[NWS-Alerts-All]

NWS Alerts (All)

[NWS-Alerts-Coastal-Hazards]

NWS Alerts (Coastal Hazards)

[NWS-Alerts-Convective]

NWS Alerts (Convective)

[NWS-Alerts-Fire-Weather]

NWS Alerts (Fire Weather)

[NWS-Alerts-Hydrologic]

NWS Alerts (Hydrologic)

[NWS-Alerts-Marine]

NWS Alerts (Marine)

[NWS-Alerts-Non-Weather-Emergencies]

NWS Alerts (Non-Weather Emergencies)

[NWS-Alerts-Non-Convective]

NWS Alerts (Non Convective)

[NWS-Alerts-Other]

NWS Alerts (Other)

[NWS-Alerts-Tropical]

NWS Alerts (Tropical)

[NWS-Alerts-Winter-Weather]

NWS Alerts (Winter Weather)

[DayLandCloudFire-RGB-GINA]

This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic Information Network of Alaska (GINA).

[FireTemperature-RGB-GINA]

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

[DayLandCloudFire-RGB-GINA]

This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic Information Network of Alaska (GINA).

[FireTemperature-RGB-GINA]

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

[CIMSS-CTCtargets]

CIMSS-Cloud Top Cooling targets

[AIRMET-ICE]

AIRMET Icing Advisory

[AIRMET-IFR]

AIRMET-IFR Advisory

[AIRMET-MTN]

AIRMET-Mountain Obscured Advisory

[TAF]

Terminal Aerodrome Forecast (TAF)

[AIRMET-TURB]

AIRMET-Turlulence Advisory

[VAA]

Volcanic Ash Advisories: Ash Clouds

[turb-grid-30-31-kft]

[turb-grid-32-33-kft]

[turb-grid-34-35-kft]

[turb-grid-36-37-kft]

[turb-grid-38-39-kft]

turb-grid-38-39-kft

[turb-grid-40-41-kft]

[turb-poly-30-31-kft]

[turb-poly-32-33-kft]

[turb-poly-34-35-kft]

[turb-poly-36-37-kft]

[turb-poly-38-39-kft]

[turb-poly-40-41-kft]

[turb-poly-Max-Value-30-41-kft]

[turb-grid-Max-Value-30-41-kft]

[mean-snow-cover-1988-2017]

Global 4 km Multisensor Automated Snow and Ice Maps (GMASI) developed at the NOAA/NESDIS Center for Satellite Applications and Research (STAR). The main function of the GMASI is to routinely generate global continuous maps of snow and ice cover distribution from combined observations in the visible/infrared and in the microwave spectral bands from operational meteorological polar orbiting and geostationary satellites.

[j01-sic]

The Sea Ice Concentration products uses threshold reflectance (temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km resolution covered by ice. The product is available over oceans, seas and lakes only under clear-sky conditions that is determined by VIIRS cloud mask.

[j01-ist]

The Sea Ice Temperature product uses a split window algorithm that is dependent on bands M15 (~11 um) and M16 (~12 um) along with satellite scan angle to come up with an atmospheric correction term that adjusts clear window Brightness Temperature to come up with a final IST value that is at 750 m resolution and has been shown to be within 1.5 K of validation measurements. The product is available over all water bodies, including rivers under clear-sky conditions that is determined by VIIRS cloud mask.

[j01-ithk]

The Sea Ice Thickness product uses a one-dimensional thermodynamic ice model (OTIM) The OTIM is based on the surface energy balance but does not directly use any channel data. Instead, it takes into account variables such as VIIRS ice surface temperature and the VIIRS cloud mask to determine sea and lake ice thickness. The product is at 750 m resolution and available over all water bodies, including rivers under clear sky conditions that is determined by VIIRS cloud mask.

[j02-sic]

The Sea Ice Concentration products uses threshold reflectance (temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km resolution covered by ice. The product is available over oceans, seas and lakes only under clear-sky conditions that is determined by VIIRS cloud mask.

[j02-ist]

The Sea Ice Temperature product uses a split window algorithm that is dependent on bands M15 (~11 um) and M16 (~12 um) along with satellite scan angle to come up with an atmospheric correction term that adjusts clear window Brightness Temperature to come up with a final IST value that is at 750 m resolution and has been shown to be within 1.5 K of validation measurements. The product is available over all water bodies, including rivers under clear-sky conditions that is determined by VIIRS cloud mask.

[j02-ithk]

The Sea Ice Thickness product uses a one-dimensional thermodynamic ice model (OTIM) The OTIM is based on the surface energy balance but does not directly use any channel data. Instead, it takes into account variables such as VIIRS ice surface temperature and the VIIRS cloud mask to determine sea and lake ice thickness. The product is at 750 m resolution and available over all water bodies, including rivers under clear sky conditions that is determined by VIIRS cloud mask.

[snpp-sic]

The Sea Ice Concentration products uses threshold reflectance (temperature) tests to detect possible ice cover for daytime (nighttime). Then uses a tie-point algorithm to determine fraction of grid cell at 1-km resolution covered by ice. The product is available over oceans, seas and lakes only under clear-sky conditions that is determined by VIIRS cloud mask.

[snpp-ist]

The Sea Ice Temperature product uses a split window algorithm that is dependent on bands M15 (~11 um) and M16 (~12 um) along with satellite scan angle to come up with an atmospheric correction term that adjusts clear window Brightness Temperature to come up with a final IST value that is at 750 m resolution and has been shown to be within 1.5 K of validation measurements. The product is available over all water bodies, including rivers under clear-sky conditions that is determined by VIIRS cloud mask.

[snpp-ithk]

The Sea Ice Thickness product uses a one-dimensional thermodynamic ice model (OTIM) The OTIM is based on the surface energy balance but does not directly use any channel data. Instead, it takes into account variables such as VIIRS ice surface temperature and the VIIRS cloud mask to determine sea and lake ice thickness. The product is at 750 m resolution and available over all water bodies, including rivers under clear sky conditions that is determined by VIIRS cloud mask.

[AFIMG-Points]

VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software.

[DayLandCloudFire-RGB-GINA]

This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic Information Network of Alaska (GINA).

[FireTemperature-RGB-GINA]

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

[DayLandCloudFire-RGB-GINA]

This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic Information Network of Alaska (GINA).

[FireTemperature-RGB-GINA]

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

[DayLandCloudFire-RGB-GINA]

This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic Information Network of Alaska (GINA).

[FireTemperature-RGB-GINA]

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

[AFIMG-Points]

VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software.

[DayLandCloudFire-RGB-GINA]

This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic Information Network of Alaska (GINA).

[FireTemperature-RGB-GINA]

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

[WFIGS-Perimeters]

This product represents the best available perimeters for recent and ongoing wildland fires in the United States. It is produced by the Wildland Fire Interagency Geospatial Services (WFIGS) Group and provides authoritative geospatial data products under the interagency Wildland Fire Data Program. Hosted in the National Interagency Fire Center ArcGIS Online Organization (The NIFC Org), WFIGS provides both internal and public facing data, accessible in a variety of formats.

[WFIGS-Points]

[AFIMG-Points]

VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software.

[DayLandCloudFire-RGB-GINA]

This RGB is created by assigning the VIIRS 3.74um channel to red, 0.87um channel to green, and the 0.64um channel to blue. It is used to assess fire perimeters and burn scars. These data are produced by the Geographic Information Network of Alaska (GINA).

[FireTemperature-RGB-GINA]

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

[ABI-Flood-DCOM]

ABI on GOES-18 & GOES-19 from NOAA

[River-Flood-ABI]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 5-min CONUS imagery since sunrise on the given day. These products are expected to be most useful in mid- and low-latitude locations. CONUS region Quick guide

[River-Flood-ABItsp]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 5-min CONUS imagery since sunrize through the given hour. These products are expected to be most useful in mid- and low-latitude locations. CONUS region Quick guide

[River-Flood-ABI-hourly]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 5-min CONUS imagery since sunrize through the given hour. These products are expected to be most useful in mid- and low-latitude locations. CONUS region Quick guide

[River-Flood-ABItsp-hourly]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 5-min CONUS imagery since sunrize through the given hour. These products are expected to be most useful in mid- and low-latitude locations. CONUS region Quick guide

[RIVER-FLD-AHI]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available 10-min imagery since sunrise on the given day. These products are expected to be most useful in mid- and low-latitude locations. For more information visit: Here

[RIVER-FLD-joint-ABI]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available ABI-full disk imagery and VIIRS imagery since sunrise on the given day. For more information visit: Here

[RIVER-FLD-joint-AHI]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available AHI-full disk imagery and VIIRS imagery since sunrise on the given day. For more information visit: Here

[VIIRS-ABI-Flood]

[RIVER-FLDglobal-composite1]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 1 day. For more information visit: Here

[RIVER-FLDglobal-composite]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 5 days. For more information visit: Here

[RIVER-FLDall-AP]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Alaska region Quick guide

[RIVER-FLDtsp-AP]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Alaska region(Transparent flood-free land) Quick guide

[RIVER-FLDglobal]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Global(CSPP product) Quick guide

[RIVER-FLDall-MB]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Missouri Basin Quick guide

[RIVER-FLDtsp-MB]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Missouri Basin(Transparent flood-free land) Quick guide

[RIVER-FLDall-NC]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. North Central Basin Quick guide

[RIVER-FLDtsp-NC]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. North Central Basin(Transparent flood-free land) Quick guide

[RIVER-FLDall-NE]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. North East Basin Quick guide

[RIVER-FLDtsp-NE]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. North East Basin(Transparent flood-free land) Quick guide

[RIVER-FLDall-NW]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Northwest Region Quick guide

[RIVER-FLDtsp-NW]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Northwest Region(Transparent flood-free land) Quick guide

[RIVER-FLDall-SE]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Southeast Region Quick guide

[RIVER-FLDtsp-SE]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Southeast Region(Transparent flood-free land) Quick guide

[RIVER-FLDall-SW]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Southwest Region Quick guide

[RIVER-FLDtsp-SW]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Southwest Region(Transparent flood-free land) Quick guide

[RIVER-FLDall-US]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. US Quick guide

[RIVER-FLDall-WG]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. West Gulf Basin Quick guide

[RIVER-FLDtsp-WG]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. West Gulf Basin(Transparent flood-free land) Quick guide

[VIIRS-3Dflood]

VIIRS downscaling software is designed to downscale the VIIRS 375-m flood products to 30-m flood products. The software uses VIIRS daily composite flood product as a basis for the downscaling.

