Severe Storm Prediction via Overshooting Cloud Top and Above Anvil Cirrus Plume Image Recognition from Satellite Imager Data
Description
Overshooting Cloud Tops (OTs) Above-Anvil Cirrus Plumes (AACPs) are indicators of especially intense thunderstorm updrafts that are precursors to severe weather including damaging winds, hail, tornadoes, lightning, flooding rainfall, and aviation weather hazards. This project involves training machine learning image recognition techniques to identify OTs AACPs in multispectral satellite imagery (visible, infrared, and lightning imaging) for storm warning. Such warnings are especially valuable in regions without near real time weather radar networks or during radar outages. Applying these methods to long-term satellite data records enables the community to assess severe storm risk, which is needed by the insurance and reinsurance industries
Detailed example
Outputs include the likelihood of OT and AACPs at the satellite pixel scale (e.g. 2 km pixel size), and metrics quantifying storm intensity based on cloud top temperature patterns.
AI / analytics pattern
Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
A NASA open source software tool has been made publicly available to enable researchers and forecasters to apply the tool in near real time and for archived satellite imagery. This is the first tool of its kind that can be used for satellite-based severe storm detection https://github.com/nasa/svrstormsig
Controls / human review
ATO: Not reported; PIA: Not published