| WeatherBench: a benchmark data set for data‐driven weather forecasting S Rasp, PD Dueben, S Scher, JA Weyn, S Mouatadid, N Thuerey Journal of Advances in Modeling Earth Systems 12 (11), e2020MS002203, 2020 | 769 | 2020 |
| DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains B Stevens, M Satoh, L Auger, J Biercamp, CS Bretherton, X Chen, ... Progress in Earth and Planetary Science 6 (1), 1-17, 2019 | 590 | 2019 |
| Neural general circulation models for weather and climate D Kochkov, J Yuval, I Langmore, P Norgaard, J Smith, G Mooers, ... Nature 632 (8027), 1060-1066, 2024 | 488 | 2024 |
| Challenges and design choices for global weather and climate models based on machine learning PD Dueben, P Bauer Geoscientific Model Development 11 (10), 3999-4009, 2018 | 385 | 2018 |
| Global cloud-resolving models M Satoh, B Stevens, F Judt, M Khairoutdinov, SJ Lin, WM Putman, ... Current Climate Change Reports 5 (3), 172-184, 2019 | 329 | 2019 |
| WeatherBench 2: A benchmark for the next generation of data‐driven global weather models S Rasp, S Hoyer, A Merose, I Langmore, P Battaglia, T Russell, ... Journal of Advances in Modeling Earth Systems 16 (6), e2023MS004019, 2024 | 295 | 2024 |
| The digital revolution of Earth-system science P Bauer, PD Dueben, T Hoefler, T Quintino, TC Schulthess, NP Wedi Nature Computational Science 1 (2), 104-113, 2021 | 277 | 2021 |
| The rise of data-driven weather forecasting: A first statistical assessment of machine learning–based weather forecasts in an operational-like context Z Ben Bouallegue, MCA Clare, L Magnusson, E Gascon, M Maier-Gerber, ... Bulletin of the American Meteorological Society 105 (6), E864-E883, 2024 | 272 | 2024 |
| Deep learning for post-processing ensemble weather forecasts P Grönquist, C Yao, T Ben-Nun, N Dryden, P Dueben, S Li, T Hoefler Philosophical Transactions of the Royal Society A 379 (2194), 20200092, 2021 | 233 | 2021 |
| Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI M Chantry, H Christensen, P Dueben, T Palmer Philosophical Transactions of the Royal Society A 379 (2194), 20200083, 2021 | 208 | 2021 |
| A generative deep learning approach to stochastic downscaling of precipitation forecasts L Harris, ATT McRae, M Chantry, PD Dueben, TN Palmer Journal of Advances in Modeling Earth Systems 14 (10), e2022MS003120, 2022 | 195 | 2022 |
| A longer and healthier life with TOR down-regulation: genetics and drugs I Bjedov, L Partridge Biochemical Society Transactions 39 (2), 460-465, 2011 | 195* | 2011 |
| AIFS--ECMWF's data-driven forecasting system S Lang, M Alexe, M Chantry, J Dramsch, F Pinault, B Raoult, MCA Clare, ... arXiv preprint arXiv:2406.01465, 2024 | 183 | 2024 |
| Bridging observations, theory and numerical simulation of the ocean using machine learning M Sonnewald, R Lguensat, DC Jones, PD Dueben, J Brajard, V Balaji Environmental Research Letters 16 (7), 073008, 2021 | 179 | 2021 |
| A baseline for global weather and climate simulations at 1 km resolution NP Wedi, I Polichtchouk, P Dueben, VG Anantharaj, P Bauer, S Boussetta, ... Journal of Advances in Modeling Earth Systems 12 (11), e2020MS002192, 2020 | 135 | 2020 |
| Single precision in weather forecasting models: An evaluation with the IFS F Váňa, P Düben, S Lang, T Palmer, M Leutbecher, D Salmond, G Carver Monthly Weather Review 145 (2), 495-502, 2017 | 125 | 2017 |
| Machine learning emulation of gravity wave drag in numerical weather forecasting M Chantry, S Hatfield, P Dueben, I Polichtchouk, T Palmer Journal of Advances in Modeling Earth Systems 13 (7), e2021MS002477, 2021 | 124 | 2021 |
| TRU-NET: a deep learning approach to high resolution prediction of rainfall RA Adewoyin, P Dueben, P Watson, Y He, R Dutta Machine Learning 110 (8), 2035-2062, 2021 | 110 | 2021 |
| Challenges and benchmark datasets for machine learning in the atmospheric sciences: Definition, status, and outlook PD Dueben, MG Schultz, M Chantry, DJ Gagne, DM Hall, A McGovern Artificial Intelligence for the Earth Systems 1 (3), e210002, 2022 | 92 | 2022 |
| Building tangent‐linear and adjoint models for data assimilation with neural networks S Hatfield, M Chantry, P Dueben, P Lopez, A Geer, T Palmer Journal of Advances in Modeling Earth Systems 13 (9), e2021MS002521, 2021 | 90 | 2021 |
