EGU General Assembly 2021
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Inferring precipitation from atmospheric general circulation model variables

Philipp Hess1,2 and Niklas Boers1,2,3
Philipp Hess and Niklas Boers
  • 1Free University of Berlin, Mathematics, Department of Mathematics and Computer Science, Germany
  • 2Potsdam Institute for Climate Impact Research, Potsdam, Germany
  • 3Global Systems Institute and Department of Mathematics, University of Exeter, Exeter, UK

The accurate prediction of precipitation, in particular of extremes, remains a challenge for numerical weather prediction (NWP) models. A large source of error are subgrid-scale parameterizations of processes that play a crucial role in the complex, multi-scale dynamics of precipitation, but are not explicitly resolved in the model formulation. Recent progress in purely data-driven deep learning for regional precipitation nowcasting [1] and global medium-range forecasting [2] tasks has shown competitive results to traditional NWP models.
Here we follow a hybrid approach, in which explicitly resolved atmospheric variables are forecast in time by a general circulation model (GCM) ensemble and then mapped to precipitation using a deep convolutional autoencoder. A frequency-based weighting of the loss function is introduced to improve the learning with regard to extreme values.
Our method is validated against a state-of-the-art GCM ensemble using three-hourly high resolution data. The results show an improved representation of extreme precipitation frequencies, as well as comparable error and correlation statistics.

[1] C.K. Sønderby et al. "MetNet: A Neural Weather Model for Precipitation Forecasting." arXiv preprint arXiv:2003.12140 (2020). 
[2] S. Rasp and N. Thuerey "Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution." arXiv preprint arXiv:2008.08626 (2020).

How to cite: Hess, P. and Boers, N.: Inferring precipitation from atmospheric general circulation model variables, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-678,, 2021.


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