The OISSS dataset grids the Level-2 orbital swath data from the Aquarius/SAC-D satellite mission (25 August 2011 to 7 June 2015), the Soil Moisture Active Passive (SMAP) satellite mission (April 2015 – present) and the Soil Moisture and Ocean Salinity (SMOS) satellite mission onto a 0.25-degree spatial and 4-day temporal grid using Optimal Interpolation (OI). In the current version of the OISSS dataset, SMOS data are used to fill gaps in SMAP observations during June-July 2019 and August-September 2022.
Input Data:
The input data are Level-2 SSS retrievals from the Aquarius, SMAP and SMOS satellites.
- Aquarius Level-2 (L2) version 5.0 (end-of-mission) data are produced by the NASA Goddard Space Flight Center’s Aquarius Data Processing System (ADPS) and distributed by the Physical Oceanography Distributed Active Archive Center (PO.DAAC) of the Jet Propulsion Laboratory (JPL), available at https://podaac.jpl.nasa.gov/dataset/AQUARIUS_L2_SSS_V5.
- SMAP observations of SSS are obtained from Level-2 version 5.0 SMAP data produced by the Remote Sensing Systems (RSS; www.remss.com).
- SMOS observations of SSS are obtained from the SMOS Level-2 SSS data products generated by version 700 of the Level 2 OS Operational Processor (L2OS). The data are available from the ESA SMOS online dissemination service at https://smos-diss.eo.esa.int/oads/access/.
Quality control:
Level-2 data from each of the three satellites are first processed to identify quality control (QC) issues. Quality control flags and auxiliary data (such as SST, surface wind, etc.) are used to eliminate questionable data points. Statistical tests based on the standard deviation (STD) are also applied to the data to remove outliers and reduce noise.
Bias correction:
Level-2 satellite data are adjusted for large-scale biases. Only static (time-mean) biases are taken into account. The bias fields are determined relative to in-situ salinity data prior to the analysis.
Optimal Interpolation:
Bias-adjusted SSS retrievals are gridded onto a regular 0.25-degree, 4-day grid using a spatiotemporal Optimum Interpolation (OI) algorithm (Bretherton et al., 1976). At each grid point, an estimate of the SSS field is provided by a weighted least squares fit of the observations that influence the grid point, where the weights are designed to minimize the estimation error variance. They are the inverse of the error covariance matrices of the observations and are estimated from the observations prior to the analysis (Melnichenko et al., 2014; Melnichenko et al., 2016).
Output Data:
Multi-mission optimally interpolated sea surface salinity (OISSS). The OISSS dataset is available from August 28, 2011 to present.
A very detailed description of the OISSS processing algorithm can be found in its user guide: OISSS_Product_Notes_v2.pdf
References
Bretherton, F. P., R.E Davis, and C.B. Fandry, 1976: A technique for objective analysis and design of oceanographic experiments applied to MODE-73, Deep Sea Res., 23, 559-582.
Melnichenko, O., P. Hacker, N. Maximenko, G. Lagerloef, and J. Potemra, 2014: Spatial Optimal Interpolation of Aquarius Sea Surface Salinity: Algorithms and Implementation in the North Atlantic, J. Atmos. Oceanic Technol., 31, 1583-1600.
Melnichenko, O., P. Hacker, N. Maximenko, G. Lagerloef, and J. Potemra, 2016: Optimum interpolation analysis of Aquarius sea surface salinity, J. Geophys. Res. Oceans, 121, 602-616, doi:10.1002/2015JC011343.