NASA · NASA-349 · record R0211

Severe Storm Prediction via Overshooting Cloud Top and Above Anvil Cirrus Plume Image Recognition from Satellite Imager Data

National Aeronautics and Space Administration · LaRC: Langley Research Center

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: Image and video analysis Derived by keyword rules; see methodology

Problem the AI is intended to solve

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

Expected benefits

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

System outputs

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.

Technology

AI classification: Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported