CNRS, DELEGATION REGIONALE ALPES (FR)
Several recent studies have concluded that climate change causes major changes in the global water cycle. There is increasing evidence that part of the multi-decadal trends observed on the sea surface salinity (SSS) are due to changes in the global water cycle, e.g. the western tropical Pacific has become fresher and the subtropical North Atlantic has become saltier. Given that most of the evaporation and precipitations occur over the ocean, a main challenge for studying the global water cycle is the monitoring of freshwater fluxes over the ocean. However monitoring these fluxes is difficult, in large part because precipitation is a very variable and intermittent process. Hence, it has been shown that the measure of sea surface salinity (SSS) provides an indirect but integrated information on air-sea freshwater flux that might be powerful for monitoring changes in the water cycle. This was one of the major motivations for observing SSS from space and two satellite salinity missions: the Soil Moisture and Ocean Salinity (SMOS) and the Aquarius missions, which now have provided global SSS fields over the last several years.
STSE SMOS+ Rainfall aims to exploit potential offered by SMOS L1 and L2 measurements to infer or enhance rainfall information over the global ocean, as well as define the potential contribution of SMOS to current efforts to retrieve rainfall information from satellites. The project has developed a suitable and scientifically sound methodological approach to exploit SMOS observations to retrieve or enhance existing rainfall information, by estimating the SSS anomalies caused by rainfall and calibrating a model that relates such anomalies to rainfall rates. The novel methodology relies only in SMOS data to obtain rainfall estimations, if well additional satellite-derived rainfall data has been used for both calibration and evaluation of the product, namely SSMI and IMERG. The project has also defined the range of validity, error structure and uncertainty of these retrievals and created a roadmap towards their improvement and integration into other existing rainfall products. Current results show that SMOS-derived rainfall performs better over ocean than some of the existing radiometer-derived products, and it has consistent results when comparing with IMERG. A global algorithm is currently under development to extend the current processor to a larger scale than the ones contemplated in the study previously. This study proves that SMOS can contribute to the increase of knowledge about the water cycle and that L-band missions can play a significant role in the acquisition of rainfall data at global scale.
Remote Sensing of Environment
Quarterly Journal of the Royal Meteorological Society