文献类型： 外文期刊

作者： Ma, Z. Q. ¹ ; Ghent, D. ³ ; Tan, X. ¹ ; He, K. ⁴ ; Li, H. Y. ⁵ ; Han, X. Z. ⁶ ; Huang, Q. T. ⁷ ; Peng, J. ⁸ ;

作者机构： 1.Peking Univ, Sch Earth & Space Sci, Inst Remote Sensing & Geog Informat Syst, Beijing, Peoples R China

2.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China

3.Univ Leicester, Dept Phys & Astron, Leicester, Leics, England

4.Univ Connecticut, Dept Civil & Environm Engn, Storrs, CT 06269 USA

5.Jiangxi Univ Finance & Econ, Sch Tourism & Urban Management, Dept Land Resource Management, Nanchang, Jiangxi, Peoples R China

6.China Meteorol Adm, Natl Satellite Meteorol Ctr, Beijing, Peoples R China

7.Guangxi Acad Agr Sci, Inst Agr Sci & Technol Informat, Nanning, Peoples R China

8.Tarim Univ, Coll Plant Sci, Alar, Peoples R China

关键词： precipitation; PERSIANN-CDR; data mining; calibration; long term; Tibetan Plateau

期刊名称：EARTH AND SPACE SCIENCE （ 影响因子：2.9； 五年影响因子：3.641 ）

ISSN：

年卷期： 2019 年 6 卷 11 期

页码：

收录情况： SCI

摘要： The World Meteorological Organization stipulates a minimum of 30 years of historical data is needed to obtain meaningful results in climatological research. However, large numbers of studies have explored downscaling approaches based on the TRMM Multi-Satellite Precipitation Analysis (TMPA) data, which span only from 1998 to the present, to obtain the precipitation estimates (similar to 1-km resolution). The main aim of the present study was to develop a new method for obtaining long-term (>30 years) precipitation estimates at similar to 1-km resolution and to apply that method to a region with complex topography, the Tibetan Plateau. First, PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record) data were used for downscaling. Considering the characteristics of the PERSIANN-CDR data, a new downscaling-calibration procedure utilizing a combination of a spatial data mining downscaling algorithm (Cubist) and a geographical ratio analysis calibration method was proposed. We found that (1) both the original PERSIANN-CDR data (Bias similar to 40.79%) and the downscaled results before calibration (Bias similar to 26.78%) overestimated the precipitation compared with ground observations; (2) the final downscaled results based on the PERSIANN-CDR data after calibration were close to the ground observations (Bias similar to 5%); (3) compared to the results interpolated based on the PERSIANN-CDR data (E <-1.0), both the downscaling procedure and calibration procedure contributed significantly to the accuracy of the final downscaled results (E similar to 0.83). These findings suggest that the proposed downscaling-calibration procedure has great potential as an approach for retrieving long-term precipitation estimates (similar to 1-km resolution) over the Tibetan Plateau.