Spatial Downscaling Model Combined with the Geographically Weighted Regression and Multifractal Models for Monthly GPM/IMERG Precipitation in Hubei Province, China
نویسندگان
چکیده
High spatial resolution (1 km or finer) precipitation data fields are crucial for understanding the Earth’s water and energy cycles at regional scale applications. The of Global Precipitation Measurement (GPM) mission (IMERG) satellite products is 0.1° (latitude) × (longitude), which too coarse regional-scale analysis. This study combined Geographically Weighted Regression (GWR) Multifractal Random Cascade (MFRC) model to downscale monthly GPM/IMERG from (approximately 11 km) 1 in Hubei Province, China. work’s results indicate following: (1) original GPM product can accurately express area, highly correlates with site 2015 2017 (R2 = 0.79) overall presents phenomenon overestimation. (2) GWR maintains field’s accuracy smoothness, even improvements specific months. In contrast, MFRC causes a slight decrease field but performs better reducing bias. (3) GWR-MF improves observation downscaling reduces bias value by introducing correct deviation GWR. conclusion analysis this paper provide meaningful experience high-resolution support related
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ژورنال
عنوان ژورنال: Atmosphere
سال: 2022
ISSN: ['2073-4433']
DOI: https://doi.org/10.3390/atmos13030476