An updated moving window algorithm for hourly-scale satellite precipitation downscaling: A case study in the Southeast Coast of China

Abstract

Accurate gridded precipitation products with both finer tempo-spatial resolutions are critical for various scientific communities (e.g., hydrology, meteorology, climatology, and agriculture). Downscaling on coarse satellite-based rainfall estimates is an optimal approach to obtain such datasets. The Integrated Multi-satellitE Retrivals for Global Precipitation Measurement (GPM) (IMERG) data provides the “best” satellite-based precipitation estimates at half-hourly/0.1° scales, while its spatial resolution is still coarse for certain hydrometeorology research. To acquire hourly downscaled precipitation estimates based on IMERG, there are two great challenges: (1) limited rainfall-related environmental variables (0.01°×0.01°, hourly) used to downscale the IMERG data; and (2) far few rainfall pixels used for regressing traditional relationships between precipitation and environmental variables. In this case, most traditional or commonly-used regression/empirical models and the state-of-art machine learning algorithms are not suitable to cater these requirements. Therefore, we proposed a new strategy to obtain hourly downscaled precipitation estimates based on IMERG called Geographically Moving Window Weight Disaggregation Analysis (GMWWDA). Additionally, we explored multiple cloud properties, including cloud effective radius (CER), cloud top height (CTH), cloud top temperature (CTT) and cloud optical thickness (COT), as covariates to downscale IMERG using GMWWDA method, and concluded as follows: (1) the downscaled results (0.01° × 0.01°, hourly) based on the above mentioned cloud properties outperformed the original IMERG data; (2) the downscaled results based on CER performed better than those based on CTH, CTT and COT, respectively; (3) the accuracy of satellite-based precipitation products pose significant effects on those of the downscaled results. This study provides a great potential solution for generating satellite-based precipitation dataset with finer spatio-temporal resolutions.