A Comparison of Satellite-Derived Sea Surface Salinity and Salt Fluxes in the Southern Ocean

Ferster, Brady; Subrahmanyam, Bulusu

doi:10.1007/s41976-018-0001-5

Cited by 6 publications

(1 citation statement)

References 43 publications

(51 reference statements)

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“…The currently available SMOS SSS data, both in the form of level 2 (L2) and level 3 (L3) products, have been validated against in situ measurements in a variety of circumstances. They may take the form of a direct comparison with co-located SSS measurements from Argo floats [31][32][33], thermosalinograph (TSG) [31,32,[34][35][36] and saildrones [31], the comparison with data from tropical moored buoys array at seasonal scale [37,38] and the global and regional comparison with in situ analyzed products [27,34,[39][40][41][42][43][44][45], as well as the SSS uncertainty estimations through a triple collocation of three independent SSS products [46,47]. However, the reliability of a comparison with both individual Argo floats and gridded Argo products has long been limited by the sparse sampling rate, especially near the coast [31,34,39,48], near large river mouths [41,42,49] and in the polar seas [32,35,36,40,43,50].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis between Sea Surface Salinity Derived from SMOS Satellite Retrievals and in Situ Measurements

et al. 2022

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Validating Sea Surface Salinity (SSS) data has become a key component of the Soil Moisture Ocean Salinity (SMOS) satellite mission. In this study, the gridded SMOS SSS products are compared with in situ SSS data from analyzed products, a ship-based thermosalinograph and a tropical moored buoy array. The comparison was conducted at different spatial and temporal scales. A regional comparison in the Baltic Sea shows that SMOS slightly underestimates the mean SSS values. The influence of river discharge overrides the temperature in the Baltic Sea, bringing larger biases near river mouths in warm seasons. The global comparison with two Optimal Interpolated (OI) gridded in situ products shows consistent large-scale structures. Excluding regions with large SSS biases, the mean ΔSSS between monthly gridded SMOS data and OI in situ data is −0.01 PSU in most open sea areas between 60°S and 60°N, with a mean Root Mean Square Deviation (RMSD) of 0.2 PSU and a mean correlation coefficient of 0.50. An interannual tendency of mean ΔSSS shifting from negative to positive between satellite SSS and in situ SSS has been identified in tropical to mid-latitude seas, especially across the tropical eastern Pacific Ocean. A comparison with collocated buoy salinity shows that on weekly and interannual scales, the SMOS Level 3 (L3) product well captures the SSS variations at the locations of tropical moored buoy arrays and shows similar performance with in situ gridded products. Excluding suspicious buoys, the synergetic analysis of SMOS, SMAP and gridded in situ products is capable of identifying the erroneous data, implying that satellite SSS has the potential to act as a real-time 27 Quality Control (QC) for buoy data.

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