In:
Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 22, No. 7 ( 2018-07-10), p. 3685-3699
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1607-7938
DOI:
10.5194/hess-22-3685-2018
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2018
detail.hit.zdb_id:
2100610-6
Permalink
