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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-592
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
08 Nov 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).
Precipitation downscaling using a probability-matching approach and geostationary infrared data: An evaluation over six climate regions
Ruifang Guo1,2, Yuanbo Liu1, Han Zhou1,2, and Yaqiao Zhu3 1Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, No. 73 East Beijing Road, Nanjing 210008, China
2University of Chinese Academy of Sciences, No. 19 Yuquan Road, Beijing 100049, China
3College of Urban and Environmental Sciences, Hubei Normal University, No.11 Cihu road,Huangshi 435002, China
Abstract. Precipitation is one of the most important components of the global water cycle. Precipitation data at high spatial 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 (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 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.

Citation: Guo, R., Liu, Y., Zhou, H., and Zhu, Y.: Precipitation downscaling using a probability-matching approach and geostationary infrared data: An evaluation over six climate regions, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-592, in review, 2017.
Ruifang Guo et al.
Ruifang Guo et al.
Ruifang Guo et al.

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Short summary
Existing satellite products are often insufficient for use in small-scale (< 10 km) hydrological and meteorological studies. We propose a new approach based cumulative distribution of frequency to downscale satellite precipitation products with geostationary (GEO) data. This paper uses the CMORPH and FY2-E GEO data to examine the approach at six different climate regions. The downscaled precipitation performed better for convective systems.
Existing satellite products are often insufficient for use in small-scale ( 10 km) hydrological...
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