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Volume 22, issue 7 | Copyright
Hydrol. Earth Syst. Sci., 22, 3685-3699, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 10 Jul 2018

Research article | 10 Jul 2018

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 Ruifang Guo et al.
  • 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 propose 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 the 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. Quantitatively, 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. Qualitatively, at the daily scale, DCDF and CMORPH had nearly equivalent performances at the regional scale, and 79% DCDF may perform better than or nearly equivalently to CMORPH at the point (rain gauge) scale. The downscaled estimates were able to capture more details about rainfall motion and changes under the condition that DCDF performs better than or nearly equivalently to CMORPH.

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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 on the cumulative distribution of frequency to downscale satellite precipitation products with geostationary (GEO) data. This paper uses CMORPH and FY2-E GEO data to examine the approach in 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...