[lsat8-llook-fc]

View of lsat8-llook-fc-scenes

[lsat8-llook-tir]

View of lsat8-llook-tir-scenes

[wrs2-land]

The Worldwide Reference System (WRS) is a global notation used in cataloging Landsat data. Landsat 8 and Landsat 7 follow the WRS-2, as did Landsat 5 and Landsat 4.

[lsat9-llook-fc]

View of lsat9-llook-fc-scenes

[lsat9-llook-tir]

View of lsat9-llook-tir-scenes

[HRR-CONUS-FZRN-SFC]

HRRR ConUS Latest Freezing MASK

[HRR-CONUS-ICEP-SFC]

HRRR ConUS Latest Ice Mask

[HRR-CONUS-PCP-LATEST]

View of HRR-CONUS-PCP-SFC

[HRR-CONUS-RAIN-SFC]

HRRR ConUS Latest Rain Mask

[HRR-CONUS-PCP-SFC]

HRR-CONUS-PCP-SFC

[HRR-CONUS-RADAR-LATEST]

View of HRR-CONUS-PCP-SFC

[HRR-CONUS-SNOW-SFC]

HRRR ConUS Latest Snow Mask

[RAP-CONUS-PRAT-SFC-DBZ]

View of RAP-CONUS-PRAT-SFC

[TAF]

Terminal Aerodrome Forecast (TAF)

[AFIMG-Points]

VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software.

[AFIMG-Points]

VIIRS 375m I-band high spatial resolution imagery provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters. The 375m data also has improved nighttime performance. Consequently, these data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. These data represent mean fire radiative power from SNPP and NOAA-20 Direct Broadcast imagery processed with CSPP software.

[G19-ABI-CONUS-BAND01]

The 0.47 µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47 µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47 µm band is more sensitive to aerosols / dust / smoke because it samples a part of the electromagnetic spectrum where clear-sky atmospheric scattering is important

[G19-ABI-CONUS-BAND02]

The ‘Red’ Visible band – 0.64 µm – has the finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color” imagery.

[G19-ABI-CONUS-BAND03]

The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.

[G19-ABI-CONUS-BAND04]

The Cirrus Band (1.37 µm) is unique among the reflective bands on the ABI in that it occupies a region of very strong absorption bywater vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.

[G19-ABI-CONUS-BAND05]

The Snow/Ice band takes advantage of the difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.

[G19-ABI-CONUS-BAND06]

The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free background over land), to create cloud masking and to detect hot spots.

[G19-ABI-CONUS-BAND07]

The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.

[G19-ABI-CONUS-BAND07-FIRE]

The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.

[G19-ABI-CONUS-BAND08]

The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating upper/ mid-level moisture (for legacy vertical moisture profiles) and identifying regions where the potential for turbulence exists. Further, it can be used to validate numerical model initialization and warming/cooling with time can reveal vertical motions at mid- and upper levels.

[G19-ABI-CONUS-BAND08-VAPR]

The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, andis used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating upper/ mid-level moisture (for legacy vertical moisture profiles) and identifying regions where the potential for turbulence exists. Further, it can be used to validate numerical model initialization and warming/cooling with time can reveal vertical motions at mid- and upper levels.

[G19-ABI-CONUS-BAND09]

The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9µm by water vapor.

[G19-ABI-CONUS-BAND09-VAPR]

The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9µm by water vapor.

[G19-ABI-CONUS-BAND10]

The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect snow bands.

[G19-ABI-CONUS-BAND10-VAPR]

The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect snow bands.

[G19-ABI-CONUS-BAND11]

The infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived brightness temperature. Water droplets also have different emissivity properties for 8.5μm radiation compared to other wavelengths. The 8.5μm band was not available on either the Legacy GOES Imager or GOES Sounder.

[G19-ABI-CONUS-BAND12]

The 9.6 μm band gives information both day and night about the dynamics of the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor absorb 9.6 μm atmospheric energy. The cooling effect is especially apparent at large zenith angles. This band alone cannot diagnose total column ozone: product generation using other bands will be necessary for that.

[G19-ABI-CONUS-BAND13]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G19-ABI-CONUS-BAND13-GRAD]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G19-ABI-CONUS-BAND14]

The infrared 11.2 μm band is a window channel; however, there is absorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7μm.

[G19-ABI-CONUS-BAND15]

Absorption and re-emission of water vapor, particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3μm) for the monitoring of simple atmospheric phenomena.

[G19-ABI-CONUS-BAND16]

Products derived using the infrared 13.3 μm “Carbon Dioxide” band can be used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.

[G19-ABI-CONUS-airmass]

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 upper-level frontal boundaries and identify high-, mid-, and low-level clouds.

[G19-ABI-CONUS-cloud-phase]

The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-CONUS-convection]

The Day Convection RGB was designed to emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.

[G19-ABI-CONUS-day-microphysics-abi]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-CONUS-water-vapors2]

The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and tropopause fold-driven high-impact wind events. Analysis of moist/dry layers is also important for predicting changes in hurricane intensity and extratropical transition.

[G19-ABI-CONUS-dust]

Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.

[G19-ABI-CONUS-fire-temperature-awips]

This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.

[G19-ABI-CONUS-fog]

G16-ABI-CONUS-fog

[G19-ABI-CONUS-geo-color]

GeoColor imagery provides as close an approximation to daytime True Color imagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.

[G19-ABI-CONUS-ir-sandwich]

With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature. Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.

[G19-ABI-CONUS-night-microphysics]

The distinction between low clouds and fog in satellite imagery is often a challenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the Nighttime Microphysics (NtMicro) RGB adds another channel difference (12.4- 10.4 µm) as a proxy to cloud thickness and repeats the use of the 10.4 µm thermal channel to enhance areas of warm (i.e. low) clouds where fog is more likely. The NtMicro RGB is also an efficient tool to quickly identify other cloud types in the mid and upper atmosphere.

[G19-ABI-CONUS-snow-fog]

On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.

[G19-ABI-CONUS-so2]

Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.

[G19-ABI-CONUS-true-color]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-CONUS-ash]

The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.

[G19-ABI-FD-BAND01]

The 0.47 µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47 µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47µm band is more sensitive to aerosols / dust / smoke because it samples a part of the electromagnetic spectrum where clear-sky atmospheric scattering is important.

[G19-ABI-FD-BAND02]

The ‘Red’ Visible band – 0.64 µm – has the finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color imagery.

[G19-ABI-FD-BAND03]

The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.

[G19-ABI-FD-BAND04]

The Cirrus Band (1.37 µm) is unique among the reflective bands on the ABI in that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.

[G19-ABI-FD-BAND05]

The Snow/Ice band takes advantage of the difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.

[G19-ABI-FD-BAND06]

The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free background over land), to create cloud masking and to detect hot spots.

[G19-ABI-FD-BAND07]

The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.

[G19-ABI-FD-BAND08]

The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating upper/ mid-level moisture (for legacy vertical moisture profiles) and identifying regions where the potential for turbulence exists. Further, it can be used to validate numerical model initialization and warming/cooling with time can reveal vertical motions at mid- and upper levels.

[G19-ABI-FD-BAND09]

The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9µm by water vapor.

[G19-ABI-FD-BAND10]

The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect snow bands.

[G19-ABI-FD-BAND11]

The infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceive brightness temperature. Water droplets also have different emissivity properties for 8.5 μm radiation compared to other wavelengths. The 8.5μm band was not available on either the Legacy GOES Imager or GOES Sounder.

[G19-ABI-FD-BAND12]

The 9.6 μm band gives information both day and night about the dynamics of the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor absorb 9.6 μm atmospheric energy. The cooling effect is especially apparent at large zenith angles. This band alone cannot diagnose total column ozone: product generation using other bands will be necessary for that.

[G19-ABI-FD-BAND13]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G19-ABI-FD-BAND13-GRAD]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G19-ABI-FD-BAND14]

The infrared 11.2 μm band is a window channel; however, there is absorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7 μm.

[G19-ABI-FD-BAND15]

Absorption and re-emission of water vapor, particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3μm band and the 10.3μm are used to compute the ‘split window difference’. The 10.3μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3μm) for the monitoring of simple atmospheric phenomena.

[G19-ABI-FD-BAND16]

Products derived using the infrared 13.3 μm “Carbon Dioxide” band can be used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.

[G19-ABI-FD-airmass]

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 upper-level frontal boundaries and identify high-, mid-, and low-level clouds.

[G19-ABI-FD-cloud-phase]

The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-FD-convection]

The Day Convection RGB was designed to emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.

[G19-ABI-FD-day-microphysics-abi]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-FD-water-vapors2]

The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and tropopause fold-driven high-impact wind events. Analysis of moist/dry layers is also important for predicting changes in hurricane intensity and extratropical transition.

[G19-ABI-FD-dust]

Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.

[G19-ABI-FD-fire-temperature-awips]

This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.

[G19-ABI-FD-fog]

G16-ABI-FD-fog

[G19-ABI-FD-geo-color]

GeoColor imagery provides as close an approximation to daytime True Color imagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.

[G19-ABI-FD-ir-sandwich]

With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature. Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.

