Abstract
As a fundamental component in material and energy circulation, precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. Since satellite measured precipitation is often too coarse for practical applications, it is essential to develop spatial downscaling algorithms. In this study, we investigated two downscaling algorithms based on the Multiple Linear Regression (MLR) and the Geographically Weighted Regression (GWR), respectively. They were employed to downscale annual and monthly precipitation obtained from the Global Precipitation Measurement (GPM) Mission in Hengduan Mountains, Southwestern China, from 10 km × 10 km to 1 km × 1 km. Ground observations were then used to validate the accuracy of downscaled precipitation. The results showed that (1) GWR performed much better than MLR to regress precipitation on Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM); (2) coefficients of GWR models showed strong spatial nonstationarity, but the spatial mean standardized coefficients were very similar to standardized coefficients of MLR in terms of intra-annual patterns: generally NDVI was positively related to precipitation when monthly precipitation was under 166 mm; DEM was negatively related to precipitation, especially in wet months like July and August; contribution of DEM to precipitation was greater than that of NDVI; (3) residuals’ correction was indispensable for the MLR-based algorithm but should be removed from the GWR-based algorithm; (4) the GWR-based algorithm rather than the MLR-based algorithm produced more accurate precipitation than original GPM precipitation. These results indicated that GWR is a promising method in satellite precipitation downscaling researches and needed to be further studied.
Highlights
As a fundamental component in material and energy circulation, precipitation is of great significance for hydrological, meteorological, and ecological studies (e.g., [1,2,3])
Jia et al [14] proposed a downscaling algorithm based on Multiple Linear Regression (MLR), which is expressed as PLR = βNDVINDVILR + βDEMDEMLR + β0 + εLR, (1)
P values of February to June were relatively larger, and the corresponding R2 was smaller than 0.06, indicating that no more than 6% of variances of Global Precipitation Measurement (GPM) precipitation could be interpreted by MLR models from February to June
Summary
As a fundamental component in material and energy circulation, precipitation is of great significance for hydrological, meteorological, and ecological studies (e.g., [1,2,3]). Spatial distribution of precipitation is obtained through rain gauge data interpolation [4]. Interpolation methods are greatly limited over mountainous regions due to the sparse rain gauge network [5, 6]. Gridded satellite precipitation datasets provide reliable estimations of precipitation reflecting more spatial distribution than rain gauge data. For regional scale applications, they are often too coarse to be used in hydrological, meteorological, or ecological studies [14, 15]. It is essential to develop downscaling algorithms for satellite datasets to improve their resolutions as well as accuracy
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