRN-SFC]

    HRRR ConUS Latest Freezing MASK

  234. HRRR ConUS Latest Ice Mask
    [HRR-CONUS-ICEP-SFC]

    HRRR ConUS Latest Ice Mask

  235. HRRR ConUS Latest Precipitation Rate
    [HRR-CONUS-PCP-LATEST]

    View of HRR-CONUS-PCP-SFC

  236. HRRR ConUS Latest Rain Mask
    [HRR-CONUS-RAIN-SFC]

    HRRR ConUS Latest Rain Mask

  237. HRRR ConUS Latest Rate Mask
    [HRR-CONUS-PCP-SFC]

    HRR-CONUS-PCP-SFC

  238. HRRR ConUS Latest Simulated Radar
    [HRR-CONUS-RADAR-LATEST]

    View of HRR-CONUS-PCP-SFC

  239. HRRR ConUS Latest Snow Depth
    [HRR-CONUS-SNOD-SFC]


  240. HRRR ConUS Latest Snow Mask
    [HRR-CONUS-SNOW-SFC]

    HRRR ConUS Latest Snow Mask

  241. Hydro Estimator Rainfall
    [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.

  242. Icing Advisory
    [AIRMET-ICE]

    AIRMET Icing Advisory

  243. IFR Advisory
    [AIRMET-IFR]

    AIRMET-IFR Advisory

  244. Infrared 6 inch Imagery of Madison
    [madisonir]

    Infrared 6 inch Imagery of Madison

  245. Insolation East
    [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.

  246. Insolation West
    [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.

  247. IntenseStormNet -- GOES-East CONUS
    [ICP]

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

  248. IntenseStormNet -- GOES-East MESO1
    [ICPRadM1]

    IntenseStormNet -- GOES East Mesoscale 1

  249. IntenseStormNet -- GOES-East MESO2
    [ICPRadM2]

    IntenseStormNet -- GOES East Mesoscale 2

  250. IR Winds 250-100mb
    [AMV-ULhigh]

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

  251. IR Winds 350-251mb
    [AMV-ULmid]

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

  252. IR Winds 500-351mb
    [AMV-ULlow]

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

  253. IR Winds 599-400mb
    [AMV-LLhigh]

    AMV: 400-599mb Low Level IR winds

  254. IR Winds 799-600mb
    [AMV-LLmid]

    AMV: Lower Level IR (600-799mb)

  255. IR Winds 950-800mb
    [AMV-LLlow]

    AMV: Lower Level IR (800-950mb)

  256. ksview-721-bands
    [ksview-721-bands]

    ksview-721-bands

  257. Lake Michigan Surface Currents
    [glofsnowcast]

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

  258. Lake Michigan Wind
    [glofsnowcastWind]

    Wind speed and direction over the surface of Lake Michigan from The Great Lakes Operational Forecast System (GLOFS), uint: m/s

  259. Landsat-1 MSS 6, 5, 4
    [landsat-1-madison-321]

    Multispectral Scanner (MSS) 80-meter ground resolution in four spectral bands: Band 4 Visible green (0.5 to 0.6 µm) Band 5 Visible red (0.6 to 0.7 µm) Band 6 Near-Infrared (0.7 to 0.8 µm) Band 7 Near-Infrared (0.8 to 1.1 µm) Six detectors for each spectral band provided six scan lines on each active scan Ground Sampling Interval (pixel size): 57 x 79 m Scene size: 170 km x 185 km (106 mi x 115 mi)

  260. Landsat-8 Orbit
    [POESNAV-LSAT8]


  261. Landsat-9 Orbit
    [POESNAV-LSAT9]

    Orbit track for Landsat 9

  262. landsat-example
    [landsat-example]


