Abstract
Ocean salinity has an important impact on marine environment simulations. The Soil Moisture and Ocean Salinity (SMOS) mission is the first satellite in the world to provide large-scale global salinity observations of the oceans. Salinity remote sensing observations in the open ocean have been successfully applied in data assimilations, while SMOS salinity observations contain large errors in the coastal ocean (including the South China Sea (SCS)) and high latitudes and cannot be effectively applied in ocean data assimilations. In this paper, the SMOS salinity observation data are corrected with the Generalized Regression Neural Network (GRNN) in data assimilation preprocessing, which shows that after correction, the bias and root mean square error (RMSE) of the SMOS sea surface salinity (SSS) compared with the Argo observations can be reduced from 0.155 PSU and 0.415 PSU to −0.003 PSU and 0.112 PSU, respectively, in the South China Sea. The effect is equally significant in the northwestern Pacific region. The preprocessed salinity data were applied to an assimilation in a coastal region for the first time. The six groups of assimilation experiments set in the South China Sea showed that the assimilation of corrected SMOS SSS can effectively improve the upper ocean salinity simulation.
Highlights
Ocean data assimilation can provide better initial and boundary conditions for ocean numerical simulations, improving the forecasts of ocean numerical models
The salinities used in this study are the salinities corrected by delayed-mode in the Argo products and we only use the data with a quality flag equal to 1 [26]
The sea surface salinity (SSS) errors are less than 0.1 PSU in the northwestern Pacific and 0.15 PSU in the northern South China Sea (SCS) after the SSS is corrected and assimilated into the model, (EX3, Figure 6, Neural Networks (NNs)), which is verified in the NNALL experiment (EX6), indicating that the assimilation of the corrected SSS can improve the model SSS simulation
Summary
Ocean data assimilation can provide better initial and boundary conditions for ocean numerical simulations, improving the forecasts of ocean numerical models. The SMOS L3 SSS product released by the Barcelona Expert Center (BEC) is retrieved using a debiased non-Bayesian method to remove the systematic part of RFI-induced and LSC-induced errors of the retrieved salinities [22] The advantage of this product over other SSS products is that the dataset contains observations from both coastal regions and high latitudes. NNs have been applied widely in remote sensing field so that the Generalized Regression Neural Network (GRNN) is proposed in this study to correct the SMOS SSS in the preprocessing process. To assimilate SMOS SSS into the ocean model and improve the SSS simulation, the Generalized Regression Neural Network is proposed to correct the SSS of the BEC L3 objective analysis data set based on the characteristic that GRNN can work with a dataset with low accuracy.
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