| snippet:
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Severe Weather Detection Product using Overshooting Tops for the US Severe Weather April 2025 (Experimental). |
| summary:
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Severe Weather Detection Product using Overshooting Tops for the US Severe Weather April 2025 (Experimental). |
| accessInformation:
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NASA; Jack Cooney, Kris Bedka |
| thumbnail:
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| maxScale:
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1155581.1085775574 |
| typeKeywords:
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["ArcGIS Server","Data","Image Service","Service"] |
| description:
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<div style='text-align:Left;'><div><div><p><span>Note: this data is currently in testing and is experimental.</span></p><p><span style='font-weight:bold;'>Date of Images:</span></p><p><span>Post-Event: 4/2/2025-4/6/2025</span></p><p><span /></p><p><span style='font-weight:bold;'>Summary:</span></p><p><span>Storms that produce heavy rain, 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><p><span>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><p><span /></p><p><span style='font-weight:bold;'>Suggested Use and Use Cases:</span></p><p><span>Target audiences: GIS specialists, tactical emergency managers</span></p><p><span>Information Needed: Near real-time situational awareness about locations where severe storm impacts may have occurred.</span></p><p><span>Example Uses: Target additional information collection. Prioritize field activities (e.g. assessments, resource allocation).</span></p><p><span>Value Proposition: Low resolution, low latency data shows areas that may have recently or may soon experience severe weather (nowcasting).</span></p><p><span /></p><p><span style='font-weight:bold;'>Satellite:</span></p><p><span>Geostationary Operational Environmental Satellite (GOES) Satellite remote sensing over North & South America</span></p><p><span /></p><p><span style='font-weight:bold;'>Resolution: </span><span /></p><p><span>5 minute temporal resolution observation frequency, 500m spatial resolution</span></p><p><span /></p><p><span style='font-weight:bold;'>Product Specifications:</span></p><p><span /></p><p><span /></p><p><span style='font-weight:bold;'>Caveats:</span></p><p><span>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><p><span /></p><p><span style='font-weight:bold;'>Service URL:</span></p><p><p><span style='font-weight:bold;'>WMS Endpoint:</span></p><p><span /></p><p><span /></p></div></div></div></p> |
| licenseInfo:
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<div style='text-align:Left;'><div><div><p><span>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><p><span>If you use this software in your project, please contact our team prior to publication and cite our contribution. When using this software, credit the technical background and validation for this software as described here:</span></p><p><span>Cooney, J. W., Bedka, K. M., Liles, C. A., and Homeyer, C. R. (in review). Automated Detection of Overshooting Tops and Above Anvil Cirrus Plumes Within Geostationary Imagery Using Deep Learning. Artificial Intelligence for the Earth Systems.</span></p><p><span>More information can be found at:</span></p><p><a href='https://github.com/nasa/svrstormsig' target='_blank' style='text-decoration:underline;'><span>https://github.com/nasa/svrstormsig</span></a></p></div></div></div> |
| catalogPath:
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| title:
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GOES_SEVEREWX |
| type:
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Image Service |
| url:
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https://gis.earthdata.nasa.gov/gis05 |
| tags:
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["Severe weather","troposphere","NASA","NASA Disasters","storm","updraft","tornado"] |
| culture:
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en-US |
| portalUrl:
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| name:
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GOES_SEVEREWX |
| guid:
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| minScale:
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7.395719094896367E7 |
| spatialReference:
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GCS_WGS_1984 |