Vol.27, NO.5, 2016
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Bi Baogui, Dai Kan, Wang Yi, et al. Advances in techniques of quantitative precipitation forecast. J Appl Meteor Sci, 2016, 27(5): 534-549. DOI:  10.11898/1001-7313.20160503.
Citation: Bi Baogui, Dai Kan, Wang Yi, et al. Advances in techniques of quantitative precipitation forecast. J Appl Meteor Sci, 2016, 27(5): 534-549. DOI:  10.11898/1001-7313.20160503.
shu

Advances in Techniques of Quantitative Precipitation Forecast

DOI: 10.11898/1001-7313.20160503

  • National Meteorological Center, Beijing 100081

  • Received Date: 2016-06-16
  • Rev Recd Date: 2016-08-01
  • Publish Date: 2016-09-30
    Abstract
  • The quantitative precipitation forecast (QPF) is a core operation of weather forecast. Modern technological processes of the QPF include numerical weather forecast, verification and evaluation, objective calibration and integration, forecaster's subjective modification and gridding post-processing. Domestic and international research work covering these five aspects are investigated and summarized, to provide reference for development of the quantitative precipitation forecast.In the aspect of numerical weather forecast, the forecast skill of the operational global model for precipitation has been improving continuously (a gain of about 1 forecast day per decade), and developments of the high resolution model (especially the convection-permitting model) contribute to describing characteristics of the convective precipitation, while the ensemble models provide uncertainty information and the most possible outcome of the forecast. These two techniques are the main way to improve the fine level and accuracy of QPF, and improvement