Physics-Informed Multitemporal Ensemble Learning for Near Real-Time Precipitation Estimates From Himawari-8/-9.

As a significant element in the water cycle and a key parameter associated with atmospheric circulation, precipitation requires to be fast and accurately monitored. In this study, a new physics-informed multitemporal model (PMDF) is proposed for the estimates of near real-time (NRT) high-resolution (0.02°) precipitation, which adopts an ensemble learning framework. The PMDF model can introduce physical knowledge into itself and fast generate precipitation during the estimating phase with no auxiliary data. Validation results show that the PMDF model performs well, with the CC (CSI) of 0.44 (0.49) and 0.73 (0.63) at hourly and daily scales, respectively. The designed physics-informed and multitemporal strategies both can effectively improve the model accuracy, which yields a significantly better performance than other widely used precipitation products. Furthermore, we can clearly observe the hourly variations of precipitation from estimated results at a high spatial resolution.