Statistical Bias Correction of Precipitation Forecasts Based on Quantile Mapping on the Sub-Seasonal to Seasonal Scale - Xiaomeng Li; Huan Wu; Nergui Nanding; Sirong Chen; Ying Hu; Lingfeng Li (Remote Sensing, 2023)
Accurate precipitation forecasting is challenging, especially on the sub-seasonal to seasonal scale (14–90 days) which mandates the bias correction. Quantile mapping (QM) has been employed as a universal method of precipitation bias correction as it is effective in correcting the distribution attributes of mean and variance, but neglects the correlation between the model and observation data and has computing inefficiency in large-scale applications. In this study, a quantile mapping of matching precipitation threshold by time series (MPTT-QM) method was proposed to tackle these problems. The MPTT-QM method was applied to correct the FGOALS precipitation forecasts on the 14-day to 90-day lead times for the Pearl River Basin (PRB), taking the IMERG-final product as the observation. MPTT-QM was justified by comparing it with the original QM method in terms of precipitation accumulation and hydrological simulations. The results show that MPTT-QM not only improves the spatial distribution of precipitation but also effectively preserves the temporal change, with a better precipitation detection ability. Moreover, the MPTT-QM-corrected hydrological modeling has better performance in runoff simulations than the QM-corrected modeling, with significantly increased KGE metrics ranging from 0.050 to 0.693. MPTT-QM shows promising values in improving the hydrological utilities of various lead time precipitation forecasts.