Validating SMOS ocean surface salinity in the Atlantic with Argo and operational ocean model data

Banks, C.J. ORCID:; Gommenginger, C.P. ORCID:; Srokosz, M.A. ORCID:; Snaith, H.M.. 2012 Validating SMOS ocean surface salinity in the Atlantic with Argo and operational ocean model data. IEEE Transactions on Geoscience and Remote Sensing, 50 (5). 1688-1702.

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This paper provides an assessment of synoptic measurements of sea surface salinity (SSS) from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) satellite. Due to the complex nature of the response of L-band signals to SSS, SMOS provides three values of SSS at each grid point from three different forward models. To meet oceanographic requirements for SSS retrieval accuracy, SMOS Level 2 SSS products are averaged over time and space. This paper reports on validation studies in the Atlantic based on monthly Level 3 products on a $1^{circ}times 1^{circ}$ grid for September 2010. Outside coastal regions, large-scale SSS patterns from SMOS are in general agreement with climatology, Argo, and ocean model output. During September 2010, SSS from descending passes provides reasonable quantitative estimates, while SSS from ascending passes overestimates SSS by over 1 practical salinity unit (psu). The daily mean difference in SSS between ascending and descending passes varies during August–December 2010, reaching a maximum in September. Differences in SMOS SSS from the three models are an order of magnitude smaller than differences between ascending and descending passes. Gridded SMOS SSS data are compared against output from the U.K. Met Office Forecasting Ocean Assimilation Model (FOAM)-Nucleus for European Modelling of the Ocean (NEMO). Basic checks confirm that SSS from FOAM-NEMO is unbiased against Argo and that FOAM-NEMO SSS is a useful independent data source to validate and rapidly identify departures in SMOS SSS. Over the whole Atlantic, SMOS SSS variability against FOAM-NEMO is around 0.9 psu, decreasing to 0.5 psu over the subtropical North Atlantic.

Item Type: Publication - Article
Digital Object Identifier (DOI):
Programmes: NOC Programmes
ISSN: 01962892
Additional Keywords: Microwave radiometry, remote sensing, sea surface
Date made live: 16 Jan 2012 15:11 +0 (UTC)

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