Improving the heavy rainfall forecasting using a weighted deep learning model - Chen, Yutong; Huang, Gang; Wang, Ya; Tao, Weichen; Tian, Qun; Yang, Kai; Zheng, Jiangshan; He, Hubin (Frontiers in Environmental Science, 2023)
Weather forecasting has been playing an important role in socio-economics. However, operational numerical weather prediction (NWP) is insufficiently accurate in terms of precipitation forecasting, especially for heavy rainfalls. Previous works on NWP bias correction utilizing deep learning (DL) methods mostly focused on a local region, and the China-wide precipitation forecast correction had not been attempted. Meanwhile, earlier studies imposed no particular focus on strong rainfalls despite their severe catastrophic impacts. In this study, we propose a DL model called weighted U-Net (WU-Net) that incorporates sample weights for various precipitation events to improve the forecasts of intensive precipitation in China. It is found that the improvement of the WU-Net reaches 141% and 36.7% on the heaviest precipitation level, comparing with the ECMWF-IFS and the ordinary U-Net, respectively. Further analysis shows that this improvement increases with growing lead time, and distributes mainly in the eastern parts of China. This study suggests that a deep learning model considering the imbalance of the meteorological data could further improve the precipitation forecasting generated by NWP.