  263. Landsat 8 Look Natural Color (Swaths)
    [lsat8-llook-fc]

    View of lsat8-llook-fc-scenes

  264. Landsat 8 Look Thermal IR (Swaths)
    [lsat8-llook-tir]

    View of lsat8-llook-tir-scenes

  265. Landsat 9 Look Natural Color (Swaths)
    [lsat9-llook-fc]

    View of lsat9-llook-fc-scenes

  266. Landsat 9 Look Thermal IR (Swaths)
    [lsat9-llook-tir]

    View of lsat9-llook-tir-scenes

  267. Landsat Footprints (WRS-2)
    [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.

  268. LandsatNY
    [LandsatNY]

    LandsatNY

  269. LaRC Cloud Phase GOESE 8km
    [LARC-CloudPhase-GOESE-8km]

    LaRC Cloud Phase GOESE 8km

  270. LaRC Cloud Phase GOESW 8km
    [LARC-CloudPhase-GOESW-8km]

    LaRC Cloud Phase GOESW 8km

  271. LaRC Cloud Phase HM 8km
    [LARC-CloudPhase-HM-8km]

    LaRC Cloud Phase HM 8km

  272. LaRC Cloud Phase MET8 9km
    [LARC-CloudPhase-MET8-9km]

    LaRC Cloud Phase MET8 9km

  273. LaRC Cloud Phase MSG 9km
    [LARC-CloudPhase-MSG-9km]

    LaRC Cloud Phase MSG 9km

  274. LaRC Cloud Top Height GOESE 8km
    [LARC-CloudZtop-GOESE-8km]

    LaRC Cloud Top Height GOESE 8km

  275. LaRC Cloud Top Height GOESW 8km
    [LARC-CloudZtop-GOESW-8km]

    LaRC Cloud Top Height GOESW 8km

  276. LaRC Cloud Top Height HM 8km
    [LARC-CloudZtop-HM-8km]

    LaRC Cloud Top Height HM 8km

  277. LaRC Cloud Top Height MET8 9km
    [LARC-CloudZtop-MET8-9km]

    LaRC Cloud Top Height MET8 9km

  278. LaRC Cloud Top Height MSG 9km
    [LARC-CloudZtop-MSG-9km]

    LaRC Cloud Top Height MSG 9km

  279. LightningCast GOES-East CONUS
    [PLTGGOESEastRadC]

    An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.

  280. LightningCast GOES-East FD (OCONUS)
    [PLTGGOESEastRadF]

    An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.

  281. LightningCast GOES-East MESO1
    [PLTGGOESEastRadM1]

    An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.

  282. LightningCast GOES-East MESO2
    [PLTGGOESEastRadM2]

    An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.

  283. LightningCast GOES-East RadC Alabama
    [PLTGGOESEastRadCAL]

    An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.

  284. LightningCast GOES-East RadM2 Gridded
    [PLTGGOESEastRadM2Gridded]

    An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.

  285. LightningCast GOES-West CONUS
    [PLTGGOESWestRadC]

    An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.

  286. LightningCast GOES-West MESO1
    [PLTGGOESWestRadM1]

    An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.

  287. LightningCast GOES-West MESO2
    [PLTGGOESWestRadM2]

    An AI model that predicts the probability of lightning in the next 60 minutes using GOES-R ABI data.

  288. LightningCast Himawari Guam
    [PLTGAHIJAPANFLDKGUAM]

    An AI model that predicts the probability of lightning in the next 60 minutes using Himawari AHI data.

  289. Low/High Pressure
    [HighLow]

    NCEP Frontal Analysis: Highs and Lows

  290. MADIS Surface DewPoint
    [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/.

  291. Maximum Arctic Sea Ice Extent
    [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.

  292. Mean Snow Duration 1988-2017
    [mean-snow-cover-1988-2017]

    mean-snow-cover-1988-2017

  293. METAR
    [SSEC-METAR]

    Global METAR

  294. Meteosat 8 SEVIRI Full Disk B01 Vis (0.6um)
    [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.

  295. Meteosat 8 SEVIRI Full Disk B04 IR Fire (3.9um)
    [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.

  296. Meteosat 8 SEVIRI Full Disk B05 WV High (6.2um)
    [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).

  297. Meteosat 8 SEVIRI Full Disk B09 IR Clean (10.8um)
    [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).

  298. Meteosat 8 SEVIRI Full Disk B09 IR Clean (10.8um) enhanced
    [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).

  299. Meteosat 8 SEVIRI Full Disk B12 Vis HRV (0.7um)
    [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.

  300. Meteosat 11 SEVIRI Full Disk B01 Vis (0.6um)
    [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.

  301. Meteosat 11 SEVIRI Full Disk B04 IR Fire (3.9um)
    [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.

  302. Meteosat 11 SEVIRI Full Disk B05 WV High (6.2um)
    [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).

  303. Meteosat 11 SEVIRI Full Disk B09 IR Clean (10.8um)
    [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).

  304. Meteosat 11 SEVIRI Full Disk B09 IR Clean (10.8um) enhanced
    [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).

