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Note: this data is currently in testing and is experimental.<\/span><\/p>

Date of Images:<\/span><\/p>

Post-Event: 4/2/2025-4/6/2025<\/span><\/p>

Summary:<\/span><\/p>

Storms that produce damaging winds, hail, and tornadoes often exhibit distinct cloud-top patterns in satellite imagery. Two of these patterns, overshooting tops (OTs) and above-anvil cirrus plumes (AACPs), are frequently observed over intense storms. Using deep learning techniques, researchers at NASA Langley Research Center developed software to automatically detect these severe storm signatures in 500m GOES satellite data collected every 5 minutes. <\/span><\/p>

Emergency managers can use this raster data to quickly identify locations where severe storms likely occurred, complementing ground-based reports in regions with limited observations. Severe weather on the ground is more likely when OTs and AACPs are both detected and in areas where detections are clustered or observed repeatedly. Long, nearly straight lines of OT and AACP time-aggregated detections can indicate severe storm tracks.<\/span><\/p>

Suggested Use and Use Cases:<\/span><\/p>

Target audiences<\/span>: GIS specialists, tactical emergency managers<\/span><\/p>

Information Needed: <\/span>Near real-time situational awareness about locations where severe storm impacts may have occurred. <\/span><\/p>

Example Uses: <\/span>Target additional information collection. Prioritize field activities (e.g. assessments, resource allocation). <\/span><\/p>

Value Proposition: <\/span>Low resolution, low latency data shows areas that may have recently or may soon experience severe weather (nowcasting).<\/span><\/p>

Satellite/Sensor<\/span><\/p>

Satellite: <\/span>Geostationary Operational Environmental Satellite (GOES) Satellite remote sensing over North & South America<\/p>

Resolution: 5 minute temporal resolution observation frequency, 500m spatial resolution<\/span><\/p>

Product Specifications:<\/b><\/span><\/p>\n \n \n \n \n \n \n
\n

Capability type<\/p>\n <\/td>\n

\n

Experimental<\/p>\n <\/td>\n <\/tr>\n

\n

Data type<\/p>\n <\/td>\n

\n

Raster of binary OT detections aggregated over a designated\n incident time frame<\/p>\n <\/td>\n <\/tr>\n

\n

Min. update frequency<\/p>\n <\/td>\n

\n

Daily during working hours<\/p>\n <\/td>\n <\/tr>\n

\n

Area coverage<\/p>\n <\/td>\n

\n

Bounded subset<\/p>\n <\/td>\n <\/tr>\n

\n

Other key attributes<\/p>\n <\/td>\n

\n

Earliest detection time<\/i> - earliest time (in UTC) an overshooting top was flagged.<\/p>\n

Count of observations<\/i> - the number of Overshooting Tops detected within each geostationary satellite 0.5 km pixel for every 5 min conus scan from 2 April 12UTC - 6 April 12UTC.<\/p>\n

Binary Overshooting Detection Flag - <\/i>indicator for if any overshooting top was detected over a given 0.5km pixel.<\/p>\n <\/td>\n <\/tr>\n

\n

Visualization<\/p>\n <\/td>\n

\n

Single-color raster map<\/p>\n

Time-aggregated plots of severe storm signatures<\/p>\n <\/td>\n <\/tr>\n<\/tbody><\/table>

Caveats:<\/span><\/p>

While the presence of OTs and AACPs often correlates with severe weather, this product does not observe ground impacts and may detect features that do not correspond to severe weather.<\/span><\/p>

Esri REST Endpoint:<\/b><\/span><\/p>

See URL on the right side of the page.<\/p>

WMS Endpoint:<\/b><\/p>

https://maps.disasters.nasa.gov/ags03/services/us_svrwx_202504/severe_storm/MapServer/WMSServer<\/a><\/p>

