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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Acknowledgements
We thank the NOAA’s National Centers for Environmental Information for providing the gridded AVHRR-AMSR data of the daily OISST v2.0 (http://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation), and the Met Office Hadley Centre for providing the EN4.2.1 dataset (http://www.metof-fice.gov.uk/hadobs/en4/download-en4-2-1.html). We also thank the editor and two anonymous reviewers for their helpful comments to improve the quality of this paper.
Funding
The National Key Research and Development Program of China under contract No. 2018YFC1406206; the National Natural Science Foundation of China under contract No. 41876014.
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Han, G., Zhou, J., Shao, Q. et al. Bias correction of sea surface temperature retrospective forecasts in the South China Sea. Acta Oceanol. Sin. 41, 41–50 (2022). https://doi.org/10.1007/s13131-021-1880-5
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DOI: https://doi.org/10.1007/s13131-021-1880-5