Bias correction of sea surface temperature retrospective forecasts in the South China Sea

作     者：Guijun Han Jianfeng Zhou Qi Shao Wei Li Chaoliang Li Xiaobo Wu Lige Cao Haowen Wu Yundong Li Gongfu Zhou Guijun Han;Jianfeng Zhou;Qi Shao;Wei Li;Chaoliang Li;Xiaobo Wu;Lige Cao;Haowen Wu;Yundong Li;Gongfu Zhou

作者机构：School of Marine Science and TechnologyTianjin UniversityTianjin 300072China Tianjin Key Laboratory for Oceanic MeteorologyTianjin 300074China 

出 版 物：《Acta Oceanologica Sinica》 (海洋学报（英文版）)

年 卷 期：2022年第41卷第2期

页      面：41-50页

核心收录：

学科分类：07[理学] 0707[理学-海洋科学] 

基　　金：The National Key Research and Development Program of China under contract No.2018YFC1406206 the National Natural Science Foundation of China under contract No.41876014 

主　　题：sea surface temperature retrospective forecasts bias correction backpropagation neural network empirical orthogonal function analysis South China Sea 

摘      要：Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature(SST) forecasts have been developed in this study: a backpropagation neural network(BPNN) algorithm, and a hybrid algorithm of empirical orthogonal function(EOF) analysis and BPNN(named EOF-BPNN). The performances of these two methods are validated using bias correction experiments implemented in the South China Sea(SCS), in which the target dataset is a six-year(2003–2008) daily mean time series of SST retrospective forecasts for one-day in advance, obtained from a regional ocean forecast and analysis system called the China Ocean Reanalysis(CORA),and the reference time series is the gridded satellite-based SST. The bias-correction results show that the two methods have similar good skills;however, the EOF-BPNN method is more than five times faster than the BPNN method. Before applying the bias correction, the basin-wide climatological error of the daily mean CORA SST retrospective forecasts in the SCS is up to-3°C;now, it is minimized substantially, falling within the error range(±0.5°C) of the satellite SST data.