Meetings: Documents

Building a Consistent Multi-Satellite SSS Data Record: A Case Study in the Eastern Tropical Pacific (SPURS-2)
[28-Aug-2018] Melnichenko, O., Hacker, P., Meissner, T., Wentz., F., and Potemra, J.
Presented at the 2018 Ocean Salinity Science Team and Salinity Continuity Processing Meeting
To address the need for a consistent, continuous, long-term, high-resolution sea surface salinity (SSS) dataset for ocean research and applications, a trial SSS analysis is produced in the eastern tropical Pacific from multi-satellite observations. The new SSS data record is a synergy of data from two NASA satellite missions. The beginning segment, covering the period from September 2011 to June 2015, utilizes Aquarius SSS data and is based on the optimum interpolation analysis (OI SSS) developed at the University of Hawaii. The analysis is produced on a 0.25-degree grid and uses a dedicated bias-correction algorithm to correct the satellite retrievals for large-scale biases with respect to in situ data. The time series is continued with the Soil Moisture Active Passive (SMAP) satellite-based SSS data provided by Remote Sensing Systems (RSS). To ensure consistency and continuity in the data record, SMAP SSS fields are adjusted using a set of optimally designed spatial filters and in situ, primarily Argo, data to: (i) remove large-scale satellite biases, and (ii) reduce small-scale noise, while preserving the high spatial and temporal resolution of the data set. The consistency and accuracy of the new SSS data set is evaluated against in situ salinity from Argo buoys and TAO array. Statistical comparison of the new analysis with in situ data demonstrates its superior performance compared to the standard Level-3 products as well as selected Soil Moisture and Ocean Salinity (SMOS) satellite-based Level-4 (bias corrected) products. The nearly 7-yr long time series of SSS from two NASA satellite missions are also used to characterize dominant patterns of SSS variability in the SPURS-2 domain.

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