1153: DYAMOND

Principal investigator: Daniel Klocke

Deutscher Wetterdienst (Community project)

Project abstract

Report 1/2020 to 12/2020
Report 1/2021 to 12/2021
Report 1/2022 to 12/2022
Report 1/2023 to 12/2023
Report 1/2024 to 12/2024
Report 1/2025 to 12/2025
Report 1/2026 to 12/2026

Publications

• DOI: 10.5194/egusphere-2025-4969, Dolores-Tesillos et al., 2025
• DOI: 10.48550/arXiv.2604.25447, van Heerwaarden, C.C., Veerman, M.A., Benedict, I., Brunner, L., Dolores-Tesillos, E., Dutra, E., Fischer, E., Lee, J., Martius, O., Pedruzo-Bagazgoitia, X. and Proske, U., 2026. Evaluating local climate in global storm-resolving models with the K\" oppen-Geiger classification. arXiv preprint arXiv:2604.25447.
• DOI: 10.48620/100325, Veerman, M., Benedict, I., Brunner, L., Dolores Tesillos, E., Dutra, E., Fischer, E., Lee, J., Martius, O., Proske, U., Warnau, S. and Wille, J., 2026. Evaluating local climate in global storm-resolving models with the Köppen-Geiger classification.
• DOI: 10.2151/jmsj.2024-028, Hans Segura, Cathy Hohenegger, How Do the Tropics Precipitate? Daily Variations in Precipitation and Cloud Distribution, Journal of the Meteorological Society of Japan. Ser. II, 2024, Volume 102, Issue 5, Pages 525-537, Released on J-STAGE July 23, 2024, Advance online publication May 28, 2024, Online ISSN 2186-9057, Print ISSN 0026-1165, https://doi.org/10.2151/jmsj.2024-028, https://www.jstage.jst.go.jp/article/jmsj/102/5/102_2024-028/_article/-char/en
• DOI: 10.1029/2024GL108856, Spät, D., Biasutti, M., Schuhbauer, D., & Voigt, A. (2024). Autocorrelation—A simple diagnostic for tropical precipitation variability in global kilometer-scale climate models. Geophysical Research Letters, 51, e2024GL108856. https://doi.org/10.1029/2024GL108856
• DOI: 10.22541/essoar.176555751.17883296, Pedruzo-Bagazgoitia, X., Sutzl, B., Dutra, E., McNorton, J.R., Tsiringakis, A. and Rüdiger, C., 2025. Unlocking Urban Climate Change Analysis in Global Kilometre-Scale Climate Simulations.
• DOI: 10.1088/1748-9326/ae9201/meta, Contizanetti, M., Uribe, A., Mauritsen, T. and Bender, F., 2026. Sensitivity of extreme precipitation to warming in a global storm-resolving model. Environmental Research Letters, 21(15), p.154037.
• DOI: 10.1029/2025GL119706, Dragaud, I.C., Vogel, R., Mellado, J.P. and Becker, T., 2026. Simulating shallow mesoscale overturning circulations in the trades with kilometer‐scale Earth system models. Geophysical Research Letters, 53(9), p.e2025GL119706.
• DOI: 10.1029/2025JD044845, Gutjahr, O., Jungclaus, J.H., Brüggemann, N., Haak, H. and Marotzke, J., 2022. Air‐sea interactions and water mass transformation during a katabatic storm in the irminger sea. Journal of Geophysical Research: Oceans, 127(5), p.e2021JC018075.
• DOI: 10.5194/wcd-7-1089-2026, Dolores-Tesillos, E., Martius, O. and Pfahl, S., 2026. Atmospheric blocking representation in storm-resolving climate models under historical and future forcing. Weather and Climate Dynamics, 7(3), pp.1089-1116.
• DOI: 10.1029/2024AV001267, Poujol, B., & Bony, S. (2024). Measuring clear-air vertical motions from space. AGU Advances, 5, e2024AV001267. https://doi.org/10.1029/2024AV001267
• DOI: 10.1029/2025GL120583, Pedruzo‐Bagazgoitia, X., Sützl, B., Dutra, E., McNorton, J., Tsiringakis, A. and Rüdiger, C., 2026. Unlocking urban climate change analysis in global kilometer‐scale climate simulations. Geophysical Research Letters, 53(13), p.e2025GL120583.
