Precipitation Nowcasting by Deep Physics-Constrained Neural Networks

During last year's summer storm season, we have introduced a precipitation nowcasting neural network MWNet and deployed it to operational use. The network tackles the nowcasting problem as a sequence to sequence prediction of radar echo, emphasizing high resolution and accuracy. We have conducted two quantitative experiments comparing MWNet 60 min forecasts to other available precipitation nowcasting models, using the metrics CSI and MSE. Both evaluations, over the domain of Denmark for years 2018 - 2020 and over the Czech Republic for the summer storm season of 2021, concluded in favor of our approach. However, we aim to improve MWNet capabilities further by focusing on severe weather nowcasting, the physical soundness of the predictions, and lead times longer than 60 min. Building on the advances in deep learning and its use in spatio-temporal forecasting, MWNet is based on the idea of disentangling physical dynamics from the residual factors. In this contribution, we consider improvements to the physical part of the network, its incorporation into the whole model, and the loss function used during training. Mainly, we are exploring the effect of implementing non-linear partial differential equations into the physical part, with various levels of hand-engineering equation terms. We analyze the impact on the dynamics learned by each part of the network and prediction quality for each setting. MWNet v1.2, based on the proposed architecture, will be operationally used and evaluated by meteorologists in Meteopress during the summer of 2022. This work aims to contribute to bridging the gap between machine learning and physical modeling in weather forecasting, alongside improving precipitation prediction.