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Combining In Situ and Satellite Observations to Retrieve Salinity and Density at the Ocean Surface

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  • 1 Istituto di Scienze dell’Atmosfera e del Clima, CNR, Roma, Italy
  • | 2 Istituto di Scienze dell’Atmosfera e del Clima, CNR, Roma, and Istituto per l’Ambiente Marino Costiero, CNR, Napoli, Italy
  • | 3 Istituto di Scienze dell’Atmosfera e del Clima, CNR, Roma, Italy
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Abstract

Monitoring sea surface density (SSD), sea surface salinity (SSS), and sea surface temperature (SST) allows for investigating important aspects of the earth system dynamics, with relevant implications on both local/regional short-scale processes and global climate. Different approaches combine in situ measurements and satellite data to provide gap-free SSS at regular spatial and temporal resolution, aiming to resolve ocean mesoscale. Depending on the application, however, knowing SSD would be more useful than SSS and/or SST alone. Indeed, even if density can be obtained by combining SSS and SST maps at the same nominal resolution, this procedure can lead to spurious features and larger errors when SSS and SST are obtained from different observations and interpolation techniques, especially at the mesoscale. A multidimensional covariance model is applied to interpolate either in situ salinity or in situ density measurements and to build dynamically coherent surface fields, using satellite SST differences as an additional parameter in the optimal estimate. SSS/SSD level 4 (L4) maps are reconstructed over the North Atlantic area, analyzing one month of data. The L4 data are validated using data from the first Salinity Processes in the Upper Ocean Regional Study (SPURS-1) field campaign. The root-mean-square error (RMSE) ranges between 0.03 and 0.13 for the SSS L4 data, and between 0.09 and 0.32 kg m−3 for the SSD L4 data, with improvements of up to 20% with respect to standard products. A holdout validation provides similar values for the SSS RMSE (0.13 ÷ 0.17) and the SSD RMSE (0.13 ÷ 0.17 kg m−3). The limitations and advantages of the two approaches are further discussed and analyzed by looking at spatial wavenumber spectra, showing that the multidimensional optimum interpolation (OI) method significantly increases the L4 effective resolution.

Corresponding author address: Riccardo Droghei, Istituto di Scienze dell’Atmosfera e del Clima, CNR, via Fosso del Cavaliere 100, Roma 00133, Italy. E-mail: riccardo.droghei@artov.isac.cnr.it

Abstract

Monitoring sea surface density (SSD), sea surface salinity (SSS), and sea surface temperature (SST) allows for investigating important aspects of the earth system dynamics, with relevant implications on both local/regional short-scale processes and global climate. Different approaches combine in situ measurements and satellite data to provide gap-free SSS at regular spatial and temporal resolution, aiming to resolve ocean mesoscale. Depending on the application, however, knowing SSD would be more useful than SSS and/or SST alone. Indeed, even if density can be obtained by combining SSS and SST maps at the same nominal resolution, this procedure can lead to spurious features and larger errors when SSS and SST are obtained from different observations and interpolation techniques, especially at the mesoscale. A multidimensional covariance model is applied to interpolate either in situ salinity or in situ density measurements and to build dynamically coherent surface fields, using satellite SST differences as an additional parameter in the optimal estimate. SSS/SSD level 4 (L4) maps are reconstructed over the North Atlantic area, analyzing one month of data. The L4 data are validated using data from the first Salinity Processes in the Upper Ocean Regional Study (SPURS-1) field campaign. The root-mean-square error (RMSE) ranges between 0.03 and 0.13 for the SSS L4 data, and between 0.09 and 0.32 kg m−3 for the SSD L4 data, with improvements of up to 20% with respect to standard products. A holdout validation provides similar values for the SSS RMSE (0.13 ÷ 0.17) and the SSD RMSE (0.13 ÷ 0.17 kg m−3). The limitations and advantages of the two approaches are further discussed and analyzed by looking at spatial wavenumber spectra, showing that the multidimensional optimum interpolation (OI) method significantly increases the L4 effective resolution.

Corresponding author address: Riccardo Droghei, Istituto di Scienze dell’Atmosfera e del Clima, CNR, via Fosso del Cavaliere 100, Roma 00133, Italy. E-mail: riccardo.droghei@artov.isac.cnr.it
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