A new downscaling-integration framework for high-resolution monthly precipitation estimates: Combining rain gauge observations, satellite-derived precipitation data and geographical ancillary data

Abstract

Deriving high quality precipitation estimates at high spatial resolution is of prime importance for many hydrological, meteorological, and environmental investigations. Rain gauge observations and satellite-derived precipitation data are two main sources of precipitation estimates. Gauge observations are accurate and reliable, but are heavily point-based and sparse in areas of rugged or complex terrains. Satellite-derived precipitation products can cover large areas, but they are generally characterized by inherent bias. To optimize the use of both datasets, we propose in this paper, a downscaling-integration framework to generate high quality monthly precipitation datasets at 1 km spatial resolution by merging rain gauge observations and TRMM 3B43 products. Firstly, an area-to-point kriging (ATPK) approach is used to downscale the original TRMM product to 1 km, so as to ensure a fair comparison with rain gauge data. Then, the downscaled TRMM precipitation datasets are integrated with the gauge observations using geographically weighted regression kriging (GWRK). The geographical factors (i.e. longitude, latitude and elevation) are also used as auxiliary variables in the GWRK model. Applying this approach to an experiment conducted at the middle and lower reaches of the Yangtze River in China from 2001 to 2014 shows that: (1) the downscaled monthly TRMM precipitation data by ATPK are more accurate than the original TRMM estimates; (2) the GWRK model employing the downscaled TRMM precipitation data and geographical factors provides better monthly precipitation estimates than the conventional ordinary kriging (OK) interpolation and the commonly used merging methods (i.e. geographical difference analysis, GDA and kriging with external drift, KED); (3) the GWRK method reduces the influence of the inaccuracy (bias) of satellite-derived precipitation data on the precipitation estimates compared to GDA. The approach presented in this study has provided an efficient alternative for solving the scale mismatch problem between point-based gauge data and low resolution satellite data, and producing improved precipitation data at high spatial resolution.