High-resolution bias-corrected precipitation data over South Siberia, Russia

Abstract

The accuracy of global hydrometeorological data is important for regional and global climate studies. We compared the ERA5 reanalysis monthly precipitation data of European Centre for Medium-range Weather Forecasts over South Siberia against observed precipitation data records from 132 ground weather stations for 1979–2015. The ERA5 reanalysis provides detailed fields reasonably reproducing features of mesoscale precipitation structures related to topography and synoptic-scale patterns. The linear correlation coefficients between reanalysis and weather station data are high but mean values are biased. The mean absolute error varies from −23 mm to 90 mm. A bias correction procedure was suggested to improve the precipitation data quality and reduce mean values error. The linear scaling coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Raw ERA5 data were scaled according to the derived scale coefficient, resulting in monthly maps of corrected reanalysis for South Siberia (CRSS). Multiply validation of bias correction was performed on the control test sets. The CRSS data fit the observation data very well. The mean error does not exceed 7 mm, the mean absolute error maximal value is 44 mm, and the mean relative error is less than 23%. The maximal biases are typical for mountain areas. The statistical downscaling methods applied in this study can be easily transferred to other regions where long-term data sets of observed precipitation are available. The obtained results are promising since the proposed simple correction scheme is more accurate and robust in reproducing monthly - precipitation values than global reanalysis data. CRSS reproduces the spatial variability of precipitation more precisely than can be done from the weather station observation network. The CRSS dataset will be useful for the study of extreme precipitation events and allow for more accurate hydrologic risk assessment at a regional level based on climate model results.