The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison
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 th...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/3/481 |
| _version_ | 1798031124382875648 |
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| author | Thierry Pellarin 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 Guillaume Quantin |
| author_facet | Thierry Pellarin 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 Guillaume Quantin |
| author_sort | Thierry Pellarin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T19:51:08Z |
| format | Article |
| id | doaj.art-3c0ee5f99f6946d0af20bf87915f4360 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T19:51:08Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3c0ee5f99f6946d0af20bf87915f43602022-12-22T04:06:17ZengMDPI AGRemote Sensing2072-42922020-02-0112348110.3390/rs12030481rs12030481The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and ComparisonThierry Pellarin0Carlos Román-Cascón1Christian Baron2Rajat Bindlish3Luca Brocca4Pierre Camberlin5Diego Fernández-Prieto6Yann H. Kerr7Christian Massari8Geremy Panthou9Benoit Perrimond10Nathalie Philippon11Guillaume Quantin12CNRS, IRD, Univ. Grenoble Alpes, Grenoble INP, IGE, F-38000 Grenoble, FranceCNRS, IRD, Univ. Grenoble Alpes, Grenoble INP, IGE, F-38000 Grenoble, FranceCIRAD UMR TETIS, Maison de la Télédétection, 500 rue J.F. Breton, F-34093 Montpellier, FranceNASA Goddard Space Flight Center, Greenbelt, MD 20771, USAResearch Institute for Geo-Hydrological Protection, Via Madonna Alta 126, 06128 Perugia, ItalyCentre de Recherches de Climatologie/Biogéosciences, UMR 6282 CNRS, Université Bourgogne Franche-Comté, 21000 Dijon, FranceEO Science, Applications and Climate Department, Largo Galileo Galilei, 1, 00044 Frascati, ItalyCESBIO (CNRS/UPS/IRD/CNES), 18 av. Edouard Belin, bpi 2801, CEDEX 9, 31401 Toulouse, FranceResearch Institute for Geo-Hydrological Protection, Via Madonna Alta 126, 06128 Perugia, ItalyCNRS, IRD, Univ. Grenoble Alpes, Grenoble INP, IGE, F-38000 Grenoble, FranceCNRS, IRD, Univ. Grenoble Alpes, Grenoble INP, IGE, F-38000 Grenoble, FranceCNRS, IRD, Univ. Grenoble Alpes, Grenoble INP, IGE, F-38000 Grenoble, FranceCNRS, IRD, Univ. Grenoble Alpes, Grenoble INP, IGE, F-38000 Grenoble, FranceNear 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.https://www.mdpi.com/2072-4292/12/3/481precipitationsoil moistureafricasatellite rainfall productscomparison |
| spellingShingle | Thierry Pellarin 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 Guillaume Quantin The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison Remote Sensing precipitation soil moisture africa satellite rainfall products comparison |
| title | The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison |
| title_full | The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison |
| title_fullStr | The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison |
| title_full_unstemmed | The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison |
| title_short | The Precipitation Inferred from Soil Moisture (PrISM) Near Real-Time Rainfall Product: Evaluation and Comparison |
| title_sort | precipitation inferred from soil moisture prism near real time rainfall product evaluation and comparison |
| topic | precipitation soil moisture africa satellite rainfall products comparison |
| url | https://www.mdpi.com/2072-4292/12/3/481 |
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