EGU24-10298, updated on 08 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Downscaling precipitation simulations from Earth system models with generative deep learning

Philipp Hess1,2, Maximilian Gelbrecht1,2, Michael Aich1, Baoxiang Pan3, Sebastian Bathiany1,2, and Niklas Boers1,2,4
Philipp Hess et al.
  • 1Technical University of Munich, School of Engineering & Design, Earth system modelling, Germany
  • 2Potsdam Institute for Climate Impact Research, Potsdam, Germany
  • 3Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 4Global Systems Institute and Department of Mathematics, University of Exeter, Exeter, UK

Accurately assessing precipitation impacts due to anthropogenic global warming relies on numerical Earth system model (ESM) simulations. However, the discretized formulation of ESMs, where unresolved small-scale processes are included as semi-empirical parameterizations, can introduce systematic errors in the simulations. These can, for example, lead to an underestimation of spatial intermittency and extreme events.
 Generative deep learning has recently been shown to skillfully bias-correct and downscale precipitation fields from numerical simulations [1,2]. Using spatial context, these methods can jointly correct spatial patterns and summary statistics, outperforming established statistical approaches.
However, these approaches require separate training for each Earth system model individually, making corrections of large ESM ensembles computationally costly. Moreover, they only allow for limited control over the spatial scale at which biases are corrected and may suffer from training instabilities.
Here, we follow a novel diffusion-based generative approach [3, 4] by training an unconditional foundation model on the high-resolution target ERA5 dataset only. Using fully coupled ESM simulations of precipitation, we investigate the controllability of the generative process during inference to preserve spatial patterns of a given ESM field on different spatial scales.

[1] Hess, P., Drüke, M., Petri, S., Strnad, F. M., & Boers, N. (2022). Physically constrained generative adversarial networks for improving precipitation fields from Earth system models. Nature Machine Intelligence, 4(10), 828-839.

[2] Harris, L., McRae, A. T., Chantry, M., Dueben, P. D., & Palmer, T. N. (2022).A generative deep learning approach to stochastic downscaling of precipitation forecasts. Journal of Advances in Modeling Earth Systems, 14(10), e2022MS003120.

[3] Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J. Y., & Ermon, S. (2021).  Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073.

[4] Bischoff, T., & Deck, K. (2023). Unpaired Downscaling of Fluid Flows with Diffusion Bridges. arXiv preprint arXiv:2305.01822.

How to cite: Hess, P., Gelbrecht, M., Aich, M., Pan, B., Bathiany, S., and Boers, N.: Downscaling precipitation simulations from Earth system models with generative deep learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10298,, 2024.