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Evaluation and restructuring of gridded precipitation data over the Greater Mekong Subregion
Author(s) -
Chen ChiaJeng,
Senarath Sharika U. S.,
DimaWest Ioana M.,
Marcella Marc P.
Publication year - 2017
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.4696
Subject(s) - cru , precipitation , climatology , environmental science , meteorology , quantitative precipitation estimation , geography , geology
Precipitation measurements in the Mekong River Basin ( MRB ) are full of variability due to this domain's varied weather systems, climate conditions, elevation, and specific land–atmosphere interactions. This study provides an in‐depth evaluation of the differences between four gridded precipitation products [i.e. Asian Precipitation—Highly‐Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) , Tropical Rainfall Measuring Mission (TRMM) , CPC MORPHing technique (CMORPH) , and Climatic Research Unit (CRU) ] over the Greater Mekong Subregion. Precipitation data from a total of 242 stations in this domain are used to investigate the quality of the gridded products. Our analysis verifies that gauge‐based APHRODITE exhibits the highest correlations with the station data as well as the highest probability of detection of daily precipitation. The false alarm ratio, on the other hand, is slightly in favour of TRMM and CMORPH . Subtracting APHRODITE (as baseline) from TRMM and CMORPH reveals the spatial and frequency distribution of potential biases. The results indicate that TRMM appears to have a wet bias in most areas, while CMORPH shows no similar or consistent bias over APHRODITE . To utilize the higher accuracy of APHRODITE and the finer spatial and temporal footprints of CMORPH , a new restructuring algorithm is introduced in this study. The algorithm is capable of eliminating biases and possible artefacts associated with CMORPH while resolving the resolution discrepancies between the two data sets.