  305. Meteosat 11 SEVIRI Full Disk B12 Vis HRV (0.7um)
    [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.

  306. Mexico - Aguascalientes
    [Aguascalientes]

    Aguascalientes

  307. Mexico - BajaCalif
    [BajaCalif]

    BajaCalif

  308. Mexico - BCS
    [BCS]

    BCS

  309. Mexico - Campeche
    [Campeche]

    Campeche

  310. Mexico - CDMX
    [CDMX]

    CDMX

  311. Mexico - Chiapas
    [Chiapas]

    Chiapas

  312. Mexico - Chihuahua
    [Chihuahua]

    Chihuahua

  313. Mexico - Coahuila
    [Coahuila]

    Coahuila

  314. Mexico - Colima
    [Colima]

    Colima

  315. Mexico - Durango
    [Durango]

    Durango

  316. Mexico - EdoMex
    [EdoMex]

    EdoMex

  317. Mexico - Guanajuato
    [Guanajuato]

    Guanajuato

  318. Mexico - Guerrero
    [Guerrero]

    Guerrero

  319. Mexico - Hidalgo
    [Hidalgo]

    Hidalgo

  320. Mexico - Jalisco
    [Jalisco]

    Jalisco

  321. Mexico - Michoacan
    [Michoacan]

    Michoacan

  322. Mexico - Morelos
    [Morelos]

    Morelos

  323. Mexico - Nayarit
    [Nayarit]

    Nayarit

  324. Mexico - NuevoL
    [NuevoL]

    NuevoL

  325. Mexico - Oaxaca
    [Oaxaca]

    Oaxaca

  326. Mexico - Puebla
    [Puebla]

    Puebla

  327. Mexico - Querétaro
    [Queretaro]

    Queretaro

  328. Mexico - QuintanaRoo
    [QuintanaRoo]

    QuintanaRoo

  329. Mexico - SanLuisPotosi
    [SanLuisPotosi]

    SanLuisPotosi

  330. Mexico - Sinaloa
    [Sinaloa]

    Sinaloa

  331. Mexico - Sonora
    [Sonora]

    Sonora

  332. Mexico - Tabasco
    [Tabasco]

    Tabasco

  333. Mexico - Tamaulipas
    [Tamaulipas]

    Tamaulipas

  334. Mexico - Tlaxcala
    [Tlaxcala]

    Tlaxcala

  335. Mexico - Veracruz
    [Veracruz]

    Veracruz

  336. Mexico - Yucatan
    [Yucatan]

    Yucatan

  337. Mexico - Zacatecas
    [Zacatecas]

    Zacatecas

  338. MI-721
    [MI-721]

    MI-721

  339. MICHIGAN
    [MICHIGAN]

    MICHIGAN

  340. Midwest Winter Road Conditions
    [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.

  341. MIMIC Total Precip Water v2
    [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.

  342. MIRS 90Ghz Brightness Temperature
    [MIRS-BT90]

    MIRS 90Ghz Brightness Temperature

  343. MIRS Rain Rate
    [MIRS-RainRate]

    MIRS Rain Rate

  344. Montana
    [Montana]

    Montana

  345. Mountains Obscured Advisory
    [AIRMET-MTN]

    AIRMET-Mountain Obscured Advisory

  346. MRMS MergedReflectivity
    [MERGEDREF]

    Multi-Radar/Multi-Sensor MergedReflectivityQCComposite

  347. NAIP WI
    [NAIPWI]

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

  348. NAIP WI Color Infrared
    [NAIPWICIR]

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

  349. NAM-CONUS-PRAT-SFC
    [NAM-CONUS-PRAT-SFC]

    NAM-CONUS-PRAT-SFC

  350. National Reflectivity MRMS Composite mask
    [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.

  351. NEXRAD Alaska Base Reflectivity
    [NEXRAD-Alaska]

    WSR 88D NEXRAD Radar Mosiac Base Reflectivity Tilt 1 for Alaska Region. Bethel (KABC) Sitka (KACG) Nome (KAEC) Anchorage (KANG) Middleton Island (KAIH) King Salmon (KAKC) Fairbanks (KAPD)

  352. NEXRAD CanAm Precipitation Phase
    [nexrphase]

    NEXRAD CanAm Precipitation Phase

  353. NEXRAD ConUS Hybrid Reflectivity mask
    [nexrhres]

    Values are actual reflectivity values instead of categories, data extends to further range, and additional elevations are available. Products from elevation angles at or below 3.5 degrees are available, and select sites may also scan at an additional low elevation angle, as low as -0.2 degrees. Specific elevation angles depend on the site and scanning mode of the Radar.

  354. NEXRAD ConUS Storm Total Precipitation
    [nexrstorm]

    Storm Total Precipitation (NTP/80) This product uses the PPS algorithm to create a continuously updated estimate of a storm’s accumulated precipitation. Accumulation is tracked on a 1.1 nm x 1 degree grid. Scientists use this product to locate flood potential over urban or rural areas, estimate total basin runoff, and provide rainfall data 24 hours a day.

  355. NEXRAD Guam Base Reflectivity
    [NEXRAD-Guam]

    NEXRAD Guam Base Reflectivity

  356. NEXRAD Hawaii Base Reflectivity
    [NEXRAD-Hawaii]

    NEXRAD Hawaii Base Reflectivity

  357. NEXRAD Puerto Rico Base Reflectivity
    [NEXRAD-PuertoRico]

    WSR 88D NEXRAD Radar Base Reflectivity Tilt 1 for San Juan, Puerto Rico

  358. NOAA-15 Orbit
    [POESNAV-N15]


  359. NOAA-18 Orbit
    [POESNAV-N18]


  360. NOAA-20 Orbit
    [POESNAV-N20]


  361. NOAA-20 VIIRS Daily DNB (Adaptive)
    [j01-viirs-adaptive-dnb-daily]

    j01-viirs-adaptive-dnb-daily

  362. NOAA-20 VIIRS Daily Fires - NASA (Image)
    [FIRMS-Fire-Image-j01-daily]

    NASA"s Fire Information for Resource Management System (FIRMS) distributes Near Real-Time (NRT) fire/thermal anomaly data within 3 hours of satellite observation from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the joint NASA/NOAA Suomi National Polar orbiting Partnership (Suomi NPP) and NOAA-20 satellites. FIRMS is part of NASA"s Land, Atmosphere Near real-time Capability for EOS (LANCE).

