Matchup Strategies for Satellite Sea Surface Salinity Validation

Abstract

Satellite validation is the process of comparing satellite measurements with in-situ measurements to ensure their accuracy. Satellite and in-situ sea surface salinity (SSS) measurements are different due to instrumental errors (IE), retrieval errors (RE), and representation differences (RD). In real-world data, IE, RE, and RD are inseparable, but validations seek to quantify only instrumental and retrieval error. Our goal is to determine which of four methods comparing in-situ and satellite measurements minimizes RD most effectively, which includes differences due to mismatches in the location and timing of the measurement, as well as representation error caused by the averaging of satellite measurements over a footprint. IE and RE were obviated by using simulated Argo float, and L2 NASA/SAC-D Aquarius, NASA·SMAP, and ESA·SMOS data generated from the high-resolution ECCO (Estimating the Climate and Circulation of the Oceans) model SSS data. The methods tested include the all-salinity difference averaging method (ASD), the N closest method (NCLO), which is an averaging method that is optimized for different satellites and regions of the ocean, and two single salinity difference methods—closest in space (SSDS) and closest in time (SSDT). The root mean square differences (RMSD) between the simulated in-situ and satellite measurements in seven regions of the ocean are used as a measure of the effectiveness of each method. The optimization of NCLO is examined to determine how the optimum matchup strategy changes depending on satellite track and region. We find that the NCLO method marginally produces the lowest RMSD in all regions but invoking a regionally optimized method is far more computationally expensive than the other methods. We find that averaging methods smooth IE, thus perhaps misleadingly lowering the detected instrumental error in the L2 product by as much as 0.15 PSU. It is apparent from our results that the dynamics of a particular region have more of an effect on matchup success than the method used. We recommend the SSDT validation strategy because it is more computationally efficient than NCLO, considers the proximity of in-situ and satellite measurements in both time and space, does not smooth instrumental errors with averaging, and generally produces RMSD values only slightly higher than the optimized NCLO method.