[G19-ABI-FD-night-microphysics]

The distinction between low clouds and fog in satellite imagery is often a challenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the Nighttime Microphysics (NtMicro) RGB adds another channel difference (12.4- 10.4 µm) as a proxy to cloud thickness and repeats the use of the 10.4 µm thermal channel to enhance areas of warm (i.e. low) clouds where fog is more likely. The NtMicro RGB is also an efficient tool to quickly identify other cloud types in the mid and upper atmosphere.

[G19-ABI-FD-snow-fog]

On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.

[G19-ABI-FD-so2]

Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.

[G19-ABI-FD-true-color]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-FD-ash]

The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.

[GOES-EAST-L2-AOD-DQF0]

[G19-L2-CONUS-FLS-Thickness]

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

[G19-L2-CONUS-FLS-IFR]

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

[G19-L2-CONUS-FLS-LIFR]

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

[G19-L2-CONUS-FLS-MVFR]

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

[G19-ABI-MESO1-BAND01]

The 0.47 µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47 µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47 µm band is more sensitive to aerosols / dust / smoke because it samples a part of the electromagnetic spectrum where clear-sky atmospheric scattering is important.

[G19-ABI-MESO1-BAND02]

The ‘Red’ Visible band – 0.64 µm – has the finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color” imagery.

[G19-ABI-MESO1-BAND03]

The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.

[G19-ABI-MESO1-BAND04]

The Cirrus Band (1.37 µm) is unique among the reflective bands on the ABI in that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.

[G19-ABI-MESO1-BAND05]

The Snow/Ice band takes advantage of the difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.

[G19-ABI-MESO1-BAND06]

The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free background over land), to create cloud masking and to detect hot spots.

[G19-ABI-MESO1-BAND07]

The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.

[G19-ABI-MESO1-BAND08]

The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating upper/ mid-level moisture (for legacy vertical moisture profiles) and identifying regions where the potential for turbulence exists. Further, it can be used to validate numerical model initialization and warming/cooling with time can reveal vertical motions at mid- and upper levels.

[G19-ABI-MESO1-BAND09]

The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9µm by water vapor.

[G19-ABI-MESO1-BAND10]

The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes thatare rich in sulphur dioxide (SO2) and track LakeEffect snow bands.

[G19-ABI-MESO1-BAND11]

The infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived brightness temperature. Water droplets also have different emissivity properties for 8.5μm radiation compared to other wavelengths. The 8.5μm band was not available on either the Legacy GOES Imager or GOES Sounder.

[G19-ABI-MESO1-BAND12]

The 9.6 μm band gives information both day and night about the dynamics of the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor absorb 9.6 μm atmospheric energy. The cooling effect is especially apparent at large zenith angles. This band alone cannot diagnose total column ozone: product generation using other bands will be necessary for that.

[G19-ABI-MESO1-BAND13]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G19-ABI-MESO1-BAND13-GRAD]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G19-ABI-MESO1-BAND14]

The infrared 11.2 μm band is a window channel; however, there is absorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7μm.

[G19-ABI-MESO1-BAND15]

Absorption and re-emission of water vapor, particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3μm) for the monitoring of simple atmospheric phenomena.

[G19-ABI-MESO1-BAND16]

Products derived using the infrared 13.3 μm “Carbon Dioxide” band can be used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.

[G19-ABI-MESO1-airmass]

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 upper-level frontal boundaries and identify high-, mid-, and low-level clouds.

[G19-ABI-MESO1-cloud-phase]

The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-MESO1-convection]

The Day Convection RGB was designed to emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.

[G19-ABI-MESO1-day-microphysics-abi]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-MESO1-water-vapors2]

The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and tropopause fold-driven high-impact wind events. Analysis of moist/dry layers is also important for predicting changes in hurricane intensity and extratropical transition.

[G19-ABI-MESO1-dust]

Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.

[G19-ABI-MESO1-fire-temperature-awips]

This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.

[G19-ABI-MESO1-fog]

G16-ABI-MESO1-fog

[G19-ABI-MESO1-geo-color]

GeoColor imagery provides as close an approximation to daytime True Color imagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.

[G19-ABI-MESO1-ir-sandwich]

With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature. Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.

[G19-ABI-MESO1-night-microphysics]

The distinction between low clouds and fog in satellite imagery is often a challenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the Nighttime Microphysics (NtMicro) RGB adds another channel difference (12.4- 10.4 µm) as a proxy to cloud thickness and repeats the use of the 10.4 µm thermal channel to enhance areas of warm (i.e. low) clouds where fog is more likely. The NtMicro RGB is also an efficient tool to quickly identify other cloud types in the mid and upper atmosphere.

[G19-ABI-MESO1-snow-fog]

On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.

[G19-ABI-MESO1-so2]

Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.

[G19-ABI-MESO1-true-color]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-MESO1-ash]

The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.

[G19-ABI-MESO2-BAND01]

The 0.47 µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47 µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47 µm band is more sensitive to aerosols / dust / smoke because it samples a part of the electromagnetic spectrum where clear-sky atmospheric scattering is important.

[G19-ABI-MESO2-BAND02]

The ‘Red’ Visible band – 0.64 µm – has the finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color” imagery.

[G19-ABI-MESO2-BAND03]

The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.

[G19-ABI-MESO2-BAND04]

The Cirrus Band (1.37 µm) is unique among the reflective bands on the ABI in that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.

[G19-ABI-MESO2-BAND05]

The Snow/Ice band takes advantage of the difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.

[G19-ABI-MESO2-BAND06]

IThe 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free background over land), to create cloud masking and to detect hot spots.

[G19-ABI-MESO2-BAND07]

The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.

[G19-ABI-MESO2-BAND08]

The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating upper/ mid-level moisture (for legacy vertical moisture profiles) and identifying regions where the potential for turbulence exists. Further, it can be used to validate numerical model initialization and warming/cooling with time can reveal vertical motions at mid- and upper levels.

[G19-ABI-MESO2-BAND09]

The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9µm by water vapor.

[G19-ABI-MESO2-BAND10]

The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect snow bands.

[G19-ABI-MESO2-BAND11]

The infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived brightness temperature. Water droplets also have different emissivity properties for 8.5μm radiation compared to other wavelengths. The 8.5μm band was not available on either the Legacy GOES Imager or GOES Sounder.

[G19-ABI-MESO2-BAND12]

The 9.6 μm band gives information both day and night about the dynamics of the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor absorb 9.6 μm atmospheric energy. The cooling effect is especially apparent at large zenith angles. This band alone cannot diagnose total column ozone: product generation using other bands will be necessary for that.

[G19-ABI-MESO2-BAND13]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G19-ABI-MESO2-BAND13-GRAD]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G19-ABI-MESO2-BAND14]

The infrared 11.2 μm band is a window channel; however, there is absorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7μm.

[G19-ABI-MESO2-BAND15]

Absorption and re-emission of water vapor, particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3μm) for the monitoring of simple atmospheric phenomena.

[G19-ABI-MESO2-BAND16]

Products derived using the infrared 13.3 μm “Carbon Dioxide” band can be used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.

[G19-ABI-MESO2-airmass]

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 upper-level frontal boundaries and identify high-, mid-, and low-level clouds.

[G19-ABI-MESO2-cloud-phase]

The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-MESO2-convection]

The Day Convection RGB was designed to emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.

[G19-ABI-MESO2-day-microphysics-abi]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-MESO2-water-vapors2]

The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and tropopause fold-driven high-impact wind events. Analysis of moist/dry layers is also important for predicting changes in hurricane intensity and extratropical transition.

[G19-ABI-MESO2-dust]

Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.

[G19-ABI-MESO2-fire-temperature-awips]

This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.

[G19-ABI-MESO2-fog]

G16-ABI-MESO2-fog

[G19-ABI-MESO2-geo-color]

GeoColor imagery provides as close an approximation to daytime True Color imagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.

[G19-ABI-MESO2-ir-sandwich]

With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature. Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.

[G19-ABI-MESO2-night-microphysics]

The distinction between low clouds and fog in satellite imagery is often a challenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the Nighttime Microphysics (NtMicro) RGB adds another channel difference (12.4- 10.4 µm) as a proxy to cloud thickness and repeats the use of the 10.4 µm thermal channel to enhance areas of warm (i.e. low) clouds where fog is more likely. The NtMicro RGB is also an efficient tool to quickly identify other cloud types in the mid and upper atmosphere.

[G19-ABI-MESO2-snow-fog]

On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.

[G19-ABI-MESO2-so2]

Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.

[G19-ABI-MESO2-true-color]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-MESO2-ash]

The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.

[G19-ABI-CONUS-airmass]

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 upper-level frontal boundaries and identify high-, mid-, and low-level clouds.

[G19-ABI-CONUS-cloud-phase]

The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-CONUS-convection]

The Day Convection RGB was designed to emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.

[G19-ABI-CONUS-day-microphysics-abi]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-CONUS-water-vapors2]

The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and tropopause fold-driven high-impact wind events. Analysis of moist/dry layers is also important for predicting changes in hurricane intensity and extratropical transition.

[G19-ABI-CONUS-dust]

Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.

[G19-ABI-CONUS-fire-temperature-awips]

This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.

[G19-ABI-CONUS-fog]

G16-ABI-CONUS-fog

[G19-ABI-CONUS-geo-color]

GeoColor imagery provides as close an approximation to daytime True Color imagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.

[G19-ABI-CONUS-ir-sandwich]

With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature. Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.

[G19-ABI-CONUS-night-microphysics]

The distinction between low clouds and fog in satellite imagery is often a challenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the Nighttime Microphysics (NtMicro) RGB adds another channel difference (12.4- 10.4 µm) as a proxy to cloud thickness and repeats the use of the 10.4 µm thermal channel to enhance areas of warm (i.e. low) clouds where fog is more likely. The NtMicro RGB is also an efficient tool to quickly identify other cloud types in the mid and upper atmosphere.

[G19-ABI-CONUS-snow-fog]

On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.

[G19-ABI-CONUS-so2]

Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.

[G19-ABI-CONUS-true-color]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-CONUS-ash]

The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.