Data Download:<\/b><\/p>

https://maps.disasters.nasa.gov/download/gis_products/event_specific/2025/us_svrwx_202504/severestorm/<\/a><\/p><\/div>", "mapName": "severe_storm", "description": "Note: this data is currently in testing and is experimental.Date of Images:Post-Event: 4/2/2025-4/6/2025Summary:Storms that produce damaging winds, hail, and tornadoes often exhibit distinct cloud-top patterns in satellite imagery. Two of these patterns, overshooting tops (OTs) and above-anvil cirrus plumes (AACPs), are frequently observed over intense storms. Using deep learning techniques, researchers at NASA Langley Research Center developed software to automatically detect these severe storm signatures in 500m GOES satellite data collected every 5 minutes. Emergency managers can use this raster data to quickly identify locations where severe storms likely occurred, complementing ground-based reports in regions with limited observations. Severe weather on the ground is more likely when OTs and AACPs are both detected and in areas where detections are clustered or observed repeatedly. Long, nearly straight lines of OT and AACP time-aggregated detections can indicate severe storm tracks.Suggested Use and Use Cases:Target audiences: GIS specialists, tactical emergency managersInformation Needed: Near real-time situational awareness about locations where severe storm impacts may have occurred. Example Uses: Target additional information collection. Prioritize field activities (e.g. assessments, resource allocation). Value Proposition: Low resolution, low latency data shows areas that may have recently or may soon experience severe weather (nowcasting).Satellite/SensorSatellite: Geostationary Operational Environmental Satellite (GOES) Satellite remote sensing over North & South AmericaResolution: 5 minute temporal resolution observation frequency, 500m spatial resolutionProduct Specifications:\n \n \n Capability type\n \n \n Experimental\n \n \n \n \n Data type\n \n \n Raster of binary OT detections aggregated over a designated\n incident time frame\n \n \n \n \n Min. update frequency\n \n \n Daily during working hours\n \n \n \n \n Area coverage\n \n \n Bounded subset\n \n \n \n \n Other key attributes\n \n \n Earliest detection time - earliest time (in UTC) an overshooting top was flagged.\n Count of observations - the number of Overshooting Tops detected within each geostationary satellite 0.5 km pixel for every 5 min conus scan from 2 April 12UTC - 6 April 12UTC.\n Binary Overshooting Detection Flag - indicator for if any overshooting top was detected over a given 0.5km pixel.\n \n \n \n \n Visualization\n \n \n Single-color raster map\n Time-aggregated plots of severe storm signatures\n \n \nCaveats:While the presence of OTs and AACPs often correlates with severe weather, this product does not observe ground impacts and may detect features that do not correspond to severe weather.Esri REST Endpoint:See URL on the right side of the page.WMS Endpoint:https://maps.disasters.nasa.gov/ags03/services/us_svrwx_202504/severe_storm/MapServer/WMSServerData Download:https://maps.disasters.nasa.gov/download/gis_products/event_specific/2025/us_svrwx_202504/severestorm/", "copyrightText": "NASA; Jack Cooney, Kris Bedka", "supportsDynamicLayers": true, "layers": [ { "id": 0, "name": "April 2, 2025-April 6, 2025 [Post-Event]", "parentLayerId": -1, "defaultVisibility": true, "subLayerIds": [ 1, 2, 3 ], "minScale": 0, "maxScale": 0, "type": "Group Layer", "supportsDynamicLegends": false }, { "id": 1, "name": "Earliest Overshooting Top Strom Updraft Time UTC at Pixel", "parentLayerId": 0, "defaultVisibility": true, "subLayerIds": null, "minScale": 0, "maxScale": 0, "type": "Raster Layer", "supportsDynamicLegends": true }, { "id": 2, "name": "Binary Overshooting Top Detection Flag", "parentLayerId": 0, "defaultVisibility": false, "subLayerIds": null, "minScale": 0, "maxScale": 0, "type": "Raster Layer", "supportsDynamicLegends": true }, { "id": 3, "name": "Count of Overshooting Top Detections", "parentLayerId": 0, "defaultVisibility": false, "subLayerIds": null, "minScale": 