Bias Correction of Summer Extreme Precipitation Simulated by CWRF Model over China

Accurate forecast of extreme precipitation plays an important role in guiding the national economy and people's livelihood. The newly developed Climate-Weather Research and Forecasting model (CWRF) is applied to the operational forecasting experiment of China National Climate Center. It provides valuable scientific basis for improving the operational prediction for extreme precipitation.CWRF integrates a comprehensive ensemble of alternate parameterization schemes for each of key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation. This facilitates the use of an optimized physics ensemble approach to improve weather and climate prediction.Daily precipitation data simulated by CWRF from June to August during 1980-2015 and the observation by China Meteorological Administration are used to evaluate the performance of various parameterization schemes, and cumulative distribution function transform (CDFt) correction method is introduced. Based on the CDFt, the probability bias correction model XCDFt is proposed for extreme rainfall by introducing generalized Pareto distribution (GPD) and results are assessed. It shows that Morrison-aerosol parameterization scheme of CWRF model can simulate the spatial distribution of extreme precipitation better as well as correct daily precipitation by CDFt.Simulated results of Morrison-aerosol for daily precipitation threshold and super-threshold samples in summer in North China, Central China and East China are similar to those observed in this scheme. In Changsha, Jinan, Nanjing, and Nanning, GPD characterizes the distribution of each extreme precipitation well. It shows that XCDFt can preserve the CDF form of the observed calibrated precipitation and acquire the small change from the calibration to validations. XCDFt can improve the consistency between model simulation and observation in regional extreme precipitation recurrence levels. In North China, Central China and South China, after model simulation correction by XCDFt, the 20-year recurrence interval of extreme precipitation are closer to the observation, which shows that the revised data are more reliable.Error correction can only be used as a supplementary means to improve extreme precipitation prediction, though. The precision description of model physical process and the improvement of model resolution are the key to improve extreme precipitation prediction level.