Extended Abstract

The Regional Ensemble Prediction System(REPS) at CMA andits application to heavy rainfall forecast in monsoon season inChinaJing Chen,Guo Deng, Yinglin Li, Xiaoli Li, Jiandong Gong,Xiaocong Wang. Jiangkai HuThe Center of Numerical Weather Prediction,CMAchenj@cma.gov.cn1. IntroductionTo improve the probabilistic forecast of the heavy rain events in summerseason in China, the Center of Numerical Weather Prediction(CNWP),CMAdeveloped regional ensemble prediction system (REPS).The REPS tookadvantage of achievements at high resolution deterministic mesoscaleprediction model, data assimilation system and experiences from developmentof global ensemble prediction system at CNWP. The real time operationalrunning of REPS is started from Jun 2010. Results showed that the REPScould give much more information comparing with control run. However, it wasfound that spread of REPS was small compared with observations andforecast errors, the forecasting error for control run was obvious, andmesoscale ensemble products were still not good enough for site- andtime-specific forecasts, but it demonstrated good ability to capture the highimpact weather event.2.Developments of REPS/CNWPThe control model of REPS was the Weather Research and Forecasting model(WRF V2.2) over a limited area domain, which is covering China. Its horizontalresolution is 0.15 x 0.15 degree. The initial perturbation technique is based onthe breeding of growing modes (BGM), which is a simple and inexpensivemethod to generate growing modes of the atmosphere. This method simulatesdata assimilation process and considers the errors in real data at the sametime. The BGM-based initial perturbation in mesoscale ensemble predictionmodel was rescaled in a forecasting cycle, then produced 60-hour predictiontwice a day at 00UTC and 12 UTC respectively. Lateral boundary conditionsare obtained from global ensemble prediction system at CMA, in which 15members participated in total, and each output of GEPS corresponds to onemember of REPS. Efforts were made to include as much meso-scale data in-292-