Paper Info
Title
A Downscaling-Merging Scheme For Improving Daily Spatial Precipitation Estimates Based On Random Forest And Cokriging
Abstract
High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling-merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01 degrees. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area.
Year
DOI
Venue
2021
10.3390/rs13112040
REMOTE SENSING
Keywords
DocType
Volume
GPM, spatial downscaling, random forest, daily precipitation, cokriging, precipitation data merging
Journal
13
Issue
Citations 
PageRank 
11
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Xin Yan100.34
Hua Chen200.68
Bingru Tian300.34
Sheng Sheng400.34
Jinxing Wang500.34
Jong-Suk Kim601.69