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Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset

Earth Sciences

Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset

G. Song, S. Li, et al.

This groundbreaking study by Ge Song, Siwei Li, and Jia Xing leverages machine learning to enhance lightning nowcasting accuracy using aerosol features and satellite observations. With a remarkable 94.3% accuracy, the team reveals unexpected influences of different aerosol types on lightning occurrences.

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Playback language: English
Abstract
Accurate lightning nowcasting is crucial for safety and environmental monitoring. This study presents a machine learning approach integrating aerosol features and enriched satellite observations (Geostationary Lightning Mapper, GLM) to improve lightning nowcasting accuracy. A LightGBM model was trained, producing spatially continuous hourly nowcasts over the Contiguous United States (CONUS) during summer. The model achieved high accuracy (94.3%), probability of detection (POD, 75.0%), and a false alarm ratio (FAR, 38.1%), surpassing baselines. Aerosol features significantly enhanced the model, revealing that high aerosol loading (sulfates, organic compounds) enhances lightning, while black carbon inhibits it.
Publisher
npj Climate and Atmospheric Science
Published On
Aug 24, 2023
Authors
Ge Song, Siwei Li, Jia Xing
Tags
lightning nowcasting
machine learning
satellite observations
aerosol features
Geostationary Lightning Mapper
accuracy
environmental monitoring

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