  363. NOAA-20 VIIRS Daily Fires - NASA (Points)
    [FIRMS-Fire-Points-j01-daily]

    NASA"s Fire Information for Resource Management System (FIRMS) distributes Near Real-Time (NRT) fire/thermal anomaly data within 3 hours of satellite observation from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the joint NASA/NOAA Suomi National Polar orbiting Partnership (Suomi NPP) and NOAA-20 satellites. FIRMS is part of NASA"s Land, Atmosphere Near real-time Capability for EOS (LANCE).

  364. NOAA-20 VIIRS Daily Fires - NOAA (Image)
    [NOAA-Fire-Image-j01-daily]

    The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.

  365. NOAA-20 VIIRS Daily Fires - NOAA (Points)
    [NOAA-Fire-Points-j01-daily]

    The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.

  366. NOAA-20 VIIRS Daily I02
    [j01-viirs-i02-daily]

    j01-viirs-i02-daily

  367. NOAA-20 VIIRS Daily I05
    [j01-viirs-i05-daily]

    j01-viirs-i05-daily

  368. NOAA-20 VIIRS Daily I05 (tops)
    [j01-viirs-i05-daily-tops]

    View of j01-viirs-i05-daily

  369. NOAA-20 VIIRS DNB (Swaths)
    [j01-viirs-dnb-swath]

    View of j01-viirs-bands-night-swath

  370. NOAA-20 VIIRS False Color (Daily Composite)
    [j01-viirs-false-color-daily]

    View of j01-viirs-false-color-swath

  371. NOAA-20 VIIRS False Color (Hourly Composite)
    [j01-viirs-false-color-hourly]

    View of j01-viirs-false-color

  372. NOAA-20 VIIRS False Color (Swaths)
    [j01-viirs-false-color-swath]

    View of j01-viirs-false-color

  373. NOAA-20 VIIRS Hourly DNB (Adaptive)
    [j01-viirs-adaptive-dnb]

    j01-viirs-adaptive-dnb

  374. NOAA-20 VIIRS Hourly I02
    [j01-viirs-i02]

    j01-viirs-i02

  375. NOAA-20 VIIRS Hourly I05
    [j01-viirs-i05]

    j01-viirs-i05

  376. NOAA-20 VIIRS Hourly I05 (tops)
    [j01-viirs-i05-tops]

    View of j01-viirs-i05

  377. NOAA-20 VIIRS M-Band Fire RGB (Swaths)
    [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.

  378. NOAA-20 VIIRS M-Band Fire Temp (Swaths)
    [j01-viirs-swath-fire-temp]

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

  379. NOAA-20 VIIRS Swath Fires - NOAA (Image)
    [NOAA-Fire-Image-j01]

    The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.

  380. NOAA-20 VIIRS Swath Fires - NOAA (Points)
    [NOAA-Fire-Points-j01]

    The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.

  381. NOAA-20 VIIRS True Color (Daily Composite)
    [j01-viirs-true-color-daily]

    View of j01-viirs-true-color-swath

  382. NOAA-20 VIIRS True Color (Hourly Composite)
    [j01-viirs-true-color-hourly]

    View of j01-viirs-true-color

  383. NOAA-20 VIIRS True Color (Swaths)
    [j01-viirs-true-color-swath]

    View of j01-viirs-true-color

  384. NUCAPS-MADIS-SBCAPE
    [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.

  385. NUCAPS-MADIS Mean Layer CAPE
    [NUCAPS-MADIS-MLCAPE]

    NUCAPS-MADIS-MLCAPE

  386. NUCAPS-MADIS Mean Layer CIN
    [NUCAPS-MADIS-MLCIN]

    NUCAPS-MADIS-MLCIN

  387. NUCAPS-MADIS Mean Layer LI
    [NUCAPS-MADIS-MLLI]

    NUCAPS-MADIS-MLLI

  388. NUCAPS-MADIS Surface CAPE
    [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.

  389. NUCAPS-MADIS Surface CIN
    [NUCAPS-MADIS-SBCIN]

    NUCAPS-MADIS-SBCIN

  390. NUCAPS-MADIS Surface LI
    [NUCAPS-MADIS-SBLI]

    NUCAPS-MADIS-SBLI

  391. NUCAPS CAA Temp 180mb
    [NUCAPS-CAA-temp-180mb]


  392. NUCAPS CAA Temp 200mb
    [NUCAPS-CAA-temp-200mb]


  393. NUCAPS CAA Temp 235mb
    [NUCAPS-CAA-temp-235mb]


  394. NUCAPS CAA Temp 260mb
    [NUCAPS-CAA-temp-260mb]


  395. NUCAPS CAA Temp 286mb
    [NUCAPS-CAA-temp-286mb]


  396. NWS Alerts (All)
    [NWS-Alerts-All]

    All NWS Alerts

  397. NWS County Warning Areas
    [NWSCWA]

    NWS County Warning Areas

  398. NWSWARNS12Z12Z
    [NWSWARNS12Z12Z]

    NWSWARNS12Z12Z (Severe and Tornado. No SVSs)

