Assessment of Two SMOS Sea Surface Salinity Level 3 Products Against Argo Upper Salinity Measurements

Zhang, Haifeng; Chen, Ge; Qian, Chao; Jiang, Haoyu

doi:10.1109/lgrs.2013.2259792

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…Zhang et al . [] compared two SMOS products (BEC L3 and CATDS V02) with Argo measurements and showed that both products perform well in the open low‐latitude oceans; but close to coasts and in higher latitudes the biases were high. The reasons for using the BEC L4 product in the present study are, on the one hand, the availability of data in the study period and, on the other hand, that frontal structures are potentially better taken into account, which is of special importance in our study area.…”

Section: Discussionmentioning

confidence: 99%

Quality assessment of spaceborne sea surface salinity observations over the northern North Atlantic

et al. 2015

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SMOS SSS fields show a temperature-dependent negative SSS bias of up to 22 g/kg for temperatures <5 C. Removing this bias significantly reduces the differences to independent ship-based thermosalinograph data but potentially corrects simultaneously also other effects not related to temperature, such as land contamination or radio frequency interference (RFI). The resulting time-mean bias, averaged over the study area, amounts to 0.1 g/kg. A respective correction applied previously by the Jet Propulsion Laboratory to the Aquarius data is shown here to have successfully removed an SST-related bias in our study area. For both missions, resulting spatial structures of SSS variability agree very well with those available from an eddy-resolving numerical simulation and from Argo data and, additionally they also show substantial salinity changes on monthly and seasonal time scales. Some fraction of the root-mean-square difference between in situ, and SMOS and Aquarius data (approximately 0.9 g/kg) can be attributed to short time scale ocean processes, notably at the Greenland shelf, and could represent associated sampling errors there.

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Section: Discussionmentioning

confidence: 99%

Quality assessment of spaceborne sea surface salinity observations over the northern North Atlantic

et al. 2015

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“…The quality of satellite products were primarily assessed through comparisons between the RMSD of satellite products with respect to in situ data (Bao et al, 2019;Garcia-Eidell et al, 2017;H. Y. Kao et al, 2018;Köhler et al, 2015;Ratheesh et al, 2013;Wenqing Tang et al, 2014;Zhang et al, 2013).…”

Section: Accepted Articlementioning

confidence: 99%

“…The previous assessment studies mainly focused on the accuracy of satellite products. The quality of satellite products was primarily assessed through comparisons between the RMSD of satellite products with respect to in situ data (Bao et al., 2019; Garcia‐Eidell et al., 2017; Kao et al., 2018; Köhler et al., 2015; Ratheesh et al., 2013; Tang et al., 2014; Zhang et al., 2013). However, there are only a few studies on the ability of SSS satellite data to depict oceanic phenomena.…”

Section: Introductionmentioning

confidence: 99%

Application of Phenomena‐Resolving Assessment Methods to Satellite Sea Surface Salinity Products

Bao

Wang

Ren

et al. 2021

Earth and Space Science

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It is necessary to evaluate satellite sea surface salinity (SSS) L3 products prior to using them to analyze SSS variability. Instead of performing comparison analysis on the accuracy of products (e.g., Root Mean Square Deviation (RMSD)), two new evaluation methods, information entropy and local variance, are introduced to assess the performance of satellite SSS products in resolving SSS fronts in the tropical Pacific from a new perspective. The satellite SSS except for SMOS BEC have captured the zonal displacement of SSS fronts under the strong El Niño in 2015. It is effective to use entropy and local variance for the assessment of the capability of satellite SSS products, which can provide new sights for analyzing the variability and distributions of SSS. The spatial distribution of local variance identified the abnormal SSS of the satellite products on or near the islands and the result of SSS entropy is beneficial to the analysis of the mechanisms of the SSS variability. In general, the SMOS CATDS and SMAP_70km product are recommended for scientific applications in the tropical Pacific.

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“…The Soil Moisture and Ocean Salinity (SMOS) [18] satellite was launched in 2009 and remains ongoing. Although satellites can provide abundant SSS data, the observations contain large errors and cannot be directly applied to the assimilation system used in coastal areas (such as the China Sea and Japan Sea) or at high latitudes [16,17,[19][20][21]. Among the previous studies, Kohl et al [17] compared the SMOS dataset produced by the University of Hamburg based on the reprocessed ESA's L2 product (Version 5.50) with EN3 v2a data and they found that the root mean square error (RMSE) is typically approximately 1 g/kg but can reach 3 g/kg in a few locations in the coastal regions and at high latitudes.…”

Section: Of 19mentioning

confidence: 99%

Assimilation of SMOS Sea Surface Salinity in the Regional Ocean Model for South China Sea

Zhang

Wang

et al. 2019

Remote Sensing

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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.

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Assessment of Two SMOS Sea Surface Salinity Level 3 Products Against Argo Upper Salinity Measurements

Cited by 6 publications

References 12 publications

Quality assessment of spaceborne sea surface salinity observations over the northern North Atlantic

Quality assessment of spaceborne sea surface salinity observations over the northern North Atlantic

Application of Phenomena‐Resolving Assessment Methods to Satellite Sea Surface Salinity Products

Assimilation of SMOS Sea Surface Salinity in the Regional Ocean Model for South China Sea

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