A COMBINED VERIFICATION METHOD FOR PREDICTABILITY OF PERSISTENT HEAVY RAINFALL EVENTS OVER EAST ASIA BASED ON ENSEMBLE FORECAST

Persistent Heavy Rainfall (PHR) is the most influential extreme weather event in Asian summer, which has attracted intensive interests of many scientists. By use of operational global ensemble forecasts from China Meteorological Administration(CMA), a new verification method applied to evaluate the predictability of PHR is investigated. A metrics called Index of Composite Predictability (ICP) established on very basic verification indicators, in this paper, Equitable Threat Score(ETS) of 24h accumulated precipitation and Root Mean Square Error(RMSE) of Height at 500hPa is Selected to distinguish “good” and “poor” prediction from all ensemble members. Using the metrics of ICP, the predictability of two typical PHR events in June 2010 and June 2011 is estimated. The results show that the “good member” and “poor member” can be identified by ICP and present an obvious discrepancy in predicting the key weather system which impact on PHR. The different performance of “Good member” and “Poor member” reveals the higher predictability both in synoptic scale and mesoscale weather system in their location, duration and the movement by “Good member”. The source of growth errors for “Poor” member is mainly from errors of initial conditions in northern polar region. The growth of perturbation errors and the reason to cause the better or worse performance of ensemble member also have great value for future model improvement and further research.