Articles | Volume 23, issue 9
Research article
18 Sep 2023
Research article |  | 18 Sep 2023

Shallow and deep learning of extreme rainfall events from convective atmospheres

Gerd Bürger and Maik Heistermann

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Cited articles

Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., and Farhan, L.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, J. Big Data, 8, 53,, 2021. 
Bianco, S., Cadene, R., Celona, L., and Napoletano, P.: Benchmark Analysis of Representative Deep Neural Network Architectures, IEEE Access, 6, 64270–64277,, 2018. 
Brownlee, J.: Statistical Methods for Machine Learning: Discover how to Transform Data into Knowledge with Python, Machine Learning Mastery, 291 pp., 2018. 
Bürger, G.: Convective Atmospheres: Linking Radar-based Event Descriptors and Losses From Flash Floods (CARLOFFF), Zenodo [code],, 2023. 
BVLC (Berkeley Vision and Learning Center): Caffe, Release 1.0, Zenodo [code], (last access: 11 September 2023), 2017. 
Short summary
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 reanalyzed daily atmospheric fields of convective indices according to CatRaRE, using conventional statistical and more recent machine learning algorithms, and apply them to present and future atmospheres. Increasing trends are projected for CatRaRE-type probabilities, from reanalyzed as well as from simulated atmospheric fields.
Final-revised paper