Long-Term Precipitation Estimates Generated by a Downscaling-Calibration Procedure Over the Tibetan Plateau From 1983 to 2015

The World Meteorological Organization stipulates a minimum of 30 years of historical data is needed to obtain meaningful results in climatological research. However, large numbers of studies have explored downscaling approaches based on the TRMM Multi-Satellite Precipitation Analysis (TMPA) data, which span only from 1998 to the present, to obtain the precipitation estimates (similar to 1-km resolution). The main aim of the present study was to develop a new method for obtaining long-term (>30 years) precipitation estimates at similar to 1-km resolution and to apply that method to a region with complex topography, the Tibetan Plateau. First, PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record) data were used for downscaling. Considering the characteristics of the PERSIANN-CDR data, a new downscaling-calibration procedure utilizing a combination of a spatial data mining downscaling algorithm (Cubist) and a geographical ratio analysis calibration method was proposed. We found that (1) both the original PERSIANN-CDR data (Bias similar to 40.79%) and the downscaled results before calibration (Bias similar to 26.78%) overestimated the precipitation compared with ground observations; (2) the final downscaled results based on the PERSIANN-CDR data after calibration were close to the ground observations (Bias similar to 5%); (3) compared to the results interpolated based on the PERSIANN-CDR data (E <-1.0), both the downscaling procedure and calibration procedure contributed significantly to the accuracy of the final downscaled results (E similar to 0.83). These findings suggest that the proposed downscaling-calibration procedure has great potential as an approach for retrieving long-term precipitation estimates (similar to 1-km resolution) over the Tibetan Plateau.