Validation and correction of sea surface salinity retrieval from SMAP

• Published in: Acta oceanologica Sinica Vol. 39; no. 3; pp. 148 - 158
• Main Authors: Qin, Sisi, Wang, Hui, Zhu, Jiang, Wan, Liying, Zhang, Yu, Wang, Haoyun
• Format: Journal Article
• Language: English
• Published: Beijing The Chinese Society of Oceanography 01.03.2020; Springer Nature B.V; University of the Chinese Academy of Sciences, Beijing 100049, China; University of the Chinese Academy of Sciences, Beijing 100049, China%Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Beijing 100081, China; National Meteorological Center, Beijing 100081, China; Numerical Weather Prediction Center of China Meteorological Administration, Beijing 100081, China%Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Beijing 100081, China%International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
• Subjects: Bias; Climatology; Coastal zone; Daily; Drifters; Earth and Environmental Science; Earth Sciences; Ecology; Engineering Fluid Dynamics; Environmental Chemistry; Floats; Latitude; Marine & Freshwater Sciences; Oceanography; Oceans; Precipitation; Regression analysis; Root-mean-square errors; Salinity; Salinity effects; Sea surface; Sea surface temperature; Soil; Soil moisture; Soils; Surface salinity; Surface temperature; Tropical climate; Tropical environment; Tropical environments; soil moisture and ocean salinity (SMOS); soil moisture active passive (SMAP); correction; sea surface salinity (SSS); validation; soil moisture and ocean salinity(SMOS)
• ISSN: 0253-505X; 1869-1099
• DOI: 10.1007/s13131-020-1533-0

In this study, sea surface salinity (SSS) Level 3 (L3) daily product derived from soil moisture active passive (SMAP) during the year 2016, was validated and compared with SSS daily products derived from soil Moisture and ocean salinity (SMOS) and in-situ measurements. Generally, the root mean square error (RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the sea surface temperature (SST). Then, a regression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.