Sea surface salinity seasonal variability in the tropics from satellites, gridded in situ products and mooring observations
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Keywordsea surface salinity; seasonal variability; satellite validation; harmonic analysis; mooring observations
Satellite observations of sea surface salinity (SSS) have been validated in a number of instances using different forms of in situ data, including Argo floats, moorings and gridded in situ products. Since one of the most energetic time scales of variability of SSS is seasonal, it is important to know if satellites and gridded in situ products are observing the seasonal variability correctly. In this study we validate the seasonal SSS from satellite and gridded in situ products using observations from moorings in the global tropical moored buoy array. We utilize six different satellite products, and two different gridded in situ products. For each product we have computed seasonal harmonics, including amplitude, phase and fraction of variance (R2). These quantities are mapped for each product and for the moorings. We also do comparisons of amplitude, phase and R2 between moorings and all the satellite and gridded in situ products. Taking the mooring observations as ground truth, we find general good agreement between them and the satellite and gridded in situ products, with near zero bias in phase and amplitude and small root mean square differences. Tables are presented with these quantities for each product quantifying the degree of agreement.
© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Bingham, F. M., Brodnitz, S., & Yu, L. Sea surface salinity seasonal variability in the tropics from satellites, gridded in situ products and mooring observations. Remote Sensing, 13(1), (2021): 110, doi:10.3390/rs13010110.
Suggested CitationBingham, F. M., Brodnitz, S., & Yu, L. (2021). Sea surface salinity seasonal variability in the tropics from satellites, gridded in situ products and mooring observations. Remote Sensing, 13(1), 110.
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