0, "maxScale": 0, "type": "Raster Layer", "supportsDynamicLegends": true } ], "tables": [], "spatialReference": { "wkid": 4326, "latestWkid": 4326, "xyTolerance": 8.983152841195215E-9, "zTolerance": 0.001, "mTolerance": 0.001, "falseX": -400, "falseY": -400, "xyUnits": 9.999999999999999E8, "falseZ": -100000, "zUnits": 10000, "falseM": -100000, "mUnits": 10000 }, "singleFusedMapCache": false, "initialExtent": { "xmin": -98.14614769000575, "ymin": 30.304574271555374, "xmax": -82.48828692213543, "ymax": 38.69364443337607, "spatialReference": { "wkid": 4326, "latestWkid": 4326, "xyTolerance": 8.983152841195215E-9, "zTolerance": 0.001, "mTolerance": 0.001, "falseX": -400, "falseY": -400, "xyUnits": 9.999999999999999E8, "falseZ": -100000, "zUnits": 10000, "falseM": -100000, "mUnits": 10000 } }, "fullExtent": { "xmin": -127.78816772438448, "ymin": 25.309120951479386, "xmax": -73.48816894553738, "ymax": 52.90912033078288, "spatialReference": { "wkid": 4326, "latestWkid": 4326, "xyTolerance": 8.983152841195215E-9, "zTolerance": 0.001, "mTolerance": 0.001, "falseX": -400, "falseY": -400, "xyUnits": 9.999999999999999E8, "falseZ": -100000, "zUnits": 10000, "falseM": -100000, "mUnits": 10000 } }, "datesInUnknownTimezone": false, "minScale": 0, "maxScale": 0, "units": "esriDecimalDegrees", "supportedImageFormatTypes": "PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP", "documentInfo": { "Title": "severe_storm", "Author": "", "Comments": "Note: this data is currently in testing and is experimental.Date of Images:Post-Event: 4/2/2025-4/6/2025Summary:Storms that produce damaging winds, hail, and tornadoes often exhibit distinct cloud-top patterns in satellite imagery. Two of these patterns, overshooting tops (OTs) and above-anvil cirrus plumes (AACPs), are frequently observed over intense storms. Using deep learning techniques, researchers at NASA Langley Research Center developed software to automatically detect these severe storm signatures in 500m GOES satellite data collected every 5 minutes. Emergency managers can use this raster data to quickly identify locations where severe storms likely occurred, complementing ground-based reports in regions with limited observations. Severe weather on the ground is more likely when OTs and AACPs are both detected and in areas where detections are clustered or observed repeatedly. Long, nearly straight lines of OT and AACP time-aggregated detections can indicate severe storm tracks.Suggested Use and Use Cases:Target audiences: GIS specialists, tactical emergency managersInformation Needed: Near real-time situational awareness about locations where severe storm impacts may have occurred. Example Uses: Target additional information collection. Prioritize field activities (e.g. assessments, resource allocation). Value Proposition: Low resolution, low latency data shows areas that may have recently or may soon experience severe weather (nowcasting).Satellite/SensorSatellite: GOES Satellite remote sensing over North & South AmericaResolution: 15 minutes observation frequency over Full-disk, CONUSCaveats:While the presence of OTs and AACPs often correlates with severe weather, this product does not observe ground impacts and may detect features that do not correspond to severe weather. ", "Subject": "Severe Weather Detection Product using Overshooting Tops for the US Severe Storm April 2025 (Experimental).", "Category": "", "AntialiasingMode": "Fast", "TextAntialiasingMode": "Force", "Version": "2.9.0", "Keywords": "Severe weather,troposphere,NASA,NASA Disasters,storm,updraft,tornado" }, "supportsQueryDomains": true, "capabilities": "Query,Map,Data", "supportedQueryFormats": "JSON, geoJSON, PBF", "exportTilesAllowed": false, "referenceScale": 0.0, "supportsDatumTransformation": true, "archivingInfo": {"supportsHistoricMoment": false}, "supportsClipping": true, "supportsSpatialFilter": true, "supportsTimeRelation": true, "supportsQueryDataElements": true, "maxRecordCount": 2000, "maxImageHeight": 4096, "maxImageWidth": 4096, "supportedExtensions": "WMSServer", "serviceItemId": "06e4feb95ba5401d87f8a6fc493eee1e" }