The Precipitation Inferred from Soil Moisture (PrISM) near real-time rainfall product: evaluation and comparison.

Thierry Pellarin (1) , Carlos Román-Cascón (1, 2) , Christian Baron (3, 4) , Rajat Bindlish (5) , Luca Brocca (6) , Pierre Camberlin (7) , Diego Fernández-Prieto (8) , Yann H. Kerr (9) , Christian Massari (6) , Gérémy Panthou (1) , Benoît Perrimond (1) , Nathalie Philippon (1) , Guillaume Quantin (1)

1 IGE - Institut des Géosciences de l’Environnement
2 LAERO - Laboratoire d'aérologie
3 UMR TETIS - Territoires, Environnement, Télédétection et Information Spatiale
4 Cirad-ES - Département Environnements et Sociétés
5 GSFC - NASA Goddard Space Flight Center
6 IRPI - Istituto di Ricerca per la Protezione Idrogeologica [Perugia]
7 BGS - Biogéosciences [UMR 6282]
8 Applications and Climate Department
9 CESBIO - Centre d'études spatiales de la biosphère

Near real-time precipitation is essential to many applications. In Africa, the lack of dense rain-gauge networks and ground weather radars makes the use of satellite precipitation products unavoidable. Despite major progresses in estimating precipitation rate from remote sensing measurements over the past decades, satellite precipitation products still suffer from quantitative uncertainties and biases compared to ground data. Consequently, almost all precipitation products are provided in two modes: a real-time mode (also called early-run or raw product) and a corrected mode (also called final-run, adjusted or post-processed product) in which ground precipitation measurements are integrated in algorithms to correct for bias, generally at a monthly timescale. This paper describes a new methodology to provide a near-real-time precipitation product based on satellite precipitation and soil moisture measurements. Recent studies have shown that soil moisture intrinsically contains information on past precipitation and can be used to correct precipitation uncertainties. The PrISM (Precipitation inferred from Soil Moisture) methodology is presented and its performance is assessed for five in situ rainfall measurement networks located in Africa in semi-arid to wet areas: Niger, Benin, Burkina Faso, Central Africa, and East Africa. Results show that the use of SMOS (Soil Moisture and Ocean Salinity) satellite soil moisture measurements in the PrISM algorithm most often improves the real-time satellite precipitation products, and provides results comparable to existing adjusted products, such as TRMM (Tropical Rainfall Measuring Mission), GPCC (Global Precipitation Climatology Centre) and IMERG (Integrated Multi-satellitE Retrievals for GPM), which are available a few weeks or months after their detection.

Mots clés: precipitation; soil moisture; Africa; satellite rainfall products; comparison

Domaines: Climatologie

Thierry Pellarin, Carlos Román-Cascón, Christian Baron, Rajat Bindlish, Luca Brocca, et al.. The Precipitation Inferred from Soil Moisture (PrISM) near real-time rainfall product: evaluation and comparison.. Remote Sensing, 2020, 12 (3), pp.481. ⟨10.3390/rs12030481⟩. ⟨hal-02531523⟩