首页 | 官方网站   微博 | 高级检索  
     

预报异常极端高影响天气的 “集合异常预报法”:以北京2012年7月21日特大暴雨为例
引用本文:杜钧,Richard H. GRUMM,邓国.预报异常极端高影响天气的 “集合异常预报法”:以北京2012年7月21日特大暴雨为例[J].大气科学,2014,38(4):685-699.
作者姓名:杜钧  Richard H. GRUMM  邓国
作者单位:1.美国国家海洋大气局国家环境预报中心, 马里兰州 20740
基金项目:国家自然科学基金项目41075079、41075044、41275065
摘    要:对罕见极端高影响天气,既使一个模式有能力预报它,其数值预报也至少有以下难点:一是有多大把握确定所预报的天气是极端事件?二是其具体的定时、定量、定点预报可靠吗?本文介绍了集合预报和气候资料相结合的 “集合异常预报法”,并通过北京2012年7月21日(7.21)特大暴雨事件揭示出“集合异常预报法”和集合预报可以提供比单一模式预报更可靠和更准确的信息,从而可有效地缓解上述两大难点。作者建议中央气象台和其他有条件的台站可采用这种办法提高重大灾害性天气的预报能力。文中具体讨论了如下三方面:(1)标准化异常度(SA)的定义以及它同集合预报相结合提高对罕见极端高影响天气预报的可靠性,并由此可导出一个“社会影响矩阵”来定量地表达一个预报对社会的潜在影响;(2)利用集合预报,特别是多模式集合预报可以克服单一模式前后预报的跳跃性或不连续性问题,由此可延长实际可预报时效,如北京7.21事件超过100 mm大暴雨的实际可预报时效提前了2天;(3)SA还有助于认识异常天气发生的原因:从SA的分布看,造成北京7.21 大暴雨事件的短期天气尺度背景因素是从西北方向移来的冷锋和台风倒槽的相遇;从SA的演变看,该事件的中期大背景因素是在北京东北方向有阻塞高压发展并导致北京以西地区的低槽加强和发展(地面强冷空气堆积),在高纬度形成了一个高、低压系统相间的波列,发展并维持,同时诱导热带系统北进。

关 键 词:极端高影响天气    北京7.21大暴雨    社会影响矩阵    可预报性    标准化异常度    集合预报    集合异常预报法
收稿时间:2013/7/18 0:00:00
修稿时间:2013/12/14 0:00:00

Ensemble Anomaly Forecasting Approach to Predicting Extreme Weather Demonstrated by Extremely Heavy Rain Event in Beijing
DU Jun,Richard H. GRUMM and DENG Guo.Ensemble Anomaly Forecasting Approach to Predicting Extreme Weather Demonstrated by Extremely Heavy Rain Event in Beijing[J].Chinese Journal of Atmospheric Sciences,2014,38(4):685-699.
Authors:DU Jun  Richard H GRUMM and DENG Guo
Affiliation:1.National Centers for Environmental Prediction (NCEP)/National Oceanic and Atmospheric Administration (NOAA), Maryland 207402.National Weather Service at Pennsylvania State, State College, Pennsylvania 191173.National Meteorological Center, China Meteorological Administration, Beijing 100081
Abstract:Even if a numerical weather prediction model is capable of predicting an extreme weather event, several questions remain such as the confidence level of the predicted event and the reliability of the information related to details such as timing, location, and magnitude. In this paper, a method known as Ensemble Anomaly Forecasting, which combines ensemble forecasts with climatology, is introduced and demonstrated by using a case of extremely heavy rain occurring in Beijing on July 21, 2012. The results show that these two questions can be effectively addressed through this method and ensemble forecasts by providing more reliable and consistent information than that provided by a single forecast. Therefore, we strongly recommend that forecasters apply this method in their daily operations to improve their prediction capability of rare high-impact weather events.The following three aspects are discussed in detail in this study. (1) By comparing a forecast with climatology, the potential rarity of the predicted variable can then be quantitatively measured in terms of standardized anomaly (SA), which normally indicates an extreme event when the departure of a forecast from its climatology mean exceeds three standard deviations. By combining further with ensemble forecasts, the confidence of such an anomaly forecast can also be estimated on the basis of the SA of individual ensemble members, which provides critical information that enables a forecaster to make a more reliable forecast of a potentially rare weather event. A combination of the anomaly and confidence then defines a "societal impact matrix," which can be used to quantitatively measure a forecast's potential impact on society. (2) Because the synoptic scale pattern associated with this heavy rain event in Beijing is quite classical for extreme flooding events, it was a highly predictable event from the large-scale pattern perspective. For example, the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model quite successfully predicted a rainfall event of approximately 115 mm over Beijing with a lead time of approximately six days (0600, July 16). However, the detailed information such as rainfall location and intensity were highly variable or uncertain in subsequent GFS forecasts, thus resulting in low predictability. Such shifting of the model solutions from one cycle to another significantly limits the usefulness of a forecast because it is difficult to follow. In contrast, and as demonstrated by this study, ensemble-based—particularly multi-model ensemble-based—ensemble mean and probabilistic forecasts can mitigate some of the issues associated with model shifting by providing more consistent information to greatly increase forecast utility. Additionally, ensemble-based forecasts may extend the practical predictability length. For example, the predictability length of rainfall exceeding 100 mm over or near Beijing can be extended for approximately two days by using THORPEX Interactive Grand Global Ensemble (TIGGE) based probabilistic forecasts as compared to that by a single GFS forecast. (3) If observation or analysis is used instead of forecasts in the calculation of SA, SA can also help to determine the possible causes responsible for an extreme event. In this case, the spatial distribution of the SA reveals that the immediate short-range synoptic cause of the extreme rainfall is the merging of a cold front from the northwest and strong ridge extending from a tropical system in the southeast, which formed a favorable moisture, convergence, and vertical lifting environment for the development and maintenance of meso-and small-scale convective systems. The time evolution of the SAs further reveals that the medium-range background cause is the flow's meridional development to form and maintain a large-amplitude low-high alternating wave train in high latitudes, particularly the development of a blocking system to the northeast of Beijing and a deepening trough to the west, resulting in a strong cold front that enhanced the north-south exchange including the northward advancement of a tropical system.
Keywords:Extreme weather event  July 21  2012 heavy rain in Beijing  Societal impact matrix  Predictability  Standardized anomalies  Ensemble forecasting  Ensemble anomaly forecasting
点击此处可从《大气科学》浏览原始摘要信息
点击此处可从《大气科学》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司    京ICP备09084417号-23

京公网安备 11010802026262号