CNRS, DELEGATION REGIONALE ALPES (FR)
Quantitative precipitation estimate is one vital input to meteorologists, hydrologic scientists, water resources managers, and environmental legislators. Yet, accurate measurement of precipitation over the relevant space and time scales remains a challenge. Soil moisture can be seen as the trace of the precipitation and, consequently, can be useful for providing a way to estimate rainfall accumulation or at least a new constrain to rainfall algorithms.
In this context, the objective of the ‘SMOS+RAINFALL’ project is to ingest satellite soil moisture information derived from ASCAT, SMOS and SMAP into the latest state-of-the-art satellite precipitation products like those derived from the Global Precipitation Measurement mission (GPM) to enhance rainfall observation accuracy over land.
Two main approaches are considered in the project: 1) the Soil Moisture to Rain (SM2RAIN) approach which retrieves rainfall information from satellite soil moisture by inverting the soil water balance equation and then merge it with the Integrated Multi-satellitE Retrievals for GPM (IMERG) Early Run version via an Optimal Linear Interpolation approach and 2) Precipitation Inferred from Soil Moisture (PrISM) approach which is based on a particle filter data assimilation.
Remote Sensing (2022)
Journal of Hydrology (2021)
Hydrology and Earth System Science (2020)
Plos One (2020)
Remote Sensing (2020)
Scientific Reports (2020)
Advances in Water Resources (2017)
Remote Sensing of Environment (2017)