[G19-ABI-FD-airmass]

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 upper-level frontal boundaries and identify high-, mid-, and low-level clouds.

[G19-ABI-FD-cloud-phase]

The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-FD-convection]

The Day Convection RGB was designed to emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.

[G19-ABI-FD-day-microphysics-abi]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-FD-water-vapors2]

The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and tropopause fold-driven high-impact wind events. Analysis of moist/dry layers is also important for predicting changes in hurricane intensity and extratropical transition.

[G19-ABI-FD-dust]

Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.

[G19-ABI-FD-fire-temperature-awips]

This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.

[G19-ABI-FD-fog]

G16-ABI-FD-fog

[G19-ABI-FD-geo-color]

GeoColor imagery provides as close an approximation to daytime True Color imagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.

[G19-ABI-FD-ir-sandwich]

With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature. Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.

[G19-ABI-FD-night-microphysics]

The distinction between low clouds and fog in satellite imagery is often a challenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the Nighttime Microphysics (NtMicro) RGB adds another channel difference (12.4- 10.4 µm) as a proxy to cloud thickness and repeats the use of the 10.4 µm thermal channel to enhance areas of warm (i.e. low) clouds where fog is more likely. The NtMicro RGB is also an efficient tool to quickly identify other cloud types in the mid and upper atmosphere.

[G19-ABI-FD-snow-fog]

On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.

[G19-ABI-FD-so2]

Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.

[G19-ABI-FD-true-color]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-FD-ash]

The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.

[G19-ABI-MESO1-airmass]

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 upper-level frontal boundaries and identify high-, mid-, and low-level clouds.

[G19-ABI-MESO1-cloud-phase]

The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-MESO1-convection]

The Day Convection RGB was designed to emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.

[G19-ABI-MESO1-day-microphysics-abi]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-MESO1-water-vapors2]

The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and tropopause fold-driven high-impact wind events. Analysis of moist/dry layers is also important for predicting changes in hurricane intensity and extratropical transition.

[G19-ABI-MESO1-dust]

Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.

[G19-ABI-MESO1-fire-temperature-awips]

This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.

[G19-ABI-MESO1-fog]

G16-ABI-MESO1-fog

[G19-ABI-MESO1-geo-color]

GeoColor imagery provides as close an approximation to daytime True Color imagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.

[G19-ABI-MESO1-ir-sandwich]

With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature. Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.

[G19-ABI-MESO1-night-microphysics]

The distinction between low clouds and fog in satellite imagery is often a challenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the Nighttime Microphysics (NtMicro) RGB adds another channel difference (12.4- 10.4 µm) as a proxy to cloud thickness and repeats the use of the 10.4 µm thermal channel to enhance areas of warm (i.e. low) clouds where fog is more likely. The NtMicro RGB is also an efficient tool to quickly identify other cloud types in the mid and upper atmosphere.

[G19-ABI-MESO1-snow-fog]

On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.

[G19-ABI-MESO1-so2]

Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.

[G19-ABI-MESO1-true-color]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-MESO1-ash]

The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.

[G19-ABI-MESO2-airmass]

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 upper-level frontal boundaries and identify high-, mid-, and low-level clouds.

[G19-ABI-MESO2-cloud-phase]

The Baseline Cloud Phase product describes the cloud-top composition. There are four phase categories: liquid water cloud top, with temperatures warmer than 273K or colder than 273 K (i.e.,supercooled), mixed-phase (liquid water and ice) clouds and glaciated (ice) clouds.

[G19-ABI-MESO2-convection]

The Day Convection RGB was designed to emphasize convection with strong updrafts and small ice particles indicative of severe storms. This RGB helps increase nowcasting capabilities of severe storms by identifying the early stage of strong convection. Knowing the microphysical characteristics of convective clouds helps determine storm strength and stage to improve nowcasts and short-term forecasts. Bright yellow in the RGB indicates strong updrafts prior to the mature storm stage.

[G19-ABI-MESO2-day-microphysics-abi]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-MESO2-water-vapors2]

The Differential Water Vapor RGB was designed to analyze water vapor distribution. It can be used to identify upper level moisture boundaries, trough/ridge patterns, potential vorticity (PV) anomalies, and the influences of PV anomalies and stratospheric air on rapid cyclogenesis and tropopause fold-driven high-impact wind events. Analysis of moist/dry layers is also important for predicting changes in hurricane intensity and extratropical transition.

[G19-ABI-MESO2-dust]

Dust can be hard to see in VIS and IR imagery because it is optically thin, or because it appears similar to other cloud types such as cirrus. The RGB product is able to contrast airborne dust from clouds using band differencing and the IR thermal channel. The IR band differencing allows dust storms to be observed during both daytime and at night. Dust appears pink/magenta during the day and can vary in color at night depending on height. Dust is also distinguishable in the RGB from land surfaces like deserts as well as oceans, given sufficient thickness/density. For cloudy regions this RGB also allows users to infer relative height of the observed cloud top surfaces as well as cloud phase and thicknesses.

[G19-ABI-MESO2-fire-temperature-awips]

This RGB allows the user to identify where the most intense fires are occurring and differentiate these from “cooler” fires. The RGB takes advantage of the fact that from 3.9 µm to shorter wavelengths, background solar radiation and surface reflectance increases. This means that fires need to be more intense in order to be detected by the 2.2 and 1.6 µm bands, as more intense fires emit more radiation at these wavelengths. Therefore, small/”cool” fires will only show up at 3.9 µm and appear red while increases in fire intensity cause greater contributions of the other channels resulting in white very intense fires.

[G19-ABI-MESO2-fog]

G16-ABI-MESO2-fog

[G19-ABI-MESO2-geo-color]

GeoColor imagery provides as close an approximation to daytime True Color imagery as is possible and thus allows for intuitive interpretation of meteorological and surface-based features. At night, instead of being dark like in other visible bands, an IR based multispectral product is provided that differentiates between low liquid water clouds and higher ice clouds. A static city lights database derived from the VIIRS Day Night Band is provided as the nighttime background for geo-referencing.

[G19-ABI-MESO2-ir-sandwich]

With this product(s) it is possible to monitor those cloud top features of mature convective storms which are possibly related to severity. It combines two different image types, a high resolution visible band, and (most often) a colour-enhanced infrared window image. Such combination provides information on both cloud top ‘morphology’ and cloud top temperature. Mature thunderstorm cloud top features, such as overshooting tops, gravity waves, and above-anvil ice plumes are seen in solar channels due to the shadows these cast.

[G19-ABI-MESO2-night-microphysics]

The distinction between low clouds and fog in satellite imagery is often a challenge. While the difference in the 10.4 and 3.9 µm channels has been a regularly applied product to meet aviation forecast needs, the Nighttime Microphysics (NtMicro) RGB adds another channel difference (12.4- 10.4 µm) as a proxy to cloud thickness and repeats the use of the 10.4 µm thermal channel to enhance areas of warm (i.e. low) clouds where fog is more likely. The NtMicro RGB is also an efficient tool to quickly identify other cloud types in the mid and upper atmosphere.

[G19-ABI-MESO2-snow-fog]

On heritage GOES, it was difficult to distinguish white “reflective” snow from white “reflective” clouds on visible imagery. On the GOES-R series, the reflectance of snow, water, and ice clouds varies across the visible, near infrared, and infrared. The channels which bring out the distinguishing differences are combined in the Day Snow-Fog RGB to show greater contrast between snow and cloud than is generally possible with a single channel.

[G19-ABI-MESO2-so2]

Sulfur dioxide (SO2) is a gas commonly released into the atmosphere during volcanic eruptions. In high concentrations it is toxic to humans and has considerable environmental effects, including volcanic smog, acid rain, and is harmful to vegetation downwind of the eruption. The SO2 RGB product can be used to detect and monitor large sulfur dioxide emissions from volcanoes, as well industrial facilities such as power plants.

[G19-ABI-MESO2-true-color]

As with the Day Cloud Phase Distinction RGB, this RGB allows a user to discern phase changes in a cloud by observing color changes in the RGB. The use of the Band 4 ‘Cirrus Channel’ at 1.38 µm (rather than the 10.3 µm Clean Window Channel) allows for better detection and discrimination of thin and thick cirrus clouds. This RGB has a very similar look to the Day Cloud Phase Distinction RGB in regions of clear skies.

[G19-ABI-MESO2-ash]

The Volcanic Ash False Color product determines the location for satellite pixels potentially containing volcanic ash. This product helps forecasters identify potentially hazardous areas and issue more accurate aviation and public health warnings. Volcanic ash products are also useful for enhancing ash dispersion and trajectory prediction models.

[G18-ABI-CONUS-airmass]

G18-ABI-CONUS-airmass

[G18-ABI-CONUS-ash]

G18-ABI-CONUS-ash

[G18-ABI-CONUS-cloud-phase]

G18-ABI-CONUS-cloud-phase

[G18-ABI-CONUS-convection]

G18-ABI-CONUS-convection

[G18-ABI-CONUS-day-microphysics-abi]

G18-ABI-CONUS-day-microphysics-abi

[G18-ABI-CONUS-dust]

G18-ABI-CONUS-dust

[G18-ABI-CONUS-fire-temperature-awips]

G18-ABI-CONUS-fire-temperature-awips

[G18-ABI-CONUS-fog]

G18-ABI-CONUS-fog

[G18-ABI-CONUS-geo-color]

G18-ABI-CONUS-geo-color

[G18-ABI-CONUS-ir-sandwich]

G18-ABI-CONUS-ir-sandwich

[G18-ABI-CONUS-night-microphysics]

G18-ABI-CONUS-night-microphysics

[G18-ABI-CONUS-snow-fog]

G18-ABI-CONUS-snow-fog

[G18-ABI-CONUS-so2]

G18-ABI-CONUS-so2

[G18-ABI-CONUS-true-color]

View of G18-ABI-CONUS-geo-color

[G18-ABI-CONUS-water-vapors2]

G18-ABI-CONUS-water-vapors2

[G18-ABI-CONUS-BAND01]

The 0.47µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47 µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47 µm band is more sensitive to aerosols / dust /smoke because it samples a part of the electromagnetic spectrum where clear-sky atmospheric scattering is important

[G18-ABI-CONUS-BAND02]

he ‘Red’ Visible band – 0.64 µm – has the finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color” imagery.

