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Comparative Assessments of the Latest GPM Mission’s Spatially Enhanced Satellite Rainfall Products over the Main Bolivian Watersheds

Authors :
Yawar Hussain
Ramiro Pillco Zolá
Marie-Paule Bonnet
Jérémie Garnier
Frédéric Satgé
Alvaro Xavier
Franck Timouk
Centre d'études spatiales de la biosphère (CESBIO)
Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3)
Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP)
Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Université Paris-Sud - Paris 11 (UP11)
Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Toulouse III - Paul Sabatier (UT3)
Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP)
Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)
Source :
Remote Sensing, Remote Sensing, 2017, 9 (4), ⟨10.3390/rs9040369⟩, Remote Sensing, MDPI, 2017, 9 (4), ⟨10.3390/rs9040369⟩, Volume 9, Issue 4, Pages: 369
Publication Year :
2017
Publisher :
HAL CCSD, 2017.

Abstract

The new IMERG and GSMaP-v6 satellite rainfall estimation (SRE) products from the Global Precipitation Monitoring (GPM) mission have been available since January 2015. With a finer grid box of 0.1 degrees, these products should provide more detailed information than their latest widely-adapted (relatively coarser spatial scale, 0.25 degrees) counterpart. Integrated Multi-satellitE Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation version 6 (GSMaP-v6) assessment is done by comparing their rainfall estimations with 247 rainfall gauges from 2014 to 2016 in Bolivia. The comparisons were done on annual, monthly and daily temporal scales over the three main national watersheds (Amazon, La Plata and TDPS), for both wet and dry seasons to assess the seasonal variability and according to different slope classes to assess the topographic influence on SREs. To observe the potential enhancement in rainfall estimates brought by these two recently released products, the widely-used TRMM Multi-satellite Precipitation Analysis (TMPA) product is also considered in the analysis. The performances of all the products increase during the wet season. Slightly less accurate than TMPA, IMERG can almost achieve its main objective, which is to ensure TMPA rainfall measurements, while enhancing the discretization of rainy and non-rainy days. It also provides the most accurate estimates among all products over the Altiplano arid region. GSMaP-v6 is the least accurate product over the region and tends to underestimate rainfall over the Amazon and La Plata regions. Over the Amazon and La Plata region, SRE potentiality is related to topographic features with the highest bias observed over high slope regions. Over the TDPS watershed, the high rainfall spatial variability with marked wet and arid regions is the main factor influencing SREs.

Details

Language :
English
ISSN :
20724292
Database :
OpenAIRE
Journal :
Remote Sensing, Remote Sensing, 2017, 9 (4), ⟨10.3390/rs9040369⟩, Remote Sensing, MDPI, 2017, 9 (4), ⟨10.3390/rs9040369⟩, Volume 9, Issue 4, Pages: 369
Accession number :
edsair.doi.dedup.....f7734eac70647c7ab14d73d35ece74a2
Full Text :
https://doi.org/10.3390/rs9040369⟩