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.
Highlights
As a fundamental factor in the water and energy cycle, precipitation data with highresolution and accuracy play an important role in hydrological, meteorological, and ecological studies [1,2]
(3) The Geographically Weighted Regression (GWR)-MF combined with the GWR and Multifractal Random Cascade (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 results show that the three adopted downscaling methods for the monthly 10 km resolution satellite precipitation cannot significantly enhance the sub-pixel heterogeneity according to the similar statistical index (CC/Bias), even in the GWR/MFRC results
Summary
As a fundamental factor in the water and energy cycle, precipitation data with highresolution and accuracy play an important role in hydrological, meteorological, and ecological studies [1,2]. Precipitation data are obtained from rain gauges or ground weather radars. The interpolation of rain gauges cannot always attain the necessary accuracy due to the sparse distribution of stations, especially in mountainous and remote areas, and the precipitation detected by ground-based radar faces challenges in practical applications because of its limited range and high uncertainties [1,3–6]. As the successor of TRMM/TMPA, the more advanced GPM/IMERG products have 0.1◦ × 0.1◦ and 0.5 h resolutions and spatial coverage between 60◦ N and 60◦ S, so the TRMM/TMPA data will no longer be produced and released after the TMPAto-IMERG transition is completed in mid-2019 [9]. The spatial resolution of GPM/IMERG is still too coarse for relevant research and applications at the regional scale; and it is essential to downscale the GPM/IMERG precipitation products to a finer resolution (1 km × 1 km or 0.01◦ × 0.01◦ generally) and while preserving their accuracy
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