Editorial Type:
Article
Article Type:
Research Article

A Novel Approach for the High-Resolution Interpolation of In Situ Sea Surface Salinity

Bruno Buongiorno Nardelli Istituto di Scienze dell’Atmosfera e del Clima, Rome, and Istituto per l’Ambiente Marino Costiero, Napoli, Italy

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Print Publication:
01 Jun 2012
DOI:
https://doi.org/10.1175/JTECH-D-11-00099.1
Page(s):
867–879
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Abstract

A novel technique for the high-resolution interpolation of in situ sea surface salinity (SSS) observations is developed and tested. The method is based on an optimal interpolation (OI) algorithm that includes satellite sea surface temperature (SST) in the covariance estimation. The covariance function parameters (i.e., spatial, temporal, and thermal decorrelation scales) and the noise-to-signal ratio are determined empirically, by minimizing the root-mean-square error and mean error with respect to fully independent validation datasets. Both in situ observations and simulated data extracted from a numerical model output are used to run these tests. Different filters are applied to sea surface temperature data in order to remove the large-scale variability associated with air–sea interaction, because a high correlation between SST and SSS is expected only at small scales. In the tests performed on in situ observations, the lowest errors are obtained by selecting covariance decorrelation scales of 400 km, 6 days, and 2.75°C, respectively, a noise-to-signal ratio of 0.01 and filtering the scales longer than 1000 km in the SST time series. This results in a root-mean-square error of ~0.11 g kg−1 and a mean error of ~0.01 g kg−1, that is, reducing the errors by ~25% and ~60%, respectively, with respect to the first guess.

Corresponding author address: Bruno Buongiorno Nardelli, CNR-Istituto per l’Ambiente Marino Costiero, Calata Porta di Massa, 80133 Napoli, Italy. E-mail: bruno.buongiornonardelli@cnr.it

Abstract

A novel technique for the high-resolution interpolation of in situ sea surface salinity (SSS) observations is developed and tested. The method is based on an optimal interpolation (OI) algorithm that includes satellite sea surface temperature (SST) in the covariance estimation. The covariance function parameters (i.e., spatial, temporal, and thermal decorrelation scales) and the noise-to-signal ratio are determined empirically, by minimizing the root-mean-square error and mean error with respect to fully independent validation datasets. Both in situ observations and simulated data extracted from a numerical model output are used to run these tests. Different filters are applied to sea surface temperature data in order to remove the large-scale variability associated with air–sea interaction, because a high correlation between SST and SSS is expected only at small scales. In the tests performed on in situ observations, the lowest errors are obtained by selecting covariance decorrelation scales of 400 km, 6 days, and 2.75°C, respectively, a noise-to-signal ratio of 0.01 and filtering the scales longer than 1000 km in the SST time series. This results in a root-mean-square error of ~0.11 g kg−1 and a mean error of ~0.01 g kg−1, that is, reducing the errors by ~25% and ~60%, respectively, with respect to the first guess.

Corresponding author address: Bruno Buongiorno Nardelli, CNR-Istituto per l’Ambiente Marino Costiero, Calata Porta di Massa, 80133 Napoli, Italy. E-mail: bruno.buongiornonardelli@cnr.it
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Journal of Atmospheric and Oceanic Technology

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