| Can deep learning beat numerical weather prediction? MG Schultz, C Betancourt, B Gong, F Kleinert, M Langguth, LH Leufen, ... Philosophical Transactions of the Royal Society A 379 (2194), 20200097, 2021 | 305 | 2021 |
| Temperature forecasting by deep learning methods B Gong, M Langguth, Y Ji, A Mozaffari, S Stadtler, K Mache, MG Schultz Geoscientific model development 15 (23), 8931-8956, 2022 | 16 | 2022 |
| Juwels booster–a supercomputer for large-scale ai research S Kesselheim, A Herten, K Krajsek, J Ebert, J Jitsev, M Cherti, M Langguth, ... High Performance Computing: ISC High Performance Digital 2021 International …, 2021 | 15 | 2021 |
| Can deep learning beat numerical weather prediction?, Philos MG Schultz, C Betancourt, B Gong, F Kleinert, M Langguth, LH Leufen, ... Roy. Soc. A 379 (20200097), 10.1098, 2021 | 13 | 2021 |
| Can deep learning beat numerical weather prediction?, Philos. T. Roy. Soc. A, 379, 20200097 MG Schultz, C Betancourt, B Gong, F Kleinert, M Langguth, LH Leufen, ... | 8 | 2021 |
| AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning C Lessig, I Luise, B Gong, M Langguth, S Stadler, M Schultz arXiv preprint arXiv:2308.13280, 2023 | 6 | 2023 |
| Deep learning models for generation of precipitation maps based on numerical weather prediction A Rojas-Campos, M Langguth, M Wittenbrink, G Pipa Geoscientific Model Development 16 (5), 1467-1480, 2023 | 4 | 2023 |
| HPC-oriented canonical workflows for machine learning applications in climate and weather prediction A Mozaffari, M Langguth, B Gong, J Ahring, AR Campos, P Nieters, ... Data Intelligence 4 (2), 271-285, 2022 | 4 | 2022 |
| CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting Y Ji, B Gong, M Langguth, A Mozaffari, X Zhi Geoscientific Model Development 16 (10), 2737-2752, 2023 | 3 | 2023 |
| CLGAN: A GAN-based video prediction model for precipitation nowcasting Y Ji, B Gong, M Langguth, A Mozaffari, X Zhi EGUsphere, 1-23, 2022 | 3 | 2022 |
| Implementing the HYbrid MAss flux Convection Scheme (HYMACS) in ICON–First idealized tests and adaptions to the dynamical core for local mass sources M Langguth, V Kuell, A Bott Quarterly Journal of the Royal Meteorological Society 146 (731), 2689-2716, 2020 | 2 | 2020 |
| Deep learning models for generation of precipitation maps based on Numerical Weather Prediction A Rojas-Campos, M Langguth, M Wittenbrink, G Pipa EGUsphere 2022, 1-20, 2022 | 1 | 2022 |
| Applying the DestinE Extremes digital twin to air quality forecasts and emission scenario simulations AC Lange, S Schröder, P Franke, M Langguth, E Friese, MG Schultz EGU24, 2024 | | 2024 |
| AtmoRep: large scale representation learning for atmospheric dynamics I Luise, C Lessig, M Schultz, M Langguth EGU24, 2024 | | 2024 |
| Downscaling with the foundation model AtmoRep M Langguth, C Lessig, M Schultz, I Luise EGU24, 2024 | | 2024 |
| A Benchmark Dataset for Statistical Downscaling of Meteorological Fields with Deep Neural Networks M Langguth, S Stadtler, B Gong, MG Schultz 104th AMS Annual Meeting, 2024 | | 2024 |
| arXiv: AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning C Lessig, M Schultz, B Gong, S Stadtler, M Langguth, I Luise | | 2023 |
| Statistical downscaling of precipitation with deep neural networks B Gong, Y Ji, M Langguth, M Schultz EGU General Assembly Conference Abstracts, EGU-10488, 2023 | | 2023 |
| Towards a benchmark dataset for statistical downscaling of meteorological fields M Langguth, B Gong, Y Ji, MG Schultz, O Stein EGU General Assembly Conference Abstracts, EGU-11489, 2023 | | 2023 |
| Representation of deep convection at gray-zone resolutions-Implementing and testing the HYbrid MAss flux Convection Scheme (HYMACS) in the ICON model M Langguth Universitäts-und Landesbibliothek Bonn, 2022 | | 2022 |
