Refined Spatialization of 10-Day Precipitation in China Based on GPM IMERG Data and Terrain Decomposition Using the BEMD Algorithm

Continuous high spatial-resolution 10-day precipitation data are essential for crop growth services and phenological research. In this study, we first use the bidimensional empirical mode decomposition (BEMD) algorithm to decompose the digital elevation model (DEM) data and obtain high-frequency (OR3), intermediate-frequency (OR5), and low-frequency (OR8) margin terrains. Then, we propose a refined precipitation spatialization model, which uses ground-based meteorological observation data, integrated multi-satellite retrievals for global precipitation measurement (GPM IMERG) satellite precipitation products, DEM data, terrain decomposition data, prevailing precipitation direction (PPD) data, and other multisource data, to construct China’s high-resolution 10-day precipitation data from 2001 to 2018. The decomposition results show mountainous terrain from fine to coarse scales; and the influences of altitude, slope, and aspect on precipitation are better represented in the model after topography is decomposed. Moreover, terrain decomposition data can be added to the model simulation to improve the quality of the simulation product; the simulation quality of the model in summer is better than that in spring and autumn, and is relatively poor in winter; and OR5 and OR8 can be improved in the simulation, with better OR5 and OR8 dynamically selected. In addition, preprocessing the data before precipitation spatialization is particularly important. For example, adding 0.01 to the 0 value of precipitation, multiplying the small value of precipitation less than 1 by 10, and performing the normal distributions transform (e.g., Yeo–Johnson) on the data can improve the simulation quality.