Preprints
https://doi.org/10.5194/essd-2021-461
https://doi.org/10.5194/essd-2021-461

  14 Jan 2022

14 Jan 2022

Review status: this preprint is currently under review for the journal ESSD.

First SMOS Sea Surface Salinity dedicated products over the Baltic Sea

Verónica González-Gambau1, Estrella Olmedo1, Antonio Turiel1, Cristina González-Haro1, Aina García-Espriu1, Justino Martínez1, Pekka Alenius2, Laura Tuomi2, Rafael Catany3, Manuel Arias3, Carolina Gabarró1, Nina Hoareau1, Marta Umbert1, Roberto Sabia4, and Diego Fernández4 Verónica González-Gambau et al.
  • 1Barcelona Expert Center (BEC) and Institute of Marine Sciences (ICM), CSIC, P. Marítim de la Barceloneta, 37-49, 08003 Barcelona, Spain
  • 2Finnish Meteorological Institute, Erik Palménin aukio 1, FI-00560 Helsinki, Finland
  • 3ARGANS Ltd, Davy Road, Plymouth Science Park, Derriford, Plymouth, PL6 8BX, United Kingdom
  • 4European Space Agency, ESA-ESRIN. Largo Galileo Galilei 1 Casella Postale 64 00044, Frascati, Italy

Abstract. This paper presents the first Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) dedicated products over the Baltic Sea. The SSS retrieval from L-band brightess temperature (TB) measurements over this basin is really challenging due to important technical issues, such as the land-sea and ice-sea contamination, the high contamination by Radio-Frequency Interferences (RFI) sources, the low sensitivity of L-band TB at SSS changes in cold waters and the poor characterization of dielectric constant models for the low SSS and SST ranges in the basin. For these reasons, exploratory research in the algorithms used from the level 0 up to level 4 has been required to develop these dedicated products. This work has been performed in the framework of the European Space Agency regional initiative Baltic+ Salinity Dynamics.

Two Baltic+ SSS products have been generated for the period 2011–2019 and are freely distributed: the Level 3 (L3) product (daily generated 9-day maps in a 0.25° grid, https://doi.org/10.20350/digitalCSIC/13859) (González-Gambau et al., 2021a) and the Level 4 (L4) product (daily maps in a 0.05° grid, https://doi.org/10.20350/digitalCSIC/13860) (González-Gambau et al., 2021b)), that are computed by applying multifractal fusion to L3 SSS with Sea Surface Temperature (SST) maps. The accuracy of L3 SSS products is typically around 0.7–0.8 psu. The L4 product has an improved spatio-temporal resolution with respect to the L3 and the accuracy is typically around 0.4 psu. Regions with the highest errors and limited coverage are located in Arkona and Bornholm basins and Gulfs of Finland and Riga.

The impact assessment of Baltic+ SSS products has shown that they can help in the understanding of salinity dynamics in the basin. They complement the temporally and spatially very sparse in situ measurements, covering data gaps in the region and they can also be useful for the validation of numerical models, particularly in areas where in situ data are very sparse.

Verónica González-Gambau et al.

Status: open (until 11 Mar 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Verónica González-Gambau et al.

Data sets

Baltic Sea Surface Salinity L4 maps Verónica González-Gambau, Estrella Olmedo, Cristina González-Haro, Aina García-Espriu, Antonio Turiel https://doi.org/10.20350/digitalCSIC/13860

Baltic Sea Surface Salinity L3 maps Verónica González-Gambau, Estrella Olmedo, Cristina González-Haro, Aina García-Espriu, Antonio Turiel https://doi.org/10.20350/digitalCSIC/13859

Verónica González-Gambau et al.

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Short summary
We present the first Soil Moisture and Ocean Salinity Sea Surface Salinity (SSS) dedicated products over the Baltic Sea (ESA Baltic+ Salinity Dynamics). The Baltic+ L3 product covers 9 days in a 0.25º grid. The Baltic+ L4 is derived by merging L3 SSS with sea surface temperature information, giving a daily product in a 0.05º grid. The accuracy of L3 is 0.7-0.8 and 0.4 psu for the L4. Baltic+ products have shown to be useful covering spatio-temporal data gaps and for validating numerical models.