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

Abstract

Abstract 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 (~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 ~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 ~40.79%) and the downscaled results before calibration (Bias ~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 ~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 ~0.83). These findings suggest that the proposed downscaling‐calibration procedure has great potential as an approach for retrieving long‐term precipitation estimates (~1‐km resolution) over the Tibetan Plateau.

Keywords

• precipitation
• PERSIANN‐CDR
• data mining
• calibration
• long term
• Tibetan Plateau