Downscaling CHIRPS precipitation data: an artificial neural network modelling approach

The Climate Hazards Group Infrared Precipitation with Station data CHIRPS is a high-resolution climatic database of precipitation embracing monthly precipitation climatology, quasi-global geostationary thermal infrared satellite observations from the Tropical Rainfall Measuring Mission TRMM 3B42 product, atmospheric model rainfall fields from National Oceanic and Atmospheric Administration – Climate Forecast System NOAA CFS, and precipitation observations from various sources. The key difference with all other existing precipitation databases is the high-resolution of the available data, since the inherent 0.05° resolution is a rather unique threshold. Monthly data for the period from January 1999 to December 2012 were processed in the present research. The main aim of this article is to propose a novel downscaling method in order to attain high resolution 1 km × 1 km precipitation datasets, by correlating the CHIRPS dataset with altitude information and the normalized difference vegetation index from satellite images at 1 km × 1 km, utilizing artificial neural network models. The final result was validated with precipitation measurements from the rain gauge network of the Cyprus Department of Meteorology.