Spatial Downscaling Model Combined with the Geographically Weighted Regression and Multifractal Models for Monthly GPM/IMERG Precipitation in Hubei Province, China

Abstract

High spatial resolution (1 km or finer) precipitation data fields are crucial for understanding the Earth’s water and energy cycles at the regional scale for applications. The spatial resolution of the Global Precipitation Measurement (GPM) mission (IMERG) satellite precipitation products is 0.1° (latitude) × 0.1° (longitude), which is too coarse for regional-scale analysis. This study combined the Geographically Weighted Regression (GWR) and the Multifractal Random Cascade (MFRC) model to downscale monthly GPM/IMERG precipitation products from 0.1° × 0.1° (approximately 11 km × 11 km) to 1 km in Hubei Province, China. This work’s results indicate the following: (1) The original GPM product can accurately express the precipitation in the study area, which highly correlates with the site data from 2015 to 2017 (R2 = 0.79) and overall presents the phenomenon of overestimation. (2) The GWR model maintains the precipitation field’s overall accuracy and smoothness, with even improvements in accuracy for specific months. In contrast, the MFRC model causes a slight decrease in the overall accuracy of the precipitation field but performs better in reducing the bias. (3) The GWR-MF combined with the GWR and MFRC model improves the observation accuracy of the downscaling results and reduces the bias value by introducing the MFRC to correct the deviation of GWR. The conclusion and analysis of this paper can provide a meaningful experience for 1 km high-resolution data to support related applications.

Keywords

• GPM/IMERG
• precipitation spatial downscaling
• geographically weighted regression
• multifractal theory