[G18-ABI-CONUS-BAND03]

The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.

[G18-ABI-CONUS-BAND04]

The Cirrus Band (1.37 µm) is unique among the reflective bands on the ABI in that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.

[G18-ABI-CONUS-BAND05]

The Snow/Ice band takes advantage of the difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.

[G18-ABI-CONUS-BAND06]

The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free background over land), to create cloud masking and to detect hot spots.

[G18-ABI-CONUS-BAND07]

The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.

[G18-ABI-CONUS-BAND07-FIRE]

The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.

[G18-ABI-CONUS-BAND08]

http://cimss.ssec.wisc.edu/goes/OCLOFactSheetPDFs/ABIQuickGuide_Band08.pdfThe 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring.

[G18-ABI-CONUS-BAND08-VAPR]

The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring.

[G18-ABI-CONUS-BAND09]

The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9 µm by water vapor.

[G18-ABI-CONUS-BAND09-VAPR]

The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9 µm by water vapor.

[G18-ABI-CONUS-BAND10]

The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect snow bands.

[G18-ABI-CONUS-BAND10-VAPR]

The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect snow bands.

[G18-ABI-CONUS-BAND11]

he infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived brightness temperature. Water droplets also have different emissivity properties for 8.5 μm radiation compared to other wavelengths. The 8.5 μm band was not available on either the Legacy GOES Imager or GOES Sounder.

[G18-ABI-CONUS-BAND12]

The 9.6 μm band gives information both day and night about the dynamics of the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor absorb 9.6 μm atmospheric energy. The cooling effect is especially apparent at large zenith angles. This band alone cannot diagnose total column ozone: product generation using other bands will be necessary for that.

[G18-ABI-CONUS-BAND13]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G18-ABI-CONUS-BAND13-GRAD]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G18-ABI-CONUS-BAND14]

http://cimss.ssec.wisc.edu/goes/OCLOFactSheetPDFs/ABIQuickGuide_Band14.pdfThe infrared 11.2 μm band is a window channel; however, there is absorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7 μm.

[G18-ABI-CONUS-BAND15]

Absorption and re-emission of water vapor, particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3 μm) for the monitoring of simple atmospheric phenomena.

[G18-ABI-CONUS-BAND16]

Products derived using the infrared 13.3 μm “Carbon Dioxide” band can be used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3 μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.

[G18-ABI-FD-airmass]

G18-ABI-FD-airmass

[G18-ABI-FD-ash]

G18-ABI-FD-ash

[G18-ABI-FD-cloud-phase]

G18-ABI-FD-cloud-phase

[G18-ABI-FD-convection]

G18-ABI-FD-convection

[G18-ABI-FD-day-microphysics-abi]

G18-ABI-FD-day-microphysics-abi

[G18-ABI-FD-dust]

G18-ABI-FD-dust

[G18-ABI-FD-fire-temperature-awips]

G18-ABI-FD-fire-temperature-awips

[G18-ABI-FD-fog]

G18-ABI-FD-fog

[G18-ABI-FD-geo-color]

G18-ABI-FD-geo-color

[G18-ABI-FD-ir-sandwich]

G18-ABI-FD-ir-sandwich

[G18-ABI-FD-night-microphysics]

G18-ABI-FD-night-microphysics

[G18-ABI-FD-snow-fog]

G18-ABI-FD-snow-fog

[G18-ABI-FD-so2]

G18-ABI-FD-so2

[G18-ABI-FD-true-color]

View of G18-ABI-FD-geo-color

[G18-ABI-FD-water-vapors2]

G18-ABI-FD-water-vapors2

[G18-ABI-FD-BAND13-GRAD]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G18-ABI-FD-BAND01]

The 0.47µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47µm band is more sensitive to aerosols / dust /smoke because it samples a part of the electromagnetic spectrum where clear-sky atmospheric scattering is important.

[G18-ABI-FD-BAND02]

he ‘Red’ Visible band – 0.64 µm – has the finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color” imagery.

[G18-ABI-FD-BAND03]

The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.

[G18-ABI-FD-BAND04]

The Cirrus Band (1.37 µm) is unique among the reflective bands on the ABI in that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.

[G18-ABI-FD-BAND05]

The Snow/Ice band takes advantage of the difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.

[G18-ABI-FD-BAND06]

The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free background over land), to create cloud masking and to detect hot spots.

[G18-ABI-FD-BAND07]

The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well assignificant reflected solar radiation during the day.

[G18-ABI-FD-BAND08]

The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring.

[G18-ABI-FD-BAND09]

The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9 µm by water vapor.

[G18-ABI-FD-BAND10]

The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles),identify regions where the potential forturbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect snow bands.

[G18-ABI-FD-BAND11]

he infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived brightness temperature. Water droplets also have different emissivity properties for 8.5 μm radiation compared to other wavelengths. The 8.5 μm band was not available on either the Legacy GOES Imager or GOES Sounder.

[G18-ABI-FD-BAND12]

The 9.6 μm band gives information both day and night about the dynamics of the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor absorb 9.6 μm atmospheric energy. The cooling effect is especially apparent at large zenith angles. This band alone cannot diagnose total column ozone: product generation using other bands will be necessary for that.

[G18-ABI-FD-BAND13]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G18-ABI-FD-BAND14]

The infrared 11.2 μm band is a window channel; however, there is absorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7 μm.

[G18-ABI-FD-BAND15]

Absorption and re-emission of water vapor, particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3 μm) for the monitoring of simple atmospheric phenomena.

[G18-ABI-FD-BAND16]

Products derived using the infrared 13.3 μm “Carbon Dioxide” band can be used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3 μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.

[GOES-WEST-L2-AOD-DQF0]

[G18-L2-CONUS-FLS-Thickness]

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

[G18-L2-CONUS-FLS-IFR]

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

[G18-L2-CONUS-FLS-LIFR]

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

[G18-L2-CONUS-FLS-MVFR]

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

[G18-L2-FD-FLS-Thickness]

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

[G18-L2-FD-FLS-IFR]

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

[G18-L2-FD-FLS-LIFR]

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

[G18-L2-FD-FLS-MVFR]

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

[G18-ABI-MESO1-airmass]

G18-ABI-MESO1-airmass

[G18-ABI-MESO1-ash]

G18-ABI-MESO1-ash

[G18-ABI-MESO1-cloud-phase]

G18-ABI-MESO1-cloud-phase

[G18-ABI-MESO1-convection]

G18-ABI-MESO1-convection

[G18-ABI-MESO1-day-microphysics-abi]

G18-ABI-MESO1-day-microphysics-abi

[G18-ABI-MESO1-dust]

G18-ABI-MESO1-dust

[G18-ABI-MESO1-fire-temperature-awips]

G18-ABI-MESO1-fire-temperature-awips

[G18-ABI-MESO1-fog]

G18-ABI-MESO1-fog

[G18-ABI-MESO1-geo-color]

G18-ABI-MESO1-geo-color

[G18-ABI-MESO1-ir-sandwich]

G18-ABI-MESO1-ir-sandwich

[G18-ABI-MESO1-night-microphysics]

G18-ABI-MESO1-night-microphysics

[G18-ABI-MESO1-snow-fog]

G18-ABI-MESO1-snow-fog

[G18-ABI-MESO1-so2]

G18-ABI-MESO1-so2

[G18-ABI-MESO1-true-color]

View of G18-ABI-MESO1-geo-color

[G18-ABI-MESO1-water-vapors2]

G18-ABI-MESO1-water-vapors2

[G18-ABI-MESO1-BAND01]

The 0.47µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47µm band is more sensitive to aerosols / dust / smoke because it samples a part of the electromagnetic spectrum where clear-sky atmospheric scattering is important

[G18-ABI-MESO1-BAND02]

he ‘Red’ Visible band – 0.64 µm – has the finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color” imagery.

[G18-ABI-MESO1-BAND03]

The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.

[G18-ABI-MESO1-BAND04]

The Cirrus Band (1.37 µm) is unique among the reflective bands on the ABI in that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.

[G18-ABI-MESO1-BAND05]

The Snow/Ice band takes advantage of the difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.

[G18-ABI-MESO1-BAND06]

The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free background over land), to create cloud masking and to detect hot spots.

[G18-ABI-MESO1-BAND07]

The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.

[G18-ABI-MESO1-BAND08]

The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring

[G18-ABI-MESO1-BAND09]

The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9 µm by water vapor.

[G18-ABI-MESO1-BAND10]

The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lower tropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect snow bands.

[G18-ABI-MESO1-BAND11]

he infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived brightness temperature. Water droplets also have different emissivity properties for 8.5 μm radiation compared to other wavelengths. The 8.5 μm band was not available on either theLegacy GOES Imager or GOES Sounder.

[G18-ABI-MESO1-BAND12]

The 9.6 μm band gives information both day and night about the dynamics of the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor absorb 9.6 μm atmospheric energy. The cooling effect is especially apparent at large zenith angles. This band alone cannot diagnose total column ozone: product generation using other bands will be necessary for that.

[G18-ABI-MESO1-BAND13]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G18-ABI-MESO1-BAND13-GRAD]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G18-ABI-MESO1-BAND14]

The infrared 11.2 μm band is a window channel; however, there is absorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7 μm.

[G18-ABI-MESO1-BAND15]

Absorption and re-emission of water vapor, particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3 μm) for the monitoring of simple atmospheric phenomena.

[G18-ABI-MESO1-BAND16]

Products derived using the infrared 13.3 μm “Carbon Dioxide” band can be used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) sky observations and to identify Volcanic Ash. The 13.3 μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.

