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Spatial downscaling of TRMM 3B43 precipitation considering spatial heterogeneity

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The availability of accurate precipitation data with high spatial resolution is deemed necessary for many types of hydrological, meteorological, and environmental applications. The Tropical Rainfall Measuring Mission (TRMM) data sets can provide effective precipitation information, but at coarse resolution (0.25°), so it is very important to improve their resolution. There is a strong relationship between precipitation and other environment variables (e.g. vegetation and topography). The existing precipitation-downscaling methods attempt to describe this relationship by using a uniform empirical model. However, in the real world, the relationship is disturbed due to the influence of certain factors such as soil type, hydrological conditions, and human activities. In this study, a new downscaling method considering this spatial heterogeneity was proposed to downscale version 7 of the TRMM 3B43 precipitation product, which assumes that the relationship varies spatially but is the same in a local region. At a spatial resolution of 0.25°, the spatially varying relationship among TRMM, normalized difference vegetation index (NDVI), and digital elevation model (DEM) is explored by using a local regression analysis approach known as geographically weighted regression (GWR), but this relationship is the same in a pixel of 0.25° × 0.25°. The derived relationship is used to construct the precipitation downscaling model, which then produces 1 km downscaled precipitation data. The existing and proposed downscaling methods were both tested in North China for 2008–2011. The accuracy of the downscaled precipitation was validated by comparing it with observed precipitation data from 49 meteorological stations located in the study area. The results show that GWR is more suitable to capture the relationship among TRMM, DEM, and NDVI (minimum R 2 = 0.93). Compared with the existing downscaling method, the proposed method, which consistently showed increased R 2 (e.g. from 0.80 to 0.82 in 2011) and reduced RMSE (e.g. from 125.4 mm to 91 mm in 2011) in all four years, can more accurately produce downscaled precipitation data.
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Document Type: Research Article

Affiliations: 1: Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, Henan University, Kaifeng, 475004, Henan Province, China 2: College of Computer and Information Engineering, Henan University, Kaifeng, 475004, Henan Province, China 3: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, China 4: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430072, China

Publication date: May 3, 2014

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