• DOI: 10.1007/s00382-026-08235-8, Uribe, A., Contizanetti, M., Bender, F.A.M. and Mauritsen, T., 2026. Icon simulations suggest model-dependent cloud feedbacks in global storm-resolving models. Climate Dynamics, 64(6), p.281.
• DOI: 10.22541/essoar.176072359.99907733/v1, Dragaud et al., 2025
• DOI: 10.1029/2022GL101796, Segura et al., 2022
• DOI: 10.1029/2024GL110124, Freischem, L. J., Weiss, P., Christensen, H. M., & Stier, P. (2024). Multifractal analysis for evaluating the representation of clouds in global kilometer-scale models. Geophysical Research Letters, 51, e2024GL110124. https://doi.org/10.1029/2024GL110124
• DOI: 10.2151/sola.2025-056, Shao-Yu Tseng, Wei-Ting Chen, Chien-Ming Wu, Evaluating Precipitation Enhancement Associated with Convective Mergers in the Global Storm-resolving Models of DYAMOND Summer Phase, SOLA, Article ID 2025-056, Advance online publication September 29, 2025, Online ISSN 1349-6476, https://doi.org/10.2151/sola.2025-056, https://www.jstage.jst.go.jp/article/sola/advpub/0/advpub_2025-056/_article/-char/en
• DOI: 10.1029/2024JD042204, Feng, Z., Prein, A. F., Kukulies, J., Fiolleau, T., Jones, W. K., Maybee, B., et al. (2025). Mesoscale convective systems tracking method intercomparison (MCSMIP): Application to DYAMOND global km-scale simulations. Journal of Geophysical Research: Atmospheres, 130, e2024JD042204. https://doi.org/10.1029/2024JD042204
• DOI: 10.1029/2024EA003884, Turbeville, S. M., Ackerman, T. P., & Blossey, P. N. (2025). Tropical cirrus in global-storm resolving models: 3. seasonal changes and TTL cirrus in the Tropical Western Pacific using DYAMOND. Earth and Space Science, 12, e2024EA003884. https://doi.org/10.1029/2024EA003884
• DOI: 10.1029/2024EA003887, Nugent, J. M., Bretherton, C. S., & Blossey, P. N. (2025). What sets the tropical cold point in GSRMs during boreal winter? Overshooting convection versus cirrus lofting. Earth and Space Science, 12, e2024EA003887. https://doi.org/10.1029/2024EA003887
• DOI: 10.1038/s41612-025-01154-1, Abramian, S., Muller, C., Risi, C. et al. How key features of early development shape deep convective systems. npj Clim Atmos Sci 8, 258 (2025). https://doi.org/10.1038/s41612-025-01154-1
• DOI: 10.1175/JAS-D-24-0174.1, Sullivan, S. C., P. Vautravers, T. Beucler, T. Makgoale, and J. Yin, 2025: Moisture–Precipitation Couplings for Mesoscale Convective Systems in Tracking Data and Idealized Simulations. J. Atmos. Sci., 82, 1885–1902, https://doi.org/10.1175/JAS-D-24-0174.1.
• DOI: 10.1029/2024MS004467, Abbott, T. H., Jeevanjee, N., Cheng, K.-Y., Zhou, L., & Harris, L. (2025). The land-ocean contrast in deep convective intensity in a global storm-resolving model. Journal of Advances in Modeling Earth Systems, 17, e2024MS004467. https://doi.org/10.1029/2024MS004467
• DOI: 10.1029/2024GL113937, Poujol, B., Lee, J., Rackow, T., Rotach, M. W., & Ban, N. (2025). Are the largest benefits of kilometer-scale climate models over mountains or over flatland? Geophysical Research Letters, 52, e2024GL113937. https://doi.org/10.1029/2024GL113937
• DOI: 10.1073/pnas.2314265121, Lee, J. and C. Hohenegger (2024). “Weaker land–atmosphere coupling in global storm-resolving simula- tion”. In: Proceedings of the National Academy of Sciences 121.12, e2314265121. DOI: 10.1073/ pnas.2314265121
• DOI: 10.5194/egusphere-2024-2724, Lenhardt, J., Quaas, J., Sejdinovic, D., and Klocke, D.: CloudViT: classifying cloud types in global satellite data and in kilometre-resolution simulations using vision transformers, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-2724, 2024.
