SPATIAL DOWNSCALING OF GPM IMERG V06 GRIDDED PRECIPITATION USING MACHINE LEARNING ALGORITHMS

Abstract. According to recent studies, Remote sensing data plays a significant role in filling gaps in the poor gauge station, particularly at high elevations and with complex underlying surface features. In order to provide high-resolution precipitation estimates over the poor gauge and with complex terrain areas, downscaling low-resolution satellite precipitation estimates using various environmental variables. In this paper, we tried to downscale the GPM IMERG V06 with a resolution of (0.1° × 0.1°) nearly 10km to (1km × 1 km) using four machine learning algorithms namely, Decision Trees, Multiple Linear Regression, Support Vector Regressor and random forest. Vegetation indices Normalized difference in vegetation index (NDVI), Topography, Land Surface Temperature (LST), and latitude and longitude. This framework can downscale the 0.1° resolution of the GPM IMERG precipitation product to 1 km, by determining the importance of features, and automatically optimizing the model parameters. Additionally, ground recorded data from rain gauge stations have validated downscaled precipitation products. Spatial downscaling can generally increase the accuracy of GPM IMERG gridded precipitation data and results reveal that spatial downscaling is an acceptable way of investigating the precipitation over Taiwan.