Evaluating the Performance of Light Gradient Boosting Machine in Merging Multiple Satellite Precipitation Products Over South Korea

Precipitation information with high accuracy plays a crucial role in hydrology and water resources management. With the advance in technology, satellite precipitation products (SPPs) provide an unprecedented opportunity for monitoring the spatial and temporal variation of precipitation from space. However, SPPs still present a low performance with high uncertainty. To overcome this problem, the current study aims to produce a new reanalysis of precipitation data by integrating information from observation data with multiple SPPs over South Korea under the aid of a fast and high-performance machine learning-based, namely a light gradient boosting machine. In addition, other statistical merging methods were also carried out to highlight the robustness of the machine learning-based algorithm. To examine the accuracy of merging precipitation products, observed data from 64 automated synoptic observation system rain gauge stations were collected and compared with merging precipitation products. A high agreement between merging precipitation data generated from the machine learning-based approach with observation was witnessed through several continuous criteria and categorical indicators. The results from this study point out that light gradient boosting machine not only has the capability in merging multi-sources precipitation but also it could provide extraordinary rainfall information for the region of interest, especially in areas with low observed station density.