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

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

doi:10.1007/s13131-021-1880-5

Volume 41 Issue 2

Feb. 2022

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Guijun Han, Jianfeng Zhou, Qi Shao, Wei Li, Chaoliang Li, Xiaobo Wu, Lige Cao, Haowen Wu, Yundong Li, Gongfu Zhou. Bias correction of sea surface temperature retrospective forecasts in the South China Sea[J]. Acta Oceanologica Sinica, 2022, 41(2): 41-50. doi: 10.1007/s13131-021-1880-5

Citation:

Guijun Han, Jianfeng Zhou, Qi Shao, Wei Li, Chaoliang Li, Xiaobo Wu, Lige Cao, Haowen Wu, Yundong Li, Gongfu Zhou. Bias correction of sea surface temperature retrospective forecasts in the South China Sea[J]. Acta Oceanologica Sinica, 2022, 41(2): 41-50. doi: 10.1007/s13131-021-1880-5

Citation:

Guijun Han, Jianfeng Zhou, Qi Shao, Wei Li, Chaoliang Li, Xiaobo Wu, Lige Cao, Haowen Wu, Yundong Li, Gongfu Zhou. Bias correction of sea surface temperature retrospective forecasts in the South China Sea[J]. Acta Oceanologica Sinica, 2022, 41(2): 41-50. doi: 10.1007/s13131-021-1880-5

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

doi: 10.1007/s13131-021-1880-5

Guijun Han¹,
Jianfeng Zhou¹,
Qi Shao^{1, 2},
Wei Li^{1, 2
,
,},
Chaoliang Li^{1
,
,},
Xiaobo Wu¹,
Lige Cao¹,
Haowen Wu¹,
Yundong Li¹,
Gongfu Zhou¹

1.
School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
2.
Tianjin Key Laboratory for Oceanic Meteorology, Tianjin 300074, China

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

More Information

Corresponding author: E-mail: liwei1978@tju.edu.cn; chaoliang_li0608@163.com
Received Date: 2021-05-21
Accepted Date: 2021-06-12

Available Online: 2021-12-09

Publish Date: 2022-02-01

Abstract

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.
- sea surface temperature retrospective forecasts,
- bias correction,
- backpropagation neural network,
- empirical orthogonal function analysis,
- South China Sea

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References(41)

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