基于T639集合预报的持续性强降水中期客观预报技术研究

A study on medium-range objective weather forecast technology for persistent heavy rainfall events based on T639 ensemble forecast

中文关键词:  持续性强降水  集合预报  中期客观预报  累积概率分布函数

英文关键词:Persistent heavy rainfall events  Ensemble forecast  Medium-range objective weather forecast  Cumulative distribution functions (CDF)

中文摘要:

      针对持续性强降水预报困难的问题，根据Anderson-Darling检验原理，构建基于中国气象局T639集合预报系统的持续性强降水中期客观预报方法。对比分析2010-2015年5-9月T639集合预报降水与实况降水的累积概率分布函数差异，在此基础上采用扩展时间序列和空间范围的方法构建3种模式气候累积概率方案，通过批量预报试验和检验，选取最优概率方案纳入预报模型，考察持续性强降水个例的最长预报时间。结果表明，随着预报时效的延长，集合预报模式的降水逐渐集中于小和中雨量级，无降水和暴雨以上量级的降水概率低于观测，168 h以后模式降水概率趋于稳定。通过扩展时间序列和空间范围能弥补模式气候资料年限不足所带来的偏差，根据区域气候特征细分模式气候的方法重点突出了不同区域的降水特征，明显优于简单集合所有区域数据的模式气候方案。基于集合预报的持续性强降水预报模型对持续性强降水个例的预报能力为8-9 d，随着预报时效的延长，降水强度以及雨带位置的预报能力逐渐减弱。

英文摘要:

      Persistent heavy rainfall events (PHREs) are difficult to forecast. Medium-range objective weather forecast technology for PHREs based on the global ensemble prediction system (EPS) of the China Meteorological Administration (CMA) is constructed based on the Anderson-Darling test principle. Using T639 forecasts of precipitation and observed rainfall during May-September of 2010-2015, characteristics of the cumulative distribution functions (CDFs) of the observed and T639 EPS forecasts of precipitation are analyzed. Accordingly, three schemes of modeling climatological CDFs are established using the method of extending time series and spatial range. Through a batch of forecasting tests, the optimal modeling scheme is selected for application in the forecast model to examine the longest valid forecast time for PHREs. The result shows that the precipitation forecasted by T639 EPS is gradually concentrated in light and moderate rain with increase in the forecast lead time, and the probabilities of no precipitation and rainstorm both are lower than observations. The probability of precipitation is stable after 168 h. The scheme can make up for the deviation of EPS data by extending the time series and the spatial range. Further subdivision of modeling of climatological CDFs according to regional climatic characteristics highlights the precipitation characteristics over different areas, which is significantly better than the one that simply incorporates all regional data into one single dataset. The model based on T639 EPS can issue early warnings of PHREs 8 to 9 d in advance. With increases in the forecast lead time, the EPFI becomes less skillful in forecasting rain belt and intensity.