Thunderstorm Nowcasting With Deep Learning: A Multi‐Hazard Data Fusion Model

Author:

Leinonen Jussi1ORCID,Hamann Ulrich1ORCID,Sideris Ioannis V.1,Germann Urs1

Affiliation:

1. Federal Office of Meteorology and Climatology MeteoSwiss Locarno‐Monti Switzerland

Abstract

AbstractPredictions of thunderstorm‐related hazards are needed in several sectors, including first responders, infrastructure management and aviation. To address this need, we present a deep learning model that can be adapted to different hazard types. The model can utilize multiple data sources; we use data from weather radar, lightning detection, satellite visible/infrared imagery, numerical weather prediction and digital elevation models. We demonstrate the ability of the model to predict lightning, hail and heavy precipitation probabilistically on a 1 km resolution grid, with a temporal resolution of 5 min and lead times up to 60 min. Shapley values quantify the importance of the different data sources, showing that the weather radar products are the most important predictors for all three hazard types.

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Geophysics

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