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Preprints
https://doi.org/10.5194/egusphere-2022-1159
https://doi.org/10.5194/egusphere-2022-1159
07 Nov 2022
 | 07 Nov 2022

Deep learning of extreme rainfall events from convective atmospheres

Gerd Bürger and Maik Heistermann

Abstract. Our subject is a new Catalogue of radar-based heavy Rainfall Events (CatRaRE) over Germany, and how it relates to the concurrent atmospheric circulation. We classify daily atmospheric ERA5 fields of convective indices according to CatRaRE, using an array of conventional statistical and more recent machine learning (ML) algorithms, and apply them to corresponding fields of simulated present and future atmospheres from the CORDEX project. Due to the stochastic nature of ML optimization there is some spread in the results. The ALL-CNN network performs best on average, with several learning runs exceeding an Equitable Threat Score (ETS) of 0.52; the single best result was from ResNet with ETS = 0.54. The best performing classical scheme was a Random Forest with ETS = 0.51. Regardless of the method, increasing trends are predicted for the probability of CatRaRE-type events, from ERA5 as well as from the CORDEX fields.

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Journal article(s) based on this preprint

18 Sep 2023
Shallow and deep learning of extreme rainfall events from convective atmospheres
Gerd Bürger and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 3065–3077, https://doi.org/10.5194/nhess-23-3065-2023,https://doi.org/10.5194/nhess-23-3065-2023, 2023
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Short summary
Our subject is a new Catalogue of radar-based heavy Rainfall Events (CatRaRE) over Germany, and...
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