  399. NWS Watches and Warnings
    [NWS-Alerts-Warnings]

    Watches and warnings from NWS

  400. OhioView-MODIS-FalseColor
    [OhioView-MODIS-FalseColor]

    OhioView-MODIS-FalseColor

  401. OhioView-MODIS-TrueColor
    [OhioView-MODIS-TrueColor]

    OhioView-MODIS-TrueColor

  402. OhioView-TrueColor
    [OhioView-TrueColor]

    OhioView-TrueColor

  403. OPC & TAFB Offshore Zones
    [OFFSHOREZONES]


  404. Overshooting Tops targets
    [CIMSS-OTtargets]

    Cloud OverShooting Tops targets

  405. Pilot Reports
    [PIREP]

    PIREP

  406. PNPOINTALL
    [PNPOINTALL]

    PNPOINTALL

  407. PNTRACKALL
    [PNTRACKALL]

    PNTRACKALL

  408. PROBSEVACCUM
    [PROBSEVACCUM]

    ≥ 50%

  409. ProbSevere
    [ProbSevere]

    ProbSevere

  410. ProbSevere (version2)
    [PROBSEVERE]

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

  411. ProbSevere (version 3)
    [PROBSEVEREV3]

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

  412. ProbSevere Accumulation 20% to 49%
    [PROBSEVACCUMLOW]

    ProbSevere Accumulation 20% to 49%

  413. PROBSEVTESTACCUM
    [PROBSEVTESTACCUM]


  414. PROBSEVTESTACCUMLOW
    [PROBSEVTESTACCUMLOW]


  415. PROBTOR
    [PROBTOR]


  416. PROBTORACCUM
    [PROBTORACCUM]


  417. PSCNN
    [PSCNN]


  418. PSNCO
    [PSNCO]


  419. PSNSSL
    [PSNSSL]


  420. Quantitative Precipitation Forecast
    [WPC-QPF]

    WPC-QPF

  421. RAP ConUS Latest Simulated Radar
    [RAP-CONUS-PRAT-SFC-DBZ]

    View of RAP-CONUS-PRAT-SFC

  422. RAP North America Near Surface Smoke
    [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.

  423. RAP North America Vertically Integrated Smoke
    [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.

  424. River-ICE-CONCENTRATION: Alaska
    [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

  425. RIVER-ICE-CONCENTRATION: Missouri Basin
    [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)

  426. River-ICE-CONCENTRATION: North Central Basin
    [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)

  427. River-ICE-CONCENTRATION: North East Basin
    [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)

  428. River Flood: 1 day VIIRS composite
    [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

  429. River Flood: 5 day VIIRS composite
    [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

  430. River Flood: ABI-daily
    [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

  431. River Flood: ABI-daily (tsp)
    [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

  432. River Flood: ABI-hourly
    [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

  433. River Flood: ABI-hourly (tsp)
    [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

  434. River Flood: AHI
    [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

  435. River Flood: Alaska
    [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

  436. River Flood: Alaska (transparent)
    [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

  437. River Flood: Global
    [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

  438. River Flood: Joint ABI/VIIRS
    [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

  439. River Flood: Joint AHI/VIIRS
    [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

  440. River Flood: Missouri Basin
    [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

  441. River Flood: Missouri Basin (transparent)
    [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

  442. River Flood: North Central Basin
    [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

  443. River Flood: North Central Basin (transparent)
    [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

  444. River Flood: North East Basin
    [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

  445. River Flood: North East Basin (transparent)
    [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

  446. River Flood: North West
    [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

  447. River Flood: North West (transparent)
    [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

  448. River Flood: South East
    [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

  449. River Flood: South East (transparent)
    [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

  450. River Flood: South West
    [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

  451. River Flood: South West (tsp)
    [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

  452. River Flood: US
    [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

  453. River Flood: US (transparent)
    [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

  454. River Flood: West Gulf Basin
    [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

  455. River Flood: West Gulf Basin (transparent)
    [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

  456. River Ice: Alaska
    [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

  457. River Ice: Missouri Basin
    [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)

  458. River Ice: North Central Basin
    [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)

  459. River Ice: North East Basin
    [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)

  460. Sea Ice Concentration
    [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.

  461. Sea Surface Temperature
    [NESDIS-SST]

    NESDIS: Hi-Res Sea Surface Temperature

  462. SENTINEL 2A Orbit
    [POESNAV-SEN2A]

    POESNAV-SEN2A

  463. SENTINEL 2B Orbit
    [POESNAV-SEN2B]

    POESNAV-SEN2B

  464. Severe Weather Outlook Day2
    [SPCsvday2]

    Severe Weather Outlook Day2

  465. Severe Weather Outlook Day3
    [SPCsvday3]

    Severe Weather Outlook Day3

  466. Severe Weather Outlook Day4
    [SPCsvday4]

    Severe Weather Outlook Day4

  467. Severe Weather Outlook Day5
    [SPCsvday5]

    Severe Weather Outlook Day5

  468. Severe Weather Warning Outlines
    [SevereOutl]

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

  469. Severe Weather Warnings
    [Severe]

    Tornado, Thunderstorm, Flash Flood and Marine Warning polygons.

