Building a Consistent Long-Term SSS Data Record from Multi-Satellite Measurements: A Case Study in the Eastern Tropical Pacific - SPURS-2 (Poster)
[07-Nov-2017] Melnichenko, O., Hacker, P.W., Wentz, F.J., Meissner, T., Maximenko, N.A., and Potemra, J.T.
Presented at the 2016 AGU Fall 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 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 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 between the two sub-sets of the data record is evaluated during their overlapping period in April-June 2015. Verification studies show that SMAP SSS has a very good agreement with the Aquarius SSS, noting that SMAP SSS can provide better spatial resolution. The 5-yr long time series of SSS in the SPURS-2 domain (125oW, 10oN) shows fresher than normal SSS during the last year's El Niño event. The year-mean difference is about 0.5 psu. The annual cycle during the El Niño year also appears to be much weaker than in a normal year.