[G18-ABI-MESO2-airmass]

G18-ABI-MESO2-airmass

[G18-ABI-MESO2-ash]

G18-ABI-MESO2-ash

[G18-ABI-MESO2-cloud-phase]

G18-ABI-MESO2-cloud-phase

[G18-ABI-MESO2-convection]

G18-ABI-MESO2-convection

[G18-ABI-MESO2-day-microphysics-abi]

G18-ABI-MESO2-day-microphysics-abi

[G18-ABI-MESO2-dust]

G18-ABI-MESO2-dust

[G18-ABI-MESO2-fire-temperature-awips]

G18-ABI-MESO2-fire-temperature-awips

[G18-ABI-MESO2-fog]

G18-ABI-MESO2-fog

[G18-ABI-MESO2-geo-color]

G18-ABI-MESO2-geo-color

[G18-ABI-MESO2-ir-sandwich]

G18-ABI-MESO2-ir-sandwich

[G18-ABI-MESO2-night-microphysics]

G18-ABI-MESO2-night-microphysics

[G18-ABI-MESO2-snow-fog]

G18-ABI-MESO2-snow-fog

[G18-ABI-MESO2-so2]

G18-ABI-MESO2-so2

[G18-ABI-MESO2-true-color]

View of G18-ABI-MESO2-geo-color

[G18-ABI-MESO2-water-vapors2]

G18-ABI-MESO2-water-vapors2

[G18-ABI-MESO2-BAND01]

The 0.47µm, or “Blue” visible band, is one of two visible bands on the ABI, and provides data for monitoring aerosols. Included on NASA’s MODIS and Suomi NPP VIIRS instruments, this band provides well-established benefits. The geostationary ABI 0.47µm band will provide nearly continuous daytime observations of dust, haze, smoke and clouds. The 0.47µm band is more sensitive to aerosols / dust / smoke because it samples a part of the electromagnetic spectrum where clear-sky atmospheric scattering is important

[G18-ABI-MESO2-BAND02]

he ‘Red’ Visible band – 0.64 µm – has the finest spatial resolution (0.5 km at the subsatellite point) of all ABI bands. Thus it is ideal to identify small-scale features such as river fogs and fog/clear air boundaries, or overshooting tops or cumulus clouds. It has also been used to document daytime snow and ice cover, diagnose low-level cloud-drift winds, assist with detection of volcanic ash and analysis of hurricanes and winter storms. The ‘Red’ Visible band is also essential for creation of “true color” imagery.

[G18-ABI-MESO2-BAND03]

The 0.86 μm band (a reflective band) detects daytime clouds, fog, and aerosols and is used to compute the normalized difference vegetation index (NDVI). Its nickname is the “veggie” or “vegetation” band. The 0.86 μm band can detect burn scars and thereby show land characteristics to determine fire and run-off potential. Vegetated land, in general, shows up brighter in this band than in visible bands. Landwater contrast is also large in this band.

[G18-ABI-MESO2-BAND04]

The Cirrus Band (1.37 µm) is unique among the reflective bands on the ABI in that it occupies a region of very strong absorption by water vapor in the electromagnetic spectrum. It will detect very thin cirrus clouds during the day.

[G18-ABI-MESO2-BAND05]

The Snow/Ice band takes advantage of the difference between the refraction components of water and ice at 1.61 µm. Liquid water clouds are bright in this channel; ice clouds are darker because ice absorbs (rather than reflects) radiation at 1.61 µm. Thus you can infer cloud phase. Fires can also be detected at night using this band.

[G18-ABI-MESO2-BAND06]

The 2.24 μm band, in conjunction with other bands, enables cloud particle size estimation. Cloud particle size changes can indicate cloud development. The 2.24 μm band is also used with other bands to estimate aerosol particle size (by characterizing the aerosol-free background over land), to create cloud masking and to detect hot spots.

[G18-ABI-MESO2-BAND07]

The 3.9 μm band can be used to identify fog and low clouds at night, identify fire hot spots, detect volcanic ash, estimate sea-surface temperatures, and discriminate between ice crystal sizes during the day. Low-level atmospheric vector winds can be estimated with this band, and the band can be used to study urban heat islands. The 3.9 μm is unique among ABI bands because it senses both emitted terrestrial radiation as well as significant reflected solar radiation during the day.

[G18-ABI-MESO2-BAND08]

The 6.2 µm “Upper-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking upper-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring

[G18-ABI-MESO2-BAND09]

The 6.9 µm “Mid-level water vapor” band is one of three water vapor bands on the ABI, and is used for tracking middle-tropospheric winds, identifying jet streams, forecasting hurricane track and mid-latitude storm motion, monitoring severe weather potential, estimating mid-level moisture (for legacy vertical moisture profiles) and identifying regions where turbulence might exist. Surface features are usually not apparent in this band. Brightness Temperatures show cooling because of absorption of energy at 6.9 µm by water vapor.

[G18-ABI-MESO2-BAND10]

The 7.3 µm “Lower-level water vapor” band is one of three water vapor bands on the ABI. It typically senses farthest down into the midtroposphere in cloud-free regions, to around 500-750 hPa. It is used to track lowertropospheric winds, identify jet streaks, monitor severe weather potential, estimate lower-level moisture (for legacy vertical moisture profiles), identify regions where the potential for turbulence exists, highlight volcanic plumes that are rich in sulphur dioxide (SO2) and track LakeEffect snow bands.

[G18-ABI-MESO2-BAND11]

he infrared 8.5 μm band is a window channel; there is little atmospheric absorption of energy in clear skies at this wavelength (unless SO2 from a volcanic eruption is present). However, knowledge of emissivity is important in the interpretation of this Band: Differences in surface emissivity at 8.5 μm occur over different soil types, affecting the perceived brightness temperature. Water droplets also have different emissivity properties for 8.5 μm radiation compared to other wavelengths. The 8.5 μm band was not available on either the Legacy GOES Imager or GOES Sounder.

[G18-ABI-MESO2-BAND12]

The 9.6 μm band gives information both day and night about the dynamics of the atmosphere near the tropopause. This band shows cooler temperatures than the clean window band because both ozone and water vapor absorb 9.6 μm atmospheric energy. The cooling effect is especially apparent at large zenith angles. This band alone cannot diagnose total column ozone: product generation using other bands will be necessary for that.

[G18-ABI-MESO2-BAND13]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G18-ABI-MESO2-BAND13-GRAD]

The 10.3 μm “clean” infrared window band is less sensitive than other infrared window bands to water vapor absorption, and therefore improves atmospheric moisture corrections, aids in cloud and other atmospheric feature identification/classification, estimation of cloudtop brightness temperature and cloud particle size, and surface property characterization in derived products.

[G18-ABI-MESO2-BAND14]

The infrared 11.2 μm band is a window channel; however, there is absorption of energy by water vapor at this wavelength. Brightness Temperatures (BTs) are affected by this absorption, and 11.2 μm BTs will be cooler than clean window (10.3 μm) BTs – by an amount that is a function of the amount of moisture in the atmosphere. This band has similarities to the legacy infrared channel at 10.7 μm.

[G18-ABI-MESO2-BAND15]

Absorption and re-emission of water vapor, particularly in the lower troposphere, slightly cools most non-cloud brightness temperatures (BTs) in the 12.3 μm band compared to the other infrared window channels: the more water vapor, the greater the BT difference. The 12.3 μm band and the 10.3 μm are used to compute the ‘split window difference’. The 10.3 μm “Clean Window” channel is a better choice than the “Dirty Window” (12.3 μm) for the monitoring of simple atmospheric phenomena.

[G18-ABI-MESO2-BAND16]

Products derived using the infrared 13.3 μm “Carbon Dioxide” band can be used to delineate the tropopause, to estimate cloudtop heights, to discern the level of Derived Motion Winds, to supplement Automated Surface Observing System (ASOS) skyobservations and to identify Volcanic Ash. The 13.3 μm band is vital for Baseline Products; that is demonstrated by its presence on heritage GOES Imagers and Sounders. Despite its importance in products, the CO2 channel is typically not used for visual interpretation of weather events.

[G18-ABI-CONUS-airmass]

G18-ABI-CONUS-airmass

[G18-ABI-CONUS-ash]

G18-ABI-CONUS-ash

[G18-ABI-CONUS-cloud-phase]

G18-ABI-CONUS-cloud-phase

[G18-ABI-CONUS-convection]

G18-ABI-CONUS-convection

[G18-ABI-CONUS-day-microphysics-abi]

G18-ABI-CONUS-day-microphysics-abi

[G18-ABI-CONUS-dust]

G18-ABI-CONUS-dust

[G18-ABI-CONUS-fire-temperature-awips]

G18-ABI-CONUS-fire-temperature-awips

[G18-ABI-CONUS-fog]

G18-ABI-CONUS-fog

[G18-ABI-CONUS-geo-color]

G18-ABI-CONUS-geo-color

[G18-ABI-CONUS-ir-sandwich]

G18-ABI-CONUS-ir-sandwich

[G18-ABI-CONUS-night-microphysics]

G18-ABI-CONUS-night-microphysics

[G18-ABI-CONUS-snow-fog]

G18-ABI-CONUS-snow-fog

[G18-ABI-CONUS-so2]

G18-ABI-CONUS-so2

[G18-ABI-CONUS-true-color]

View of G18-ABI-CONUS-geo-color

[G18-ABI-CONUS-water-vapors2]

G18-ABI-CONUS-water-vapors2

[G18-ABI-FD-airmass]

G18-ABI-FD-airmass

[G18-ABI-FD-ash]

G18-ABI-FD-ash

[G18-ABI-FD-cloud-phase]

G18-ABI-FD-cloud-phase

[G18-ABI-FD-convection]

G18-ABI-FD-convection

[G18-ABI-FD-day-microphysics-abi]

G18-ABI-FD-day-microphysics-abi

[G18-ABI-FD-dust]

G18-ABI-FD-dust

[G18-ABI-FD-fire-temperature-awips]

