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Multiregional Satellite Precipitation Products Evaluation over Complex Terrain
Date
2016-06-01
Author
Derin, Yagmur
Anagnostou, Emmanouil
Berne, Alexis
BORGA, Marco
BOUDEVILLAIN, Brice
BUYTAERT, Wouter
CHANG, Che-Hao
DELRIEU, Guy
HONG, Yang
HSU, Yung Chia
LAVADO-CASIMIRO, Waldo
MANZ, Bastian
MOGES, Semu
NIKOLOPOULOS, Efthymios I.
SAHLU, Dejene
SALERNO, Franco
RODRIGUEZ-SANCHEZ, Juan-Pablo
VERGARA, Humberto J.
Yılmaz, Koray Kamil
Metadata
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This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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An extensive evaluation of nine global-scale high-resolution satellite-based rainfall (SBR) products is performed using aminimumof 6 years (within the period of 2000-13) of reference rainfall data derived from rain gauge networks in nine mountainous regions across the globe. The SBR products are compared to a recently released global reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF). The study areas include the eastern Italian Alps, the Swiss Alps, the western Black Sea of Turkey, the French Cevennes, the Peruvian Andes, the Colombian Andes, the Himalayas over Nepal, the Blue Nile in East Africa, Taiwan, and the U.S. Rocky Mountains. Evaluation is performed at annual, monthly, and daily time scales and 0.258 spatial resolution. The SBR datasets are based on the following retrieval algorithms: Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA), the NOAA/Climate Prediction Center morphing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN), and Global Satellite Mapping of Precipitation (GSMaP). SBRproducts are categorized into those that include gauge adjustment versus unadjusted. Results show that performance of SBR is highly dependent on the rainfall variability. Many SBR products usually underestimate wet season and overestimate dry season precipitation. The performance of gauge adjustment to the SBR products varies by region and depends greatly on the representativeness of the rain gauge network.
Subject Keywords
Atmospheric Science
URI
https://hdl.handle.net/11511/36057
Journal
JOURNAL OF HYDROMETEOROLOGY
DOI
https://doi.org/10.1175/jhm-d-15-0197.1
Collections
Department of Geological Engineering, Article