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- W4313121310 abstract "Retrieving temperature structure of the interior ocean based on surface parameters is important for the study of complex dynamic processes in the ocean. This study introduces an artificial intelligence (AI) approach called Attention U-net to reconstruct the subsurface temperature (ST) field in the South China Sea (SCS) from sea surface parameters. The 5-day average temperature profiles with 0.5° spatial resolution from Simple Ocean Data Assimilation (SODA) product were used for training the network and evaluating the accuracy of estimated results. The Attention U-net model showed a good performance on the ST reconstruction in the upper 100 m of the SCS. The root mean square error (RMSE) between the reconstructed temperature and SODA reanalysis data ranges from 0.39 to 1.42°C, and the average value is 1.08°C. The correlation coefficient (R) is significant in the upper 60 m and becomes weaker as depth increases. The overall R is 0.95. This study provides an effective technique for the ST reconstruction with relatively high spatial and temporal resolution in the SCS." @default.
- W4313121310 created "2023-01-06" @default.
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- W4313121310 date "2022-07-17" @default.
- W4313121310 modified "2023-10-16" @default.
- W4313121310 title "Deep Learning Based Subsurface Temperature Reconstruction in the South China Sea from Surface Parameters" @default.
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- W4313121310 doi "https://doi.org/10.1109/igarss46834.2022.9883749" @default.
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