G18-ABI-FD-fire-temperature-awips

[G18-ABI-FD-fog]

G18-ABI-FD-fog

[G18-ABI-FD-geo-color]

G18-ABI-FD-geo-color

[G18-ABI-FD-ir-sandwich]

G18-ABI-FD-ir-sandwich

[G18-ABI-FD-night-microphysics]

G18-ABI-FD-night-microphysics

[G18-ABI-FD-snow-fog]

G18-ABI-FD-snow-fog

[G18-ABI-FD-so2]

G18-ABI-FD-so2

[G18-ABI-FD-true-color]

View of G18-ABI-FD-geo-color

[G18-ABI-FD-water-vapors2]

G18-ABI-FD-water-vapors2

[G18-ABI-MESO1-airmass]

G18-ABI-MESO1-airmass

[G18-ABI-MESO1-ash]

G18-ABI-MESO1-ash

[G18-ABI-MESO1-cloud-phase]

G18-ABI-MESO1-cloud-phase

[G18-ABI-MESO1-convection]

G18-ABI-MESO1-convection

[G18-ABI-MESO1-day-microphysics-abi]

G18-ABI-MESO1-day-microphysics-abi

[G18-ABI-MESO1-dust]

G18-ABI-MESO1-dust

[G18-ABI-MESO1-fire-temperature-awips]

G18-ABI-MESO1-fire-temperature-awips

[G18-ABI-MESO1-fog]

G18-ABI-MESO1-fog

[G18-ABI-MESO1-geo-color]

G18-ABI-MESO1-geo-color

[G18-ABI-MESO1-ir-sandwich]

G18-ABI-MESO1-ir-sandwich

[G18-ABI-MESO1-night-microphysics]

G18-ABI-MESO1-night-microphysics

[G18-ABI-MESO1-snow-fog]

G18-ABI-MESO1-snow-fog

[G18-ABI-MESO1-so2]

G18-ABI-MESO1-so2

[G18-ABI-MESO1-true-color]

View of G18-ABI-MESO1-geo-color

[G18-ABI-MESO1-water-vapors2]

G18-ABI-MESO1-water-vapors2

[G18-ABI-MESO2-airmass]

G18-ABI-MESO2-airmass

[G18-ABI-MESO2-ash]

G18-ABI-MESO2-ash

[G18-ABI-MESO2-cloud-phase]

G18-ABI-MESO2-cloud-phase

[G18-ABI-MESO2-convection]

G18-ABI-MESO2-convection

[G18-ABI-MESO2-day-microphysics-abi]

G18-ABI-MESO2-day-microphysics-abi

[G18-ABI-MESO2-dust]

G18-ABI-MESO2-dust

[G18-ABI-MESO2-fire-temperature-awips]

G18-ABI-MESO2-fire-temperature-awips

[G18-ABI-MESO2-fog]

G18-ABI-MESO2-fog

[G18-ABI-MESO2-geo-color]

G18-ABI-MESO2-geo-color

[G18-ABI-MESO2-ir-sandwich]

G18-ABI-MESO2-ir-sandwich

[G18-ABI-MESO2-night-microphysics]

G18-ABI-MESO2-night-microphysics

[G18-ABI-MESO2-snow-fog]

G18-ABI-MESO2-snow-fog

[G18-ABI-MESO2-so2]

G18-ABI-MESO2-so2

[G18-ABI-MESO2-true-color]

View of G18-ABI-MESO2-geo-color

[G18-ABI-MESO2-water-vapors2]

G18-ABI-MESO2-water-vapors2

[globalir]

This product is a global composite of imagery from multiple satellites. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

[globalir-avn]

This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

[globalir-bd]

This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

[globalir-funk]

This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

[globalir-nhc]

This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

[globalir-rr]

This product is based on a statistical relationship between cloud top temperature and observed rain rate. It is derived every hour (at about 35-minutes after the hour UTC) using the global IR composite produced by the SSEC Data Center. While it shows the most current imagery, shifting occurs along composite seams.

[globalir-ott]

This product is an enhanced view of the global infrared composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

[globalvis]

This product is a global composite of imagery from multiple satellites. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

[globalvis-tsp]

This view is based on the global Visible composite product in which night time regions are rendered transparent. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

[global1kmvis]

This product is a 15-minute snapshot of a global composite of imagery from multiple satellites. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

[global1kmvisfull]

[globalwv]

This product is a global composite of imagery from multiple satellites. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

[globalwv-grad]

This product is an enhanced view of the global Water Vapor composite product. It is completed every hour (at about 35-minutes after the hour UTC) by the SSEC Data Center with the best available imagery. While it shows the most current imagery, shifting occurs along composite seams.

[GLERL-GLSEAimage]

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

[WICoast]

WI Coastal Imagery displays aerial photographs of the Lake Michigan coast of Wisconsin from 2007. The images are being used to monitor cladophora algae growth.

[WIcoastalshdrlf]

WI coastal shaded relief map generated from LiDAR data.

[RIVER-FLDglobal-composite1]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 1 day. For more information visit: Here

[RIVER-FLDglobal-composite]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available VIIRS daylight imagery over the past 5 days. For more information visit: Here

[RIVER-FLDall-AP]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Alaska region Quick guide

[RIVER-FLDtsp-AP]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Alaska region(Transparent flood-free land) Quick guide

[RIVER-FLDglobal]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Global(CSPP product) Quick guide

[RIVER-FLD-joint-ABI]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS, adapted to the GOES-ABI and Himawari -AHI. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. These products represent a composite of all available ABI-full disk imagery and VIIRS imagery since sunrise on the given day. For more information visit: Here

[RIVER-FLDall-MB]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Missouri Basin Quick guide

[RIVER-FLDtsp-MB]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Missouri Basin(Transparent flood-free land) Quick guide

[RIVER-FLDall-NC]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. North Central Basin Quick guide

[RIVER-FLDtsp-NC]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. North Central Basin(Transparent flood-free land) Quick guide

[RIVER-FLDall-NE]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. North East Basin Quick guide

[RIVER-FLDtsp-NE]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. North East Basin(Transparent flood-free land) Quick guide

[RIVER-FLDall-NW]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Northwest Region Quick guide

[RIVER-FLDtsp-NW]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Northwest Region(Transparent flood-free land) Quick guide

[RIVER-FLDall-SE]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Southeast Region Quick guide

[RIVER-FLDtsp-SE]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Southeast Region(Transparent flood-free land) Quick guide

[RIVER-FLDall-SW]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Southwest Region Quick guide

[RIVER-FLDtsp-SW]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. Southwest Region(Transparent flood-free land) Quick guide

[RIVER-FLDall-US]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. US Quick guide

[RIVER-FLDtsp-US]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. US(Transparent flood-free land) Quick guide

[RIVER-FLDall-WG]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. West Gulf Basin Quick guide

[RIVER-FLDtsp-WG]

CIMSS hosts a flood product developed at George Mason University (GMU) derived from VIIRS. The product provides an estimate of flooding water fractions, regions of ice, cloud, snow cover, and shadows. Products are generated with direct broadcast VIIRS data in near real-time. The success of the product has sparked interest from several river forecast centers (APRFC, NERFC, MBRFC, and WGRFC) as well as FEMA. These products could be useful to other institutions that monitor river ice and flooding conditions, especially in mid- and high-latitude locations. West Gulf Basin(Transparent flood-free land) Quick guide

[VIIRS-ABI-Flood]

[Earthquake-mag]

Earthquake Magnitude (Past 24hr)

[XREDFLAG]

The National Weather Service issues a variety of Weather warnings, watches and advisories. The event type is indicated on the map by different colors. This product contains Wildland Fire Weather Hazards VALID for a 48hr Window spanning from the previous 24hrs to 24hrs in the future at 1hr increments

[WFLASH]

Flash Flood Hazards

[FOP]

WPC FLood Outlook Product

[FLOODWARN]

Flood Warning Polygons

[XFLOODWARN]

XFLOODWARN

[HVTEC]

For Flood Warnings (FLW) and follow up Flood Statements (FLS) at specific river forecast points, the H-VTEC specifies the flood severity; immediate cause, timing of flood beginning, crest, and end; and how the flood compares to the flood of record.

[WFOG]

Fog Hazards

[Severe]

Tornado, Thunderstorm, Flash Flood and Marine Warning polygons.

[SAW]

Severe Weather Watch Box - Aviation

[WWTOR]

Tornado Watches and Warnings

[TSFCST]

National Hurricane Center Tropical Storm & Hurricane Forecast

[VAA]

Volcanic Ash Advisories: Ash Clouds

[WWIND]

Wind Hazards is a collection of alerts associated with all types of Wind related events. These Hazards are issued by the NWS WSFOs as Advisories, Watches and Warnings. WindEvents include Wind, LakeWind and HighWind categories. Click on objects to get a detailed description of the specific hazard.

[WWINTER]

Winter Weather is a collection of Hazards associated with all types of Winter precip and conditions. Hazards are issued by the NWS WSFOs as Advisories, Watches and Warnings. SnowEvents include SnowStorm, WinterStorm, Snow, HeavySnow, LakeEffectSnow and BlowingSnow. IceEvents include Sleet, HeavySleet, FreezingRain, IceStorm and FreezingFog. Click on objects to get a detailed description of the specific hazard.

[XWINTER]

The National Weather Service issues a variety of Winter Weather warnings, watches and advisories. The event type is indicated on the map by different colors. This product contains Winter Weather Hazards VALID for a 48hr Window spanning from the previous 24hrs to 24hrs in the future at 1hr increments.

[madisonir]

Infrared 6 inch Imagery of Madison

[NAIPWI]

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

[NAIPWICIR]

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

[WIcoastallidar]

WI Coastal LiDAR

[WIcoastalshdrlf]

WI coastal shaded relief map generated from LiDAR data.