  470. Severe Weather Warning Vectors
    [SevereVect]

    Tornado and Thunderstorm Warning Vectors

  471. Severe Weather Watch Box
    [SAW]

    Severe Weather Watch Box - Aviation

  472. Severe Wind Outlook Day1
    [SPCwnday1]

    Severe Wind Outlook Day1 (%)

  473. Ship & Buoy
    [SSEC-ShipBuoy]

    Global Ship & Buoy

  474. Snow Depth (SNODAS)
    [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.

  475. Snowfall Probability >= 4" Final Forecast
    [WPC-psnowgt04]

    WPC-psnowgt04

  476. Snowfall Probability >= 8" Final Forecast
    [WPC-psnowgt08]

    WPC-psnowgt08

  477. Snowfall Probability >= 12" Final Forecast
    [WPC-psnowgt12]

    WPC-psnowgt12

  478. Snow Fall Rate
    [NESDIS-SnowFallRate]

    AMSU Snow Fall Rate Global by NOAA-NESDIS

  479. Snowfall Reports - 6hr
    [lsr-snow]

    NWS reported 6hr Snowfall Totals (inches).

  480. Snowfall Total - 24hr (SNODAS)
    [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.

  481. Snow Probability >= 0.1"/24h
    [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.

  482. Snow Probability >= 1.0"/24h
    [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.

  483. Snow Probability >= 2.0"/24h
    [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.

  484. Snow Probability >= 4.0"/24h
    [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.

  485. Snow Probability >= 6.0"/24h
    [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.

  486. Snow Probability >= 8.0"/24h
    [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.

  487. Snow Probability >= 12.0"/24h
    [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.

  488. Snow Probability >= 18.0"/24h
    [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.

  489. SNPP Day/Night AM Composite - Adaptive
    [nppadpam]

    NPP Day/Night AM Composite - Adaptive

  490. SNPP Day/Night Band (DNB) - Honolulu DB
    [nppdnbdyn-hnl]

    NPP Day/Night Band (DNB) - Honolulu DB

  491. SNPP Day/Night Band (DNB) - Madison DB
    [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.

  492. SNPP Day/Night Band (DNB) - Puerto Rico DB
    [nppdnbdyn-upr]

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

  493. SNPP Day/Night Band - Dynamic
    [nppdnb]

    NPP Day/Night Band - Dynamic

  494. SNPP False Color
    [nppfc]

    NPP False Color

  495. SNPP NUCAPS CO-MR-496mb
    [CO-MR-496mb]

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

  496. SNPP Orbit
    [POESNAV-NPP]


  497. SNPP Sea Surface Temperature
    [nppsst]

    NPP Sea Surface Temperature

  498. SNPP Sea Surface Temperature (SST) - Madison DB
    [nppsst-msn]

    NPP Sea Surface Temperature (SST) - Madison DB

  499. SNPP True Color (TC) - Honolulu DB
    [npptc-hnl]

    NPP True Color (TC) - Honolulu DB

  500. SNPP True Color (TC) - Puerto Rico DB
    [npptc-upr]

    NPP True Color (TC) - Puerto Rico DB

  501. SNPP VIIRS Daily DNB (Adaptive)
    [npp-viirs-adaptive-dnb-daily]

    npp-viirs-adaptive-dnb-daily

  502. SNPP VIIRS Daily Fires - NASA (Image)
    [FIRMS-Fire-Image-npp-daily]

    The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.

  503. SNPP VIIRS Daily Fires - NASA (Points)
    [FIRMS-Fire-Points-npp-daily]

    The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.

  504. SNPP VIIRS Daily Fires - NOAA (Image)
    [NOAA-Fire-Image-npp-daily]

    The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.

  505. SNPP VIIRS Daily Fires - NOAA (Points)
    [NOAA-Fire-Points-npp-daily]

    The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.

