A Downscaling-Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging

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.

Automated summary

This study proposes a downscaling-merging scheme based on random forest and cokriging, which efficiently generates high-resolution and high-quality daily precipitation data in a large area. The random forest model can accurately spatially downscale GPM daily precipitation data, retaining the accuracy of the original data and greatly improving their spatial details; moreover, the cokriging method significantly enhances the accuracy of the downscaled GPM daily precipitation data.