标题: Spatial and Temporal Downscaling of TRMM Precipitation with Novel Algorithms

作者: Huihui Zhang, Hugo A Loáiciga, D A Ha, Qingyun Du

来源出版物: JOURNAL OF HYDROMETEOROLOGY 卷: 21 页码:1259-1278  DOI: 10.1175/JHM-D-19-0289.1 出版年: JUN 2020

摘要: Tropical Rainfall Measuring Mission (TRMM) satellite products constitute valuable precipitation data-sets over regions with sparse rain gauge networks. Downscaling is an effective approach to estimating the precipitation over ungauged areas with high spatial resolution. However, a large bias and low resolution of original TRMM satellite images constitute constraints for practical hydrologic applications of TRMM precipitation products. This study contributes two precipitation downscaling algorithms by exploring the nonstationarity relations between precipitation and various environment factors [daytime surface temperature (LTD), terrain slope, normalized difference vegetation index (NDVI), altitude, longitude, and latitude] to overcome bias and low-resolution constraints of TRMM precipitation. Downscaling of precipitation is achieved with the geographically weighted regression model (GWR) and the backward-propagation artificial neural networks (BP_ANN). The probability density function (PDF) algorithm corrects the bias of satellite precipitation data with respect to spatial and temporal scales prior to downscaling. The principal component analysis algorithm (PCA) provides an alternative method of obtaining accurate monthly rainfall estimates during the wet rainfall season that minimizes the temporal uncertainties and upscaling effects introduced by direct accumulation (DA) of precipitation. The performances of the proposed downscaling algorithms are assessed by downscaling the latest version of TRMM3B42 V7 datasets within Hubei Province from 0.258 (about 25 km) to 1-km spatial resolution at the monthly scale. The downscaled datasets are systematically evaluated with in situ observations at 27 rain gauges from the years 2005 through 2010. This paper's results demonstrate the bias correction is necessary before downscaling. The high-resolution precipitation datasets obtained with the proposed downscaling model with GWR relying on the NDVI and slope are shown to improve the accuracy of precipitation estimates. GWR exhibits more accurate downscaling results than BP_ANN coupled with the genetic algorithm (GA) in most dry and wet seasons.

地址:

[Huihui Zhang, Qingyun Du] School of Resources and Environmental Sciences, Wuhan University, Wuhan, China

[Huihui Zhang, Hugo A Loáiciga, D A Ha]Department of Geography, University of California, Santa Barbara, Santa Barbara, California

[D A Ha]School of Civil Engineering, Tianjin University, Tianjin, China, and Department of Geography

[Qingyun Du]Ministry of Education, and Key Laboratory of Digital Mapping and Land Information Application Engineering;Ministry of Natural Resources, Wuhan University, Wuhan, China

通讯作者地址: [Qingyun Du] School of Resources and Environmental Sciences, Wuhan University, Wuhan, China ;Ministry of Education, and Key Laboratory of Digital Mapping and Land Information Application Engineering;Ministry of Natural Resources, Wuhan University, Wuhan, China

电子邮箱：qydu@whu.edu.cn

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