Articles | Volume 29, issue 15
https://doi.org/10.5194/hess-29-3687-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-3687-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Aug 2025
Research article |
| 12 Aug 2025
| 12 Aug 2025
Infilling of missing rainfall radar data with a memory-assisted deep learning approach
Data Analysis Department, German Climate Computing Center (DKRZ), Hamburg, Germany
Laurens M. Bouwer
Climate Service Center Germany (GERICS), Helmholtz-Zentrum Hereon, Hamburg, Germany
Institute of Geography, University of Hamburg, Hamburg, Germany
Frank Kaspar
Hydrometeorology Department, Deutscher Wetterdienst (DWD), Offenbach, Germany
Roman Lehmann
Institute for Technical Informatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Wolfgang Karl
Institute for Technical Informatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Thomas Ludwig
Data Analysis Department, German Climate Computing Center (DKRZ), Hamburg, Germany
Christopher Kadow
Data Analysis Department, German Climate Computing Center (DKRZ), Hamburg, Germany
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Earth Syst. Sci. Data, 17, 2641–2680, https://doi.org/10.5194/essd-17-2641-2025, https://doi.org/10.5194/essd-17-2641-2025, 2025
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Earth Syst. Sci. Data, 16, 2625–2658, https://doi.org/10.5194/essd-16-2625-2024, https://doi.org/10.5194/essd-16-2625-2024, 2024
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Katharina Lengfeld, Paul Voit, Frank Kaspar, and Maik Heistermann
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Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
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In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Lennart Marien, Mahyar Valizadeh, Wolfgang zu Castell, Christine Nam, Diana Rechid, Alexandra Schneider, Christine Meisinger, Jakob Linseisen, Kathrin Wolf, and Laurens M. Bouwer
Nat. Hazards Earth Syst. Sci., 22, 3015–3039, https://doi.org/10.5194/nhess-22-3015-2022, https://doi.org/10.5194/nhess-22-3015-2022, 2022
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Myocardial infarctions (MIs; heart attacks) are influenced by temperature extremes, air pollution, lack of green spaces and ageing population. Here, we apply machine learning (ML) models in order to estimate the influence of various environmental and demographic risk factors. The resulting ML models can accurately reproduce observed annual variability in MI and inter-annual trends. The models allow quantification of the importance of individual factors and can be used to project future risk.
Kevin Sieck, Christine Nam, Laurens M. Bouwer, Diana Rechid, and Daniela Jacob
Earth Syst. Dynam., 12, 457–468, https://doi.org/10.5194/esd-12-457-2021, https://doi.org/10.5194/esd-12-457-2021, 2021
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This paper presents new estimates of future extreme weather in Europe, including extreme heat, extreme rainfall and meteorological drought. These new estimates were achieved by repeating model calculations many times, thereby reducing uncertainties of these rare events at low levels of global warming at 1.5 and 2 °C above
pre-industrial temperature levels. These results are important, as they help to assess which weather extremes could increase at moderate warming levels and where.
Christopher W. Frank, Frank Kaspar, Jan D. Keller, Till Adams, Miriam Felkers, Bernd Fischer, Marcus Handte, Pedro José Marrón, Hinrich Paulsen, Markus Neteler, Jochen Schiewe, Marvin Schuchert, Christian Nickel, Richard Wacker, and Richard Figura
Adv. Sci. Res., 17, 183–190, https://doi.org/10.5194/asr-17-183-2020, https://doi.org/10.5194/asr-17-183-2020, 2020
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Access to high quality weather and climate data is crucial for a wide range of societal and economic issues. It allows optimising industrial processes with respect to efficiency. The goal of the research project FAIR is to simplify the information exchange between the DWD and economical players. This paper summarizes the results of the requirement analysis of three use cases and presents the deduced technical architecture and FAIR services aiming at a user-friendly exchange of weather data.
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Short summary
Our study focuses on filling in missing precipitation data using an advanced neural network model. Traditional methods for estimating missing climate information often struggle in large regions where data are scarce. Our solution, which incorporates recent advances in machine learning, captures the intricate patterns of precipitation over time, especially during extreme weather events. Our model shows good performance in reconstructing large regions of missing rainfall radar data.
Our study focuses on filling in missing precipitation data using an advanced neural network...
