Sea Surface Salinity Subfootprint Variability from a Global High-Resolution Model
- UNCW Author/Contributor (non-UNCW co-authors, if there are any, appear on document)
- Frederick Morton Bingham, Professor (Creator)
- Institution
- The University of North Carolina Wilmington (UNCW )
- Web Site: http://library.uncw.edu/
Abstract: Subfootprint variability (SFV) is variability at a spatial scale smaller than the footprint of a satellite, and it cannot be resolved by satellite observations. It is important to quantify and understand, as it contributes to the error budget for satellite data. The purpose of this study was to estimate the SFV for sea surface salinity (SSS) satellite observations. This was performed by using a high-resolution numerical model, a 1/48° version of the MITgcm simulation, from which one year of output has recently become available. SFV, defined as the weighted standard deviation of SSS within the satellite footprint, was computed from the model for a 2° × 2° grid of points for the one model year. We present maps of median SFV for 40 and 100 km footprint size, display histograms of its distribution for a range of footprint sizes and quantify its seasonality. At a 100 km (40 km) footprint size, SFV has a mode of 0.06 (0.04). It is found to vary strongly by location and season. It has larger values in western-boundary and eastern-equatorial regions, as well as in a few other areas. SFV has strong variability throughout the year, with the largest values generally being in the fall season. We also quantified the representation error, the degree of mismatch between random samples within a footprint and the footprint average. Our estimates of SFV and representation error can be used in understanding errors in the satellite observation of SSS.
Sea Surface Salinity Subfootprint Variability from a Global High-Resolution Model
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Created on 11/2/2021
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Additional Information
- Publication
- https://doi.org/10.3390/rs13214410
- Language: English
- Date: 2021
- Keywords
- sea surface salinity, subfootprint variability, errors, validation