Probabilistic Precipitation Nowcasting with Physically-Constrained GANs

It is generally accepted that weather forecasts contain errors due to the chaotic nature of the atmosphere. Regression models, such as neural networks, are traditionally trained to minimize the pixel-wise difference between their predictions and ground truth. The major shortcoming of these models is that they express uncertainty about prediction with blurring, especially for longer prediction lead times. One way to tackle this issue is to use a generative adversarial network, which learns what real precipitation should look like during training. Coupled with a loss, such as Mean Squared or Mean Absolute Error, these networks can produce highly accurate and realistic nowcasts. As there is an inherent randomness in those networks, they allow to be sampled from, just like ensemble models, and various probabilistic metrics can be calculated from the samples. In this work, we have designed a physically-constrained generative adversarial network for radar reflectivity prediction. We compare this network to one without physical restraints and show that it predicts events with higher accuracy and shows much less variance among its samples. Furthermore, we explore fine-tuning the network to the prediction of severe weather events, as an accurate prediction of these benefits both automated warning systems and forecasters.