[wi-hillshade]

WisconsinView is a remote sensing consortium and member of AmericaView.org. These Wisconsin lidar data sets were collected by aircraft and processed by state and county agencies. These data are hosted by WisconsinView and visualized here with coordination and funding from the WI State Dept. of Administration, Geographic Information Office and NOAA"s coastal management program.

[wilandsat]

This is a georeferenced poster from the USGS. The original source is: http://eros.usgs.gov/imagegallery/landsat-state-mosaics unfortunately the original poster imagery without graphics burned-in is not available.

[HIMAWARI-airmass]

HIMAWARI-airmass

[HIMAWARI-ash]

HIMAWARI-ash

[HIMAWARI-cloudtop]

HIMAWARI-cloudtop

[HIMAWARI-convection]

HIMAWARI-convection

[HIMAWARI-day-microphysics-ahi]

HIMAWARI-day-microphysics-ahi

[HIMAWARI-dust]

HIMAWARI-dust

[HIMAWARI-fire-temperature-awips]

HIMAWARI-fire-temperature-awips

[HIMAWARI-fog]

HIMAWARI-fog

[HIMAWARI-night-microphysics]

HIMAWARI-night-microphysics

[HIMAWARI-true-color]

HIMAWARI-true-color

[HIMAWARI-water-vapors2]

HIMAWARI-water-vapors2

[HIMAWARI-B01]

Himawari AHI Full Disk B01 "Blue" Visible

[HIMAWARI-B02]

Himawari AHI Full Disk B02 "Green" Visible

[HIMAWARI-B03]

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

[HIMAWARI-B04]

Himawari AHI Full Disk B04 "Veggie"

[HIMAWARI-B05]

Himawari AHI Full Disk B05 Snow and Ice

[HIMAWARI-B06]

Himawari AHI Full Disk B06 Cloud Particle Size

[HIMAWARI-B07]

Himawari AHI Full Disk B07 "Fire"

[HIMAWARI-B07-FIRE]

View of HIMAWARI-B07

[HIMAWARI-B08]

Himawari AHI Full Disk B08 Upper-level Water Vapor

[HIMAWARI-B08-VAPR]

View of HIMAWARI-B08

[HIMAWARI-B09]

Himawari AHI Full Disk B09 Mid-level Water Vapor

[HIMAWARI-B09-VAPR]

View of HIMAWARI-09

[HIMAWARI-B10]

Himawari AHI Full Disk B10 Low-level Water Vapor

[HIMAWARI-B10-VAPR]

View of HIMAWARI-B10

[HIMAWARI-B11]

Himawari AHI Full Disk B11 Cloud Phase

[HIMAWARI-B12]

Himawari AHI Full Disk B12 Ozone

[HIMAWARI-B13]

Himawari AHI Full Disk B13 "Clean" Infrared

[HIMAWARI-B13-GRAD]

View of HIMAWARI-B13

[HIMAWARI-B14]

Himawari AHI Full Disk B14 Infrared

[HIMAWARI-B14-GRAD]

View of HIMAWARI-B14

[HIMAWARI-B15]

Himawari AHI Full Disk B15 "Dirty" Infrared

[HIMAWARI-B15-GRAD]

View of HIMAWARI-B15

[HIMAWARI-B16]

Himawari AHI Full Disk B16 Carbon Dioxide

[HIMAWARI-Japan-airmass]

HIMAWARI-Japan-airmass

[HIMAWARI-Japan-ash]

HIMAWARI-Japan-ash

[HIMAWARI-Japan-B01]

HIMAWARI-Japan-B01

[HIMAWARI-Japan-B02]

HIMAWARI-Japan-B02

[HIMAWARI-Japan-B03]

HIMAWARI-Japan-B03

[HIMAWARI-Japan-B04]

HIMAWARI-Japan-B04

[HIMAWARI-Japan-B05]

HIMAWARI-Japan-B05

[HIMAWARI-Japan-B06]

HIMAWARI-Japan-B06

[HIMAWARI-Japan-B07]

HIMAWARI-Japan-B07

[HIMAWARI-Japan-B08]

HIMAWARI-Japan-B08

[HIMAWARI-Japan-B09]

HIMAWARI-Japan-B09

[HIMAWARI-Japan-B10]

HIMAWARI-Japan-B10

[HIMAWARI-Japan-B11]

HIMAWARI-Japan-B11

[HIMAWARI-Japan-B12]

HIMAWARI-Japan-B12

[HIMAWARI-Japan-B13]

HIMAWARI-Japan-B13

[HIMAWARI-Japan-B14]

HIMAWARI-Japan-B14

[HIMAWARI-Japan-B15]

HIMAWARI-Japan-B15

[HIMAWARI-Japan-B16]

HIMAWARI-Japan-B16

[HIMAWARI-Japan-cloudtop]

HIMAWARI-Japan-cloudtop

[HIMAWARI-Japan-convection]

HIMAWARI-Japan-convection

[HIMAWARI-Japan-day-microphysics-ahi]

HIMAWARI-Japan-day-microphysics-ahi

[HIMAWARI-Japan-dust]

HIMAWARI-Japan-dust

[HIMAWARI-Japan-fire-temperature-awips]

HIMAWARI-Japan-fire-temperature-awips

[HIMAWARI-Japan-fog]

HIMAWARI-Japan-fog

[HIMAWARI-Japan-night-microphysics]

HIMAWARI-Japan-night-microphysics

[HIMAWARI-Japan-true-color]

HIMAWARI-Japan-true-color

[HIMAWARI-Japan-water-vapors2]

HIMAWARI-Japan-water-vapors2

[HIMAWARI-airmass]

HIMAWARI-airmass

[HIMAWARI-ash]

HIMAWARI-ash

[HIMAWARI-cloudtop]

HIMAWARI-cloudtop

[HIMAWARI-convection]

HIMAWARI-convection

[HIMAWARI-day-microphysics-ahi]

HIMAWARI-day-microphysics-ahi

[HIMAWARI-dust]

HIMAWARI-dust

[HIMAWARI-fire-temperature-awips]

HIMAWARI-fire-temperature-awips

[HIMAWARI-fog]

HIMAWARI-fog

[HIMAWARI-Japan-airmass]

HIMAWARI-Japan-airmass

[HIMAWARI-Japan-ash]

HIMAWARI-Japan-ash

[HIMAWARI-Japan-cloudtop]

HIMAWARI-Japan-cloudtop

[HIMAWARI-Japan-convection]

HIMAWARI-Japan-convection

[HIMAWARI-Japan-day-microphysics-ahi]

HIMAWARI-Japan-day-microphysics-ahi

[HIMAWARI-Japan-dust]

HIMAWARI-Japan-dust

[HIMAWARI-Japan-fire-temperature-awips]

HIMAWARI-Japan-fire-temperature-awips

[HIMAWARI-Japan-fog]

HIMAWARI-Japan-fog

[HIMAWARI-Japan-night-microphysics]

HIMAWARI-Japan-night-microphysics

[HIMAWARI-Japan-true-color]

HIMAWARI-Japan-true-color

[HIMAWARI-Japan-water-vapors2]

HIMAWARI-Japan-water-vapors2

[HIMAWARI-night-microphysics]

HIMAWARI-night-microphysics

[HIMAWARI-Target-airmass]

HIMAWARI-Target-airmass

[HIMAWARI-Target-ash]

HIMAWARI-Target-ash

[HIMAWARI-Target-cloudtop]

HIMAWARI-Target-cloudtop

[HIMAWARI-Target-convection]

HIMAWARI-Target-convection

[HIMAWARI-Target-day-microphysics-ahi]

HIMAWARI-Target-day-microphysics-ahi

[HIMAWARI-Target-dust]

HIMAWARI-Target-dust

[HIMAWARI-Target-fire-temperature-awips]

HIMAWARI-Target-fire-temperature-awips

[HIMAWARI-Target-fog]

HIMAWARI-Target-fog

[HIMAWARI-Target-night-microphysics]

HIMAWARI-Target-night-microphysics

[HIMAWARI-Target-true-color]

HIMAWARI-Target-true-color

[HIMAWARI-Target-water-vapors2]

HIMAWARI-Target-water-vapors2

[HIMAWARI-true-color]

HIMAWARI-true-color

[HIMAWARI-water-vapors2]

HIMAWARI-water-vapors2

[HIMAWARI-Target-airmass]

HIMAWARI-Target-airmass

[HIMAWARI-Target-ash]

HIMAWARI-Target-ash

[HIMAWARI-Target-B01]

HIMAWARI-Target-B01

[HIMAWARI-Target-B02]

HIMAWARI-Target-B02

[HIMAWARI-Target-B03]

HIMAWARI-Target-B03

[HIMAWARI-Target-B04]

HIMAWARI-Target-B04

[HIMAWARI-Target-B05]

HIMAWARI-Target-B05

[HIMAWARI-Target-B06]

HIMAWARI-Target-B06

[HIMAWARI-Target-B07]

HIMAWARI-Target-B07

[HIMAWARI-Target-B08]

HIMAWARI-Target-B08

[HIMAWARI-Target-B09]

HIMAWARI-Target-B09

[HIMAWARI-Target-B10]

HIMAWARI-Target-B10

[HIMAWARI-Target-B11]

HIMAWARI-Target-B11

[HIMAWARI-Target-B12]

HIMAWARI-Target-B12

[HIMAWARI-Target-B13]

HIMAWARI-Target-B13

[HIMAWARI-Target-B14]

HIMAWARI-Target-B14

[HIMAWARI-Target-B15]

HIMAWARI-Target-B15

[HIMAWARI-Target-B16]

HIMAWARI-Target-B16

[HIMAWARI-Target-cloudtop]

HIMAWARI-Target-cloudtop

[HIMAWARI-Target-convection]

HIMAWARI-Target-convection

[HIMAWARI-Target-day-microphysics-ahi]

HIMAWARI-Target-day-microphysics-ahi

[HIMAWARI-Target-dust]

HIMAWARI-Target-dust

[HIMAWARI-Target-fire-temperature-awips]

HIMAWARI-Target-fire-temperature-awips