Improving deep learning precipitation nowcasting by using prior knowledge

Matej Choma; Petr Šimánek; Jakub Bartel

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arXiv · 2301.11707

Improving deep learning precipitation nowcasting by using prior knowledge

Matej Choma·Petr Šimánek·Jakub Bartel

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Abstract

Deep learning methods dominate short-term high-resolution precipitation nowcasting in terms of prediction error. However, their operational usability is limited by difficulties explaining dynamics behind the predictions, which are smoothed out and missing the high-frequency features due to optimizing for mean error loss functions. We experiment with hand-engineering of the advection-diffusion differential equation into a PhyCell to introduce more accurate physical prior to a PhyDNet model that disentangles physical and residual dynamics. Results indicate that while PhyCell can learn the intended dynamics, training of PhyDNet remains driven by loss optimization, resulting in a model with the same prediction capabilities.

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Matej Choma, Petr Šimánek, Jakub Bartel. 2023-01-27. Improving deep learning precipitation nowcasting by using prior knowledge. https://arxiv.org/abs/2301.11707

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