极端降水天气预报指数对气候累积概率分布敏感性研究

The sensitivity of the Extreme Precipitation Forecast Index on climatological cumulative probability distribution

针对中国气象局T213全球集合预报系统历史样本较短、极端降水天气预报指数数学模型中气候累积概率分布样本不足的问题,选取气候背景和地理地貌特征较为相似的长江中下游地区,利用扩展时间序列和空间范围的方法,增加T213全球集合预报系统的降水预报气候分布样本数,改进模式降水预报气候累积概率分布,利用2011年6月10—20日集合预报资料进行极端降水天气预报指数预报试验,分析气候累积概率分布敏感性。结果显示:扩展时间序列和空间范围增加模式T213集合预报气候样本数的方法,生成的模式气候累积概率分布较单一格点方法更具代表性,能提高极端降水天气预报指数的识别技巧,并提前8 d发出长江中下游地区极端强降水预报信号。

The history of the T213 Ensemble Prediction System （EPS） is short, and the sample of the model＇s climatological cumulative probability distribution is small in establishing a mathematical model of Extreme Precipitation Forecast Index （EPFI）. For minimizing this problem, we select the middle and lower reaches of the Yangtze River as the study area where the climatological backgrounds as well as the geographical and topographic features are similar. Additionally, we extend the samples of the T213 EPS precipitation forecast and improve model＇s climatological cumulative distribution function by using the extended time series and the range of spatial coverge. A new EPFI is established and applied to forecast experiments of several extreme rainfall events in Yangtze River reaches from June 10 to 20 in 2011. The results show that the above method, i.e., time series and spatial range are extended to increase the number of samples of T213 ensemble forecasts, have generated more representative model climatological cumulative distribution function than a single grid method, and it can improve EPFI identify skills and forecast the extreme precipitation events in the Yangtze River reaches by 8 days in advance.