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Application of Ensemble Methodology to Heavy-Rain Research a

  发布日期:07-04   浏览次数:   责任编辑:管理员   【字体大小: |

机理了解不够和可预报性问题的忽略是暴雨预报不尽人意的两大原因。主要讨论第二方面,即如何面对和处理可预报性问题,这对如何提高现有数值预报的价值、做好气象服务尤其重要。根据作者多年的研究和实践经验以及直接接触的一些研究和方法,简要地总结了数值集合预报方法在暴雨研究和预报中的应用,具体包括以下四个方面:(1)暴雨集合预报系统的建立:初值和物理扰动、成员数、模式分辨率、资料同化和“虚拟”集合预报;(2)预报方法:集合异常预报法、再预报相似集合法和台风路径聚类法;(3)在预报后处理与订正中的应用:平均、成员排序与最佳成员法、加权平均、概率匹配平均法和集合动力因子法;(4)对暴雨天气系统的机理分析与模式初值的改进:初值扰动差异分析和集合敏感性法、目标观测。希望国内气象业务部门能在日常业务中借鉴以上方法以提高暴雨预报和服务水平,为今后的研究工作提供一个新的起点、方向和方法,这包括指导现有的一些业务集合预报系统今后的进一步完善。

Abstract:

Inability in correctly predicting heavy rain events is primarily due to two reasons: lack of full understanding its physical mechanism and negligence of its predictability limit. How to deal with its predictability limit is the focus of this review paper, which is especially important to enhance the value of numerical weather prediction products to better serve end-users. Based mainly on authors' own or directly involved researches and experiences, many applications of ensemble methodology to heavy rain research and prediction are brielfy overviewed. Speciifcally speaking, the following four general areas are discussed: (1) ensemble prediction system including initial condition and model/physics perturbations, optimal ensemble size, model resolution, data assimilation, and various "virtual" ensembles; (2) forecast methods including ensemble anomaly forecasting, reforecasting analog ensemble, and storm track clustering; (3) forecast post-processing and calibration including ensemble mean, performance ranking and best member, weighted ensemble mean, probability-matched ensemble mean, and ensemble of dynamic factors; and (4) weather system analysis and model initial condition improvement including perturbation difference analysis, ensemble sensitivity, and targeted observation. It is expected that this review will inspire actions from both operation and research communities: many proven-to-be effective methods described in this paper could be adopted in routine weather forecasting practice by operational meteorologists to improve their forecast and service; research community could have a new starting point with new ideas and a clearer direction for future science and technology development including the improvement of current existing operational ensemble prediction systems in years to come.

 
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