• DOI: 10.1007/s00382-024-07355-3, Respati, M.R., Dommenget, D., Segura, H. et al. Diagnosing drivers of tropical precipitation biases in coupled climate model simulations. Clim Dyn 62, 8691–8709 (2024). https://doi.org/10.1007/s00382-024-07355-3
• DOI: 10.2151/jmsj.2024-028, Hans Segura, Cathy Hohenegger, How Do the Tropics Precipitate? Daily Variations in Precipitation and Cloud Distribution, Journal of the Meteorological Society of Japan. Ser. II, 2024, Volume 102, Issue 5, Pages 525-537, Released on J-STAGE July 23, 2024, Advance online publication May 28, 2024, Online ISSN 2186-9057, Print ISSN 0026-1165, https://doi.org/10.2151/jmsj.2024-028, https://www.jstage.jst.go.jp/article/jmsj/102/5/102_2024-028/_article/-char/en
• DOI: 10.1029/2023MS003763, @article{https://doi.org/10.1029/2023MS003763, author = {Grundner, Arthur and Beucler, Tom and Gentine, Pierre and Eyring, Veronika}, title = {Data-Driven Equation Discovery of a Cloud Cover Parameterization}, journal = {Journal of Advances in Modeling Earth Systems}, volume = {16}, number = {3}, pages = {e2023MS003763}, keywords = {symbolic regression, cloud fraction, cloud cover, parameterization, Pareto frontier}, doi = {https://doi.org/10.1029/2023MS003763}, url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023MS003763}, eprint = {https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2023MS003763}, note = {e2023MS003763 2023MS003763}, abstract = {Abstract A promising method for improving the representation of clouds in climate models, and hence climate projections, is to develop machine learning-based parameterizations using output from global storm-resolving models. While neural networks (NNs) can achieve state-of-the-art performance within their training distribution, they can make unreliable predictions outside of it. Additionally, they often require post-hoc tools for interpretation. To avoid these limitations, we combine symbolic regression, sequential feature selection, and physical constraints in a hierarchical modeling framework. This framework allows us to discover new equations diagnosing cloud cover from coarse-grained variables of global storm-resolving model simulations. These analytical equations are interpretable by construction and easily transferable to other grids or climate models. Our best equation balances performance and complexity, achieving a performance comparable to that of NNs (R2 = 0.94) while remaining simple (with only 11 trainable parameters). It reproduces cloud cover distributions more accurately than the Xu-Randall scheme across all cloud regimes (Hellinger distances < 0.09), and matches NNs in condensate-rich regimes. When applied and fine-tuned to the ERA5 reanalysis, the equation exhibits superior transferability to new data compared to all other optimal cloud cover schemes. Our findings demonstrate the effectiveness of symbolic regression in discovering interpretable, physically-consistent, and nonlinear equations to parameterize cloud cover.}, year = {2024} }
• DOI: 10.1029/2024AV001267, Poujol, B., & Bony, S. (2024). Measuring clear-air vertical motions from space. AGU Advances, 5, e2024AV001267. https://doi.org/10.1029/2024AV001267
• DOI: 10.1029/2023GL105083, Nugent, J. M., & Bretherton, C. S. (2023). Tropical convection overshoots the cold point tropopause nearly as often over warm oceans as over land. Geophysical Research Letters, 50, e2023GL105083. https://doi.org/10.1029/2023GL105083
• DOI: 10.1029/2024GL110124, Freischem, L. J., Weiss, P., Christensen, H. M., & Stier, P. (2024). Multifractal analysis for evaluating the representation of clouds in global kilometer-scale models. Geophysical Research Letters, 51, e2024GL110124. https://doi.org/10.1029/2024GL110124
• DOI: 10.1007/s00024-024-03467-3, Listowski, C., Stephan, C.C., Le Pichon, A. et al. Stratospheric Gravity Waves Impact on Infrasound Transmission Losses Across the International Monitoring System. Pure Appl. Geophys. (2024). https://doi.org/10.1007/s00024-024-03467-3
• DOI: 10.22541/essoar.172405869.95851202/v1, Jacqueline M Nugent, Christopher S. Bretherton, Peter N. Blossey. What sets the tropical cold point in GSRMs? Overshooting convection vs. cirrus lofting. ESS Open Archive . August 19, 2024.