  506. SNPP VIIRS Daily I02
    [npp-viirs-i02-daily]

    npp-viirs-i02-daily

  507. SNPP VIIRS Daily I05
    [npp-viirs-i05-daily]

    npp-viirs-i05-daily

  508. SNPP VIIRS Daily I05 (tops)
    [npp-viirs-i05-daily-tops]

    View of npp-viirs-i05-daily

  509. SNPP VIIRS DNB (Swaths)
    [npp-viirs-dnb-swath]

    View of npp-viirs-bands-night-swath

  510. SNPP VIIRS DNB (Swaths)
    [npp-viirs-dnb-swath]

    View of npp-viirs-bands-night-swath

  511. SNPP VIIRS False Color (Daily Composite)
    [npp-viirs-false-color-daily]

    View of npp-viirs-false-color-swath

  512. SNPP VIIRS False Color (Daily Composite)
    [npp-viirs-false-color-daily]

    View of npp-viirs-false-color-swath

  513. SNPP VIIRS False Color (Hourly Composite)
    [npp-viirs-false-color-hourly]

    View of npp-viirs-false-color

  514. SNPP VIIRS False Color (Hourly Composite)
    [npp-viirs-false-color-hourly]

    View of npp-viirs-false-color

  515. SNPP VIIRS False Color (Swaths)
    [npp-viirs-false-color-swath]

    View of npp-viirs-false-color

  516. SNPP VIIRS False Color (Swaths)
    [npp-viirs-false-color-swath]

    View of npp-viirs-false-color

  517. SNPP VIIRS False Color - Madison DB
    [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.

  518. SNPP VIIRS Fire RGB (Swaths)
    [npp-viirs-swath-fire-color]

    View of npp-viirs-bands-day-swath

  519. SNPP VIIRS Fire RGB (Swaths)
    [npp-viirs-swath-fire-color]

    View of npp-viirs-bands-day-swath

  520. SNPP VIIRS Fire Temp (Swaths)
    [npp-viirs-swath-fire-temp]

    View of npp-viirs-bands-day-swath

  521. SNPP VIIRS Fire Temp (Swaths)
    [npp-viirs-swath-fire-temp]

    View of npp-viirs-bands-day-swath

  522. SNPP VIIRS Hourly DNB (Adaptive)
    [npp-viirs-adaptive-dnb]

    npp-viirs-adaptive-dnb

  523. SNPP VIIRS Hourly I02
    [npp-viirs-i02]

    npp-viirs-i02

  524. SNPP VIIRS Hourly I05
    [npp-viirs-i05]

    npp-viirs-i05

  525. SNPP VIIRS Hourly I05 (tops)
    [npp-viirs-i05-tops]

    View of npp-viirs-i05

  526. SNPP VIIRS Swath Fires - NOAA (Image)
    [NOAA-Fire-Image-npp]

    The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.

  527. SNPP VIIRS Swath Fires - NOAA (Points)
    [NOAA-Fire-Points-npp]

    The 375m I-Band VIIRS Active Fire (AF) product is a Level 2 data set produced by NOAA/NESDIS Center for Satellite Applications and Research (STAR). The AF algorithm is designed for the Joint Polar Satellite System (JPSS). It produces two main outputs from the Visible Infrared Imaging Radiometer Suite (VIIRS) multi-spectral data, namely: (i) an image classification product (fire mask) including thematic classes such as fire/no-fire, clouds, water and clear-land pixels; and (ii) sub-pixel characterization of the instantaneous power emitted by detected fires (fire radiative power [FRP] retrievals). Fire source is classified, in part, by a fixed known sources database.

  528. SNPP VIIRS True Color (Daily Composite)
    [npp-viirs-true-color-daily]

    View of npp-viirs-true-color-swath

  529. SNPP VIIRS True Color (Daily Composite)
    [npp-viirs-true-color-daily]

    View of npp-viirs-true-color-swath

  530. SNPP VIIRS True Color (Hourly Composite)
    [npp-viirs-true-color-hourly]

    View of npp-viirs-true-color

  531. SNPP VIIRS True Color (Hourly Composite)
    [npp-viirs-true-color-hourly]

    View of npp-viirs-true-color

  532. SNPP VIIRS True Color (Swaths)
    [npp-viirs-true-color-swath]

    View of npp-viirs-true-color

  533. SNPP VIIRS True Color (Swaths)
    [npp-viirs-true-color-swath]

    View of npp-viirs-true-color

  534. SNPP VIIRS True Color - Madison DB
    [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.

  535. SPC reports 12Z to 12Z
    [SPCREPS12Z12Z]

    SPCREPS12Z12Z

  536. Storm Cell ID and Tracking - Filter 1
    [SCIT-ALL]

    Storm Cell Identification and Tracking (SCIT) Filters 1| ALL Cells 2| Moderate Threat level Cells 3| Severe Threat level Cells

  537. Storm Cell ID and Tracking - Filter 2
    [SCIT-MOD]

    Storm Cell Identification and Tracking (SCIT) Filters 1| ALL Cells 2| Moderate Threat level Cells 3| Severe Threat level Cells

  538. Storm Cell ID and Tracking - Filter 3
    [SCIT-SEV]

    Storm Cell Identification and Tracking (SCIT) Filters 1| ALL Cells 2| Moderate Threat level Cells 3| Severe Threat level Cells

  539. Storm Reports 3hrs
    [StormReports]

    Storm Reports (last 3hrs)

  540. Storm Reports 24hrs
    [StormReports24]

    Storm Reports (last 24hrs)

  541. Stroke Density XP
    [XLSD]

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

  542. SVRWARNS12Z12Z
    [SVRWARNS12Z12Z]


  543. Terminal Area Forecasts
    [TAF]

    Terminal Aerodrome Forecast (TAF)

