ProbSevere LightningCast: A Deep-Learning Model for Satellite-Based Lightning Nowcasting

John L. Cintineo, Michael J. Pavolonis, Justin M. Sieglaff

Weather and Forecasting 2022 article cited by 30

Source Open-access PDF More in Climate & Environment

importance 6/10 ai assisted Lightning and Electromagnetic Phenomena weather forecasting stage: research

Problem

Lightning strikes threaten life and property but are difficult to forecast in a timely, location-specific manner.

Why it matters

Timely next-hour lightning guidance can protect people and property from a lethal hazard.

What the AI did

A convolutional neural network for semantic segmentation was trained on GOES-16 satellite imagery to give probabilistic next-hour lightning predictions.

Results

Model shown to generalize to GOES-16 full disk regions outside its CONUS training domain; numeric skill metrics not stated in source.

Impact

Evidence supports a working satellite-based nowcasting model that generalizes beyond its training domain; operational adoption not stated.

Record

Venue	Weather and Forecasting
Published	2022
Work type	article
Authors	John L. Cintineo, Michael J. Pavolonis, Justin M. Sieglaff
Citations	30 (OpenAlex, at import time)
References	58
Model or method	Convolutional neural network (semantic segmentation); LightningCast
Organization	not stated in source
Primary source	https://doi.org/10.1175/waf-d-22-0019.1
Open access	bronze
Keywords	lightning nowcasting, deep learning, GOES-16, satellite imagery, convolutional neural network

Importance rating

6 / 10

significant — thousands to millions affected

Lightning is a lethal hazard to human life and property; satellite nowcasting with 20+ min lead time offers clear public-safety benefit, though no scale quantified.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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