• DOI: 10.22541/essoar.172021737.79807737/v1, Tristan H Abbott, Nadir Jeevanjee, Kai-Yuan Cheng, et al. The Land-Ocean Contrast in Deep Convective Intensity in a Global Storm-Resolving Model. ESS Open Archive . July 05, 2024.
• DOI: 10.1029/2024GL108856, Spät, D., Biasutti, M., Schuhbauer, D., & Voigt, A. (2024). Autocorrelation—A simple diagnostic for tropical precipitation variability in global kilometer-scale climate models. Geophysical Research Letters, 51, e2024GL108856. https://doi.org/10.1029/2024GL108856
• DOI: 10.1029/2023JC020258, Gutjahr, O. and C. Mehlmann (2024). “Polar Lows and Their Effects on Sea Ice and the Upper Ocean in the Iceland, Greenland, and Labrador Seas”. In: Journal of Geophysical Research: Oceans 129.7. e2023JC020258 2023JC020258, e2023JC020258. https://doi.org/10.1029/2023JC020258.
• DOI: https: //nextgems-h2020.eu/publications/, A list of NextGEMS publications is under the link above
• DOI: https://www.esiwace.eu/the-project/past-phases/dyamond-initiative/dyamond-related-publications, A list of DYAMOND publications is under the link above
• Publications are listed here: https://www.esiwace.eu/services/dyamond-initiative/dyamond-related-publications There was a special issue in JMSJ and in total more than 30 publications were published up to now.
• DOI: 10.2151/jmsj.2021-062, Heim, C., L. Hentgen, N. Ban, and C. Schär, 2021: Inter-model variability in convection-resolving simulations of subtropical marine low clouds. J. Meteor. Soc. Japan, 99, Special Edition on DYAMOND: The DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains https://doi.org/10.2151/jmsj.2021-062.
• DOI: 10.2151/jmsj.2020-005, Hohenegger, C., Kornblueh, L., Klocke, D., Becker, T., Cioni, G., Engels, J. F., Schulzweida, U. and Stevens, B.: Climate Statistics in Global Simulations of the Atmosphere, from 80 to 2.5 km Grid Spacing, Journal of the Meteorological Society of Japan. Ser. II, 98(1), 73&ndash;91, doi:10.2151/jmsj.2020-005, 2020.
• DOI: arXiv:2108.08565, Christensen, H. M., Driver, O. G. A.: The Fractal Nature of Clouds in Global Storm-Resolving Models https://arxiv.org/abs/2108.08565
• DOI: 10.2151/jmsj.2021-029, Judt, F., Klocke, D., Rios-Berrios, R., Vanniere, B., Ziemen, F., Auger, L., Biercamp, J., Bretherton, C., Chen, X., Düben, P., Hohenegger, C., Khairoutdinov, M., Kodama, C., Kornblueh, L., Lin, S.-J., Nakano, M., Neumann, P., Putman, W., Röber, N., Roberts, M., Satoh, M., Shibuya, R., Stevens, B., Vidale, P. L., Wedi, N., & Zhou, L. (2021). Tropical Cyclones in Global Storm-Resolving Models. Journal of the Meteorological Society of Japan. Ser. II, 99(3), 579–602. https://doi.org/10.2151/jmsj.2021-029