  544. Terra Aerosol Optical Depth
    [TERRA-AER]

    MODIS: TERRA Aerosol Optical Depth (ta)

  545. Terra False Color
    [terrafalsecolor]

    CIMSS-MODIS Satellite False Color (Terra)

  546. Terra Land Surface True Color
    [GLOBALterratc]

    MODIS: Terra land Surface True Color composite

  547. TERRA Orbit
    [POESNAV-TERRA]


  548. Terra True Color
    [terratruecolor]

    CIMSS-MODIS Satellite True Color (Terra)

  549. TEST
    [TEST]


  550. TESTGRBRADF
    [TESTGRBRADF]

    TESTGRBRADF

  551. Thunderstorm Watches/Warnings
    [WWSEVTRW]

    Thunderstorm Watches and Warnings

  552. Tornado Outlook Day1
    [SPCtnday1]

    Tornado Outlook Day1 (%)

  553. Tornado Watches/Warnings
    [WWTORNADO]

    Tornado Watches and Warnings

  554. Tornado Watches and Warnings
    [WWTOR]

    Tornado Watches and Warnings

  555. TORWARNS12Z12Z
    [TORWARNS12Z12Z]


  556. Total Column Sulphur Dioxide
    [AURA-SO2]

    AURA - OMI Total Column Sulphur Dioxide (SO2)

  557. Tropical Storm & Hurricane Forecast
    [TSFCST]

    National Hurricane Center Tropical Storm & Hurricane Forecast

  558. True Color Clear View
    [BRDF]

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

  559. TS HDOB - Atlantic points
    [TSHDOBATLparm]

    TS HDOB - Atlantic points

  560. TS HDOB - Atlantic winds
    [TSHDOBATL]

    TS HDOB - Atlantic winds

  561. TS HDOB - EPacific points
    [TSHDOBEPACparm]

    TS HDOB - EPacific points

  562. TS HDOB - EPacific winds
    [TSHDOBEPAC]

    TS HDOB - EPacific winds

  563. Turbulence Advisory
    [AIRMET-TURB]

    AIRMET-Turlulence Advisory

  564. US Landsat Analysis Ready Data (ARD) Grids
    [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.

  565. VIIRS Aerosol Optical Depth (AOD) - GINA
    [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.

  566. VIIRS Fire RGB - CIRA
    [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.

  567. VIIRS Fire RGB - GINA
    [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).

  568. VIIRS Fire Temp RGB - CIRA
    [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.

  569. VIIRS Fire Temp RGB - GINA
    [FireTemperature-RGB-GINA]

    This RGB is created by assigning the VIIRS 3.74um channel to red, 2.25um channel to green, and the 1.61um channel to blue. It is used to assess fire intensity and size, with fires ranging from red (lowest) to yellow to white (hottest or biggest). These data are produced by the Geographic Information Network of Alaska (GINA).

  570. VIIRS Fire Temp RGB 375m CIRA
    [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.

  571. VIIRS Floodwater Depth
    [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.

  572. VIIRS i04 - GINA
    [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).

  573. VIIRS NDVI 16-day Composite
    [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.

  574. VIIRS Snowmelt - GINA
    [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).

  575. Vis Winds 800-700mb
    [AMV-VISmid]

    AMV: Middle Level Visible (700-800mb)

  576. Vis Winds 925-801mb
    [AMV-VISlow]

    AMV: Lower Level Visible (801-925mb)

  577. Volcanic Ash Adv plumes
    [VAA]

    Volcanic Ash Advisories: Ash Clouds

  578. WI Coastal Imagery
    [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.

  579. WI Coastal LiDAR
    [WIcoastallidar]

    WI Coastal LiDAR

  580. WI Coastal Shaded Relief
    [WIcoastalshdrlf]

    WI coastal shaded relief map generated from LiDAR data.

  581. WI Lake Clarity
    [LakesTSI]

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

  582. Wildland Fire Perimeters - Current
    [WFIGS-Current]

    The Wildland Fire Interagency Geospatial Services (WFIGS) Group provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.

  583. Wind Hazards
    [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.

  584. WI Nordic Ski Trails
    [SKITrails]

    SKITrails

  585. Winter Weather Hazards (Issued)
    [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.

  586. Winter Weather Hazards (Valid)
    [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.

  587. WISCLAND 1993
    [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.

  588. Wisconsin Counties
    [wi-counties-basic]


  589. Wisconsin in 3D (SRTM)
    [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).

  590. Wisconsin LIDAR Hillshade
    [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.

  591. WI USGS Landsat Poster
    [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.

  592. WSSI Blowing Snow
    [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.

  593. WSSI Flash Freeze
    [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

  594. WSSI Ground Blizzard
    [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.

  595. WSSI Ice Accumulation
    [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.

  596. WSSI Overall Impact
    [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

  597. WSSI Snow Amount
    [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.

  598. WSSI Snow Load
    [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.

  599. Wyoming-143-true-color
    [wvview-143-true-color]

    wvview-143-true-color