Precipitation downscaling using a probability-matching approach and geostationary infrared data: An evaluation over six climate regions

Abstract

Abstract. Precipitation is one of the most important components of the global water cycle. Precipitation data at high spatial 10 and temporal resolutions are crucial for basin-scale hydrological and meteorological studies. In this study, we proposed a cumulative distribution of frequency (CDF)-based downscaling method (DCDF) to obtain hourly 0.05°×0.05° precipitation data. The main hypothesis is that a variable with the same resolution of target data should produce a CDF that is similar to the reference data. The method was demonstrated using the 3 hourly 0.25°×0.25° Climate Prediction Center Morphing method (CMORPH) dataset and the hourly 0.05°×0.05° FY2-E Geostationary (GEO) Infrared (IR) temperature brightness 15 (Tb) data. Initially, power function relationships were established between precipitation rate and Tb for each 1°×1° region.Then the CMORPH data were downscaled to 0.05°×0.05°. The downscaled results were validated over diverse rainfall regimes in China. Within each rainfall regime, the fitting functions coefficients were able to implicitly reflect the characteristics of precipitation. Qualitatively, the downscaled estimates were able to capture more details about rainfall motions and changes. Quantitatively, the time series of the downscaled estimates were more similar to the rain gauge data 20 than the original CMORPH product at the daily scale. The downscaled estimates not only improved spatio-temporal resolutions, but also performed better (Bias: -7.35%~10.35%; correlation coefficient (CC): 0.48~0.60) than the CMORPH product (Bias: 20.82%~94.19%; CC: 0.31~0.59) over convective precipitating regions. The downscaled results performed as well as the CMORPH product over regions dominated with frontal rain systems and performed relatively poorly over mountainous or hilly areas where orographic rain systems dominate. 25