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dc.contributor.authorDerin, Yagmuren_US
dc.contributor.authorAnagnostou, Emmanouilen_US
dc.contributor.authorBerne, Alexisen_US
dc.contributor.authorBorga, Marcoen_US
dc.contributor.authorBoudevillain, Briceen_US
dc.contributor.authorBuytaert, Wouteren_US
dc.contributor.authorChang, Che-Haoen_US
dc.contributor.authorChen, Haonanen_US
dc.contributor.authorDelrieu, Guyen_US
dc.contributor.authorHsu, Yung Chiaen_US
dc.contributor.authorLavado-Casimiro, Waldoen_US
dc.contributor.authorManz, Bastianen_US
dc.contributor.authorMoges, Semuen_US
dc.contributor.authorNikolopoulos, Efthymios I.en_US
dc.contributor.authorSahlu, Dejeneen_US
dc.contributor.authorSalerno, Francoen_US
dc.contributor.authorRodriguez-Sanchez, Juan-Pabloen_US
dc.contributor.authorVergara, Humberto J.en_US
dc.contributor.authorYilmaz, Koray K.en_US
dc.date.accessioned2020-02-02T23:54:35Z-
dc.date.available2020-02-02T23:54:35Z-
dc.date.issued2019-12-02en_US
dc.identifier.urihttp://dx.doi.org/10.3390/rs11242936en_US
dc.identifier.urihttp://hdl.handle.net/11536/153546-
dc.description.abstractThe great success of the Tropical Rainfall Measuring Mission (TRMM) and its successor Global Precipitation Measurement (GPM) has accelerated the development of global high-resolution satellite-based precipitation products (SPP). However, the quantitative accuracy of SPPs has to be evaluated before using these datasets in water resource applications. This study evaluates the following GPM-era and TRMM-era SPPs based on two years (2014-2015) of reference daily precipitation data from rain gauge networks in ten mountainous regions: Integrated Multi-SatellitE Retrievals for GPM (IMERG, version 05B and version 06B), National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center Morphing Method (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Multi-Source Weighted-Ensemble Precipitation (MSWEP), which represents a global precipitation data-blending product. The evaluation is performed at daily and annual temporal scales, and at 0.1 deg grid resolution. It is shown that GSMaPV07 surpass the performance of IMERGV06B Final for almost all regions in terms of systematic and random error metrics. The new orographic rainfall classification in the GSMaPV07 algorithm is able to improve the detection of orographic rainfall, the rainfall amounts, and error metrics. Moreover, IMERGV05B showed significantly better performance, capturing the lighter and heavier precipitation values compared to IMERGV06B for almost all regions due to changes conducted to the morphing, where motion vectors are derived using total column water vapor for IMERGV06B.en_US
dc.language.isoen_USen_US
dc.subjectsatellite-based precipitation producten_US
dc.subjectcomplex terrainen_US
dc.subjectvalidationen_US
dc.titleEvaluation of GPM-era Global Satellite Precipitation Products over Multiple Complex Terrain Regionsen_US
dc.typeArticleen_US
dc.identifier.doi10.3390/rs11242936en_US
dc.identifier.journalREMOTE SENSINGen_US
dc.citation.volume11en_US
dc.citation.issue24en_US
dc.citation.spage0en_US
dc.citation.epage0en_US
dc.contributor.department防災與水環境研究中心zh_TW
dc.contributor.departmentDisaster Prevention and Water Environment Research Centeren_US
dc.identifier.wosnumberWOS:000507333400052en_US
dc.citation.woscount0en_US
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