The Study Of SMOS Salinity Data Assimilation And Observations Sensitivity Analysis For South China Sea

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.The influence depth is 40 meters in South China Sea but 150 meters in West Pacific.The experiment results also point out that the assimilation of SSS has impact on simulation of Sea Surface Temperature(SST)and Sea Level Anomaly(SLA).We also study on the observation impact and sensitivity based on data assimilation,The sensitivity analysis frame is constructed sucessfully.We prove the reliability of the frame by the experiment on West Boundary Current and key section for South China Sea,This may improve the design of observation networks and the simulation of ocean state.