Abstract
Abstract. After more than 10 years in orbit, the Soil Moisture and Ocean Salinity (SMOS) European mission is still a unique, high-quality instrument for providing soil moisture over land and sea surface salinity (SSS) over the oceans. At the Barcelona Expert Center (BEC), a new reprocessing of 9 years (2011–2019) of global SMOS SSS maps has been generated. This work presents the algorithms used in the generation of BEC global SMOS SSS product v2.0, as well as an extensive quality assessment. Three SMOS SSS fields are distributed: a high-resolution level-3 product (with DOI https://doi.org/10.20350/digitalCSIC/12601, Olmedo et al., 2020a) consisting of binned SSS in 9 d maps at 0.25∘×0.25∘; low-resolution level-3 SSS computed from the binned salinity by applying a smoothing spatial window of 50 km radius; and level-4 SSS (with DOI https://doi.org/10.20350/digitalCSIC/12600, Olmedo et al., 2020b) consisting of daily 0.05∘×0.05∘ maps that are computed by multifractal fusion with sea surface temperature maps. For the validation of BEC SSS products, we have applied a battery of tests aimed at the assessment of quality of the products both in value and in structure. First, we have compared BEC SSS products with near-to-surface salinity measurements provided by Argo floats. Secondly, we have assessed the geophysical consistency of the products characterized by singularity analysis, and the effective spatial resolutions are also estimated by means of power density spectra and singularity density spectra. Finally, we have calculated full maps of SSS errors by using correlated triple collocation. We have compared the performance of the BEC SMOS product with other satellite SSS and reanalysis products. The main outcomes of this quality assessment are as follows. (i) The bias between BEC SMOS and Argo salinity is lower than 0.02 psu at a global scale, while the standard deviation of their difference is lower than 0.34 and 0.27 psu for the high- and low-resolution level-3 fields (respectively) and 0.24 psu for the level-4 salinity. (ii) The effective spatial resolution is around 40 km for all SSS products and regions. (iii) The results from triple collocation show the BEC SMOS level-4 product as the product with the lowest estimated salinity error in most of the global ocean and the BEC SMOS high-resolution level-3 as the one with the lowest estimated salinity error in regions strongly affected by rainfall and continental freshwater discharge.
Highlights
The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite was launched in November 2009, carrying the first orbiting radiometer that collects regular and global observations from space of two Essential Climate Variables (ECV) according to the Global Climate Observing System: sea surface salinity (SSS) and soil moisture (SM) (Font et al, 2010; Kerr et al, 2010; Mecklenburg et al, 2009)
In this work we present the new reprocessing of the Barcelona Expert Center (BEC) SMOS SSS global L3 and L4 products v2.0 for a 9-year period comprising 2011 to 2019, which comes with an improvement of the currently used methodology
We have presented 9 years of the new release of SMOS SSS global products generated at the Barcelona Expert Center: the BEC SMOS SSS global L3 and L4 products v2.0
Summary
The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite was launched in November 2009, carrying the first orbiting radiometer that collects regular and global observations from space of two Essential Climate Variables (ECV) according to the Global Climate Observing System: sea surface salinity (SSS) and soil moisture (SM) (Font et al, 2010; Kerr et al, 2010; Mecklenburg et al, 2009). An extensive battery of validation methods is applied to 1 year (2017) of data, and the results are compared with three other satellite and one reanalysis SSS products. Those methods are (i) statistics of the differences with Argo salinity match-ups; (ii) singularity analysis to assess the geophysical consistency of the data (Turiel et al, 2008b); (iii) spectral analysis to analyze the effective spatial resolution of each product, using power density spectra (PDS) and singularity power spectra (SPS) (Hoareau et al, 2018b); and (iv) triple collocation analysis to estimate the errors of the different products (González-Gambau et al, 2020).
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