Bias Correction of Summer Extreme Precipitation Simulated by CWRF Model

The 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) integrates a comprehensive ensemble of alterable parameterization schemes for each of the 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. Evaluating the simulation performance and correcting the error can effectively improve the operational prediction level of extreme precipitation in CWRF model.Daily rainfall data simulated by CWRF model and observed at 2416 meteorological stations in China from June to August during 1980-2015 are used to compare correcting effects of Q-lin, Q-tri, RQ-lin, RQ-tri, SSP-lin and CDFt on extreme precipitation of control scheme simulated by CWRF in eastern China. Based on the simulation performance ranking of 14 parameterization schemes in CWRF model, effects of the top 4, the latter 4 and the ensemble of 14 parameterization schemes are compared. Correcting effects of two approaches are compared: Revising after the collection of members and revising before the collection of members. Main results show that the error of the extreme precipitation simulation of C1 scheme can be obviously reduced by using six error correction methods, among which the RQ-lin correction method is the best. Although there are great differences between parameterization schemes in the simulation of extreme precipitation index, CWRF model shows good ability for extreme precipitation index in eastern China. The first four parametric schemes with good extreme precipitation simulation ability are C13, C14, C12 and C1, while the C6, C4, C3 and C10 schemes perform worse, respectively. Different parameterization schemes are revised to ensure that it is the closest to the average value of observed extreme precipitation after each of 14 members of the parameterization scheme being revised. Results have important application value for improving outputs of model and improving its prediction ability.Error correction can only be used as a supplementary means to improve extreme precipitation prediction. The precision of model physical process and the improvement of model resolution are the key to improve extreme precipitation prediction.