Persistent Heavy Rainfall(PHR)is the most influential extreme weather event in Asia in summer,and thus it has attracted intensive interests of many scientists.In this study,operational global ensemble forecasts from C...Persistent Heavy Rainfall(PHR)is the most influential extreme weather event in Asia in summer,and thus it has attracted intensive interests of many scientists.In this study,operational global ensemble forecasts from China Meteorological Administration(CMA)are used,and a new verification method applied to evaluate the predictability of PHR is investigated.A metrics called Index of Composite Predictability(ICP)established on basic verification indicators,i.e.,Equitable Threat Score(ETS)of 24 h accumulated precipitation and Root Mean Square Error(RMSE)of Height at 500 h Pa,are selected in this study to distinguish"good"and"poor"prediction from all ensemble members.With the use of 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 there is an obvious discrepancy in their ability to predict the key weather system that affects PHR."Good member"shows a higher predictability both in synoptic scale and mesoscale weather system in their location,duration and the movement.The growth errors for"poor"members is mainly due to errors of initial conditions in northern polar region.The growth of perturbation errors and the reason for better or worse performance of ensemble member also have great value for future model improvement and further research.展开更多
基金National 973 Program of China(2012CB417204)National Natural Science Foundation of China(41075035,41475044)Special Fund for Meteorological Scientific Research in the Public Interest(GYHY201006015)
文摘Persistent Heavy Rainfall(PHR)is the most influential extreme weather event in Asia in summer,and thus it has attracted intensive interests of many scientists.In this study,operational global ensemble forecasts from China Meteorological Administration(CMA)are used,and a new verification method applied to evaluate the predictability of PHR is investigated.A metrics called Index of Composite Predictability(ICP)established on basic verification indicators,i.e.,Equitable Threat Score(ETS)of 24 h accumulated precipitation and Root Mean Square Error(RMSE)of Height at 500 h Pa,are selected in this study to distinguish"good"and"poor"prediction from all ensemble members.With the use of 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 there is an obvious discrepancy in their ability to predict the key weather system that affects PHR."Good member"shows a higher predictability both in synoptic scale and mesoscale weather system in their location,duration and the movement.The growth errors for"poor"members is mainly due to errors of initial conditions in northern polar region.The growth of perturbation errors and the reason for better or worse performance of ensemble member also have great value for future model improvement and further research.
