This layer displays Wisconsin county outlines. Right-click-probe allows downloads of source imagery for the 2015 Wisconsin NAIP aerial photography county mosaics.
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.
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.
This satellite-derived precipitation product represents global 1-day accumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree lat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include various rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS).
This satellite-derived precipitation product represents global 1-hour accumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree lat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include various rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS).
This satellite-derived precipitation product represents global 7-day accumulation. The second generation CMORPH (CMORPH2) has started test real-time production of 30-minute precipitation estimates on a 0.05-degree lat/lon grid over the entire globe, from pole-to-pole. The CMORPH2 is built upon the Kalman Filter based CMORPH algorithm of Joyce and Xie (2011). Inputs to the system include various rainfall and snowfall rate retrievals from passive microwave (PMW) measurements aboard all available low earth orbit (LEO) satellites, precipitation estimates derived from infrared (IR) observations of geostationary (GEO) and LEO platforms, and model precipitation forecast from the NCEP operational global forecast system (GFS).
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.
WPC Excessive Rainfall Threat Area Day1:
In the Excessive Rainfall Outlooks, the Weather Prediction Center (WPC) forecasts the probability that rainfall will exceed flash flood guidance at a point. Gridded FFG is provided by the twelve NWS River Forecast Centers (RFCs) whose service areas cover the lower 48 states. WPC creates a national mosaic of FFG, whose 1, 3, and 6-hour values represent the amount of rainfall over those short durations which it is estimated would bring rivers and streams up to bankfull conditions. WPC estimates the likelihood that FFG will be exceeded by assessing environmental conditions (e.g. moisture content and steering winds), recognizing weather patterns commonly associated with heavy rainfall, and using a variety of deterministic and ensemble-based numerical model tools
WPC Excessive Rainfall Threat Area Day2: In the Excessive Rainfall Outlooks, the Weather Prediction Center (WPC) forecasts the probability that rainfall will exceed flash flood guidance at a point. Gridded FFG is provided by the twelve NWS River Forecast Centers (RFCs) whose service areas cover the lower 48 states. WPC creates a national mosaic of FFG, whose 1, 3, and 6-hour values represent the amount of rainfall over those short durations which it is estimated would bring rivers and streams up to bankfull conditions. WPC estimates the likelihood that FFG will be exceeded by assessing environmental conditions (e.g. moisture content and steering winds), recognizing weather patterns commonly associated with heavy rainfall, and using a variety of deterministic and ensemble-based numerical model tools
WPC Excessive Rainfall Threat Area Day3: In the Excessive Rainfall Outlooks, the Weather Prediction Center (WPC) forecasts the probability that rainfall will exceed flash flood guidance at a point. Gridded FFG is provided by the twelve NWS River Forecast Centers (RFCs) whose service areas cover the lower 48 states. WPC creates a national mosaic of FFG, whose 1, 3, and 6-hour values represent the amount of rainfall over those short durations which it is estimated would bring rivers and streams up to bankfull conditions. WPC estimates the likelihood that FFG will be exceeded by assessing environmental conditions (e.g. moisture content and steering winds), recognizing weather patterns commonly associated with heavy rainfall, and using a variety of deterministic and ensemble-based numerical model tools
WPC Excessive Rainfall Threat Area Forecast. In the Excessive Rainfall Outlooks, the Weather Prediction Center (WPC) forecasts the probability that rainfall will exceed flash flood guidance at a point. Gridded FFG is provided by the twelve NWS River Forecast Centers (RFCs) whose service areas cover the lower 48 states. WPC creates a national mosaic of FFG, whose 1, 3, and 6-hour values represent the amount of rainfall over those short durations which it is estimated would bring rivers and streams up to bankfull conditions. WPC estimates the
likelihood that FFG will be exceeded by assessing environmental conditions (e.g. moisture content and steering winds), recognizing weather patterns commonly associated with heavy rainfall, and using a variety of deterministic and ensemble-based numerical model tools
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
VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as otherscience applications requiring improved fire mapping fidelity.
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.
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.
Canada’s Wildfire Smoke Prediction System (FireWork) produces daily smoke forecast maps. This map represents small particles (PM2.5), the whole atmosphere compressed (column), and the smoke-only after subtracting the atmospheric background (diff). Smoke from wildfires in forests and grasslands can be a major source of air pollution for Canadians. The fine particles in the smoke can be a serious risk to health, particularly for children, seniors and those with heart or lung disease. Because smoke may be carried thousands of kilometers downwind, distant locations can be affected almost as severely as areas close to the fire. To help Canadians be better prepared, wildfire smoke forecast maps are available through the Government of Canada’s FireWork system. FireWork is an air quality prediction system that indicates how smoke from wildfires is expected to move across North America over the next 72 hours.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
The Geostationary Lightning Mapper, or GLM, on board Geostationary Operational Environmental Satellite– R Series spacecraft, is the first operational lightning mapper flown in geostationary orbit. GLM detects the light emitted by lightning at the tops of clouds day and night and collects information such as the frequency, location and extent of lightning discharges. The instrument measures total lightning, both in-cloud and cloud-to-ground, to aid in forecasting developing severe storms and a wide range of high-impact environmental phenomena including hailstorms, microburst winds, tornadoes, hurricanes, ash clouds. and snowstorms
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
LandsatLook images are full resolution files derived from Landsat Level 1 data products. The images are compressed and stretched to create an image optimized for image selection and visual interpretation. "Natural Color" is a false color composite that minimizes haze by combining bands 6 (1.57 - 1.65µ), 5 (0.85 - 0.88µ), and 4 (0.64 - 0.67µ) as Red, Green, and Blue.
The LandsatLook "Thermal" image is a one-band gray scale .jpg image made to display thermal properties of the scene. The image is made from band 10 (10.60 - 11.19µ) with darker values representing colder temperatures.
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/.
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.
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.
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.
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).
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).
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).
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.
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.
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.
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).
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).
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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)
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)
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)
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
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
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 regionQuick guide
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 regionQuick guide
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 regionQuick guide
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 regionQuick guide
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
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 regionQuick guide
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
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
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
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
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 BasinQuick guide
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
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 BasinQuick guide
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
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 BasinQuick guide
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
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 RegionQuick guide
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
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 RegionQuick guide
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
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 RegionQuick guide
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
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.
USQuick guide
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
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 BasinQuick guide
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
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
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)
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)
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)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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).
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.
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.
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).
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.
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).
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.
Night time image from the DNB (Day/Night Band) of the VIIRS sensor on S-NPP and NOAA-20 that represents panchromatic spectra covering a wavelength range of 500–900 nm.
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.
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.
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.
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.
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).
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.
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.
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.
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
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.
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.
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
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.
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.