Physics-Informed Data-Driven Model for Short-Term Precipitation Prediction Using Radar-Observed Big Data

With the development of big data and advances in computing capability, data-driven models have shown significant potential in various fields. In this regard, several studies have focused on using meteorological big data for short-term rainfall predictions. However, limited efforts have been devoted toward elucidating the physical nature of meteorological phenomena. To address this, we constructed a physics-informed data-driven model for short-term precipitation predictions using meteorological big data. The spatial movement of precipitation was predicted via the optical flow method, and the substantial development and decay of precipitation was predicted using Koopman operator analysis. We verified the accuracy of the proposed model based on the radar-observed precipitation data for 2015 and 2017 in Japan. The results reveal that the model can effectively analyze changes in the physical quantities and dynamic systems of atmospheric fluids.