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The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison

CNRS, IRD, Univ. Grenoble Alpes, Grenoble INP, IGE, F-38000 Grenoble, France
Laboratoire d’Aérologie, Université Toulouse Paul Sabatier, CNRS, F-31400 Toulouse, France
CIRAD UMR TETIS, Maison de la Télédétection, 500 rue J.F. Breton, F-34093 Montpellier, France
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Research Institute for Geo-Hydrological Protection, Via Madonna Alta 126, 06128 Perugia, Italy
Centre de Recherches de Climatologie/Biogéosciences, UMR 6282 CNRS, Université Bourgogne Franche-Comté, 21000 Dijon, France
EO Science, Applications and Climate Department, Largo Galileo Galilei, 1, 00044 Frascati, Italy
CESBIO (CNRS/UPS/IRD/CNES), 18 av. Edouard Belin, bpi 2801, CEDEX 9, 31401 Toulouse, France
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 481;
Received: 30 December 2019 / Revised: 23 January 2020 / Accepted: 29 January 2020 / Published: 3 February 2020
(This article belongs to the Special Issue New Outstanding Results over Land from the SMOS Mission)
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. View Full-Text
Keywords: precipitation; soil moisture; Africa; satellite rainfall products; comparison precipitation; soil moisture; Africa; satellite rainfall products; comparison
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MDPI and ACS Style

Pellarin, T.; Román-Cascón, C.; Baron, C.; Bindlish, R.; Brocca, L.; Camberlin, P.; Fernández-Prieto, D.; Kerr, Y.H.; Massari, C.; Panthou, G.; Perrimond, B.; Philippon, N.; Quantin, G. The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison. Remote Sens. 2020, 12, 481.

AMA Style

Pellarin T, Román-Cascón C, Baron C, Bindlish R, Brocca L, Camberlin P, Fernández-Prieto D, Kerr YH, Massari C, Panthou G, Perrimond B, Philippon N, Quantin G. The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison. Remote Sensing. 2020; 12(3):481.

Chicago/Turabian Style

Pellarin, Thierry, Carlos Román-Cascón, Christian Baron, Rajat Bindlish, Luca Brocca, Pierre Camberlin, Diego Fernández-Prieto, Yann H. Kerr, Christian Massari, Geremy Panthou, Benoit Perrimond, Nathalie Philippon, and Guillaume Quantin. 2020. "The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison" Remote Sensing 12, no. 3: 481.

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