Next Article in Journal
CO2 Flux over the Contiguous United States in 2016 Inverted by WRF-Chem/DART from OCO-2 XCO2 Retrievals
Previous Article in Journal
Modified Interpolation Method of Multi-Reference Station Tropospheric Delay Considering the Influence of Height Difference
Previous Article in Special Issue
Global Analysis of Coastal Gradients of Sea Surface Salinity
Article

Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model

1
Center for Marine Science, University of North Carolina Wilmington, Wilmington, NC 28403, USA
2
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Viviane V. Menezes
Remote Sens. 2021, 13(15), 2995; https://doi.org/10.3390/rs13152995
Received: 15 June 2021 / Revised: 26 July 2021 / Accepted: 27 July 2021 / Published: 30 July 2021
(This article belongs to the Special Issue Moving Forward on Remote Sensing of Sea Surface Salinity)
Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values. View Full-Text
Keywords: surface salinity; ocean modeling; representation error; satellite validation; matchups surface salinity; ocean modeling; representation error; satellite validation; matchups
Show Figures

Figure 1

MDPI and ACS Style

Bingham, F.M.; Fournier, S.; Brodnitz, S.; Ulfsax, K.; Zhang, H. Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model. Remote Sens. 2021, 13, 2995. https://doi.org/10.3390/rs13152995

AMA Style

Bingham FM, Fournier S, Brodnitz S, Ulfsax K, Zhang H. Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model. Remote Sensing. 2021; 13(15):2995. https://doi.org/10.3390/rs13152995

Chicago/Turabian Style

Bingham, Frederick M., Severine Fournier, Susannah Brodnitz, Karly Ulfsax, and Hong Zhang. 2021. "Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model" Remote Sensing 13, no. 15: 2995. https://doi.org/10.3390/rs13152995

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop