Bias correction of sea surface temperature retrospective fore-casts in the South China Sea |
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| Authors: | Guijun Han Jianfeng Zhou Qi Shao Wei Li Chaoliang Li Xiaobo Wu Lige Cao Haowen Wu Yundong Li Gongfu Zhou |
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| Affiliation: | 1.School of Marine Science and Technology, Tianjin University, Tianjin 300072, China2.Tianjin Key Laboratory for Oceanic Meteorology, Tianjin 300074, China |
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| Abstract: | 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. |
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| Keywords: | sea surface temperature retrospective forecasts bias correction backpropagation neural network empirical orthogonal function analysis South China Sea |
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