Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data

- A multidimensional optimal interpolation is applied to SMOS and in situ SSS data.
- Interpolated surface salinity fields are able to resolve ocean mesoscale.
- A complete validation vs independent data is presented.

Availability of accurate remotely-sensed sea surface salinity (SSS) measurements is crucial to investigating fundamental aspects of the global hydrological cycle, ocean dynamics and marine biogeochemistry. The European Space Agency (ESA) Soil Moisture Ocean Salinity (SMOS) mission has been specifically designed for this aim. However, SMOS data display a high level of noise with respect to the signal they have to detect. Space-time averaging over relatively large sampling periods/areas is thus generally carried out to increase SSS accuracy, and further interpolation is required to fill in data gaps resulting from both mission geometry and other instrumental/physical limitations. Here, a daily, 1/4° nominal resolution, mesoscale-resolving SSS field product is obtained by using a multidimensional optimal interpolation (OI) algorithm combining SMOS salinity retrievals and satellite sea surface temperature data with in situ salinity measurements. The methodology has been developed in the framework of the ESA OSMOSIS (Ocean ecoSystem Modelling based on Observations from Satellite and In-Situ data) project and it is applied here to a wide zonal portion of the Southern Hemisphere (10°S-65°S). The interpolated SSS has been validated by looking at the differences with respect to fully independent in situ observations and by performing a wavenumber spectrum analysis. Despite minor progress being obtained in terms of root mean square of the differences with respect to in situ observations, a significant improvement in terms of effective spatial resolution is obtained with the new technique with respect to presently available SSS observation-based analyses.