A Downscaling Method of TRMM Satellite Precipitation Based on Geographically Neural Network Weighted Regression: A Case Study in Sichuan Province, China

被引:1
|
作者
Zheng, Ge [1 ,2 ]
Zhang, Nan [3 ]
Zhang, Laifu [1 ,2 ]
Chen, Yijun [1 ,2 ]
Wu, Sensen [1 ,2 ]
机构
[1] Zhejiang Univ, Sch Earth Sci, 38 Zheda Rd, Hangzhou 310027, Peoples R China
[2] Zhejiang Prov Key Lab Geog Informat Sci, Hangzhou 310028, Peoples R China
[3] China Highway Engn Consulting Grp Co Ltd, Beijing 100089, Peoples R China
基金
中国国家自然科学基金;
关键词
precipitation; spatial downscaling; geographically neural network weighted regression; spatial non-stationarity; complex nonlinearity; BASIN; NDVI;
D O I
10.3390/atmos15070792
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Spatial downscaling is an effective way to improve the spatial resolution of precipitation products. However, the existing methods often fail to adequately consider the spatial heterogeneity and complex nonlinearity between precipitation and surface parameters, resulting in poor downscaling performance and inaccurate expression of regional details. In this study, we propose a precipitation downscaling model based on geographically neural network weighted regression (GNNWR), which integrates normalized difference vegetation index, digital elevation model, land surface temperature, and slope data to address spatial heterogeneity and complex nonlinearity. We explored the spatiotemporal trends of precipitation in the Sichuan region over the past two decades. The results show that the GNNWR model outperforms common methods in downscaling precipitation for the four distinct seasons, achieving a maximum R2 of 0.972 and a minimum RMSE of 3.551 mm. Overall, precipitation in Sichuan Province exhibits a significant increasing trend from 2001 to 2019, with a spatial distribution pattern of low in the northwest and high in the southeast. The GNNWR downscaled results exhibit the strongest correlation with observed data and provide a more accurate representation of precipitation spatial patterns. Our findings suggest that GNNWR is a practical method for precipitation downscaling considering its high accuracy and model performance.
引用
收藏
页数:16
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