Deep Learning-based Bias Correction Method for Seasonal Prediction of Summer Rainfall in China  

作　　者：QU An-kang BAO Qing ZHU Tao LUO Zhao-ming 瞿安康;包庆;朱涛;罗昭明

机构地区：[1]State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029 China [2]School of Emergency Management Science and Engineering,University of Chinese Academy of Sciences,Beijing 101408 China

出　　处：《Journal of Tropical Meteorology》2025年第1期64-74,共11页热带气象学报(英文版)

基　　金：Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004);Postdoctoral Fellowship Program of CPSF(GZC20232598);China Postdoctoral Science Foundation(2024M753168);National Key Scientific and Technological Infrastructure Project“Earth System Numerical Simulation Facility”(EarthLab)。

摘　　要：Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.

关 键 词：seasonal prediction RAINFALL statistical-dynamical model deep learning 

分 类 号：P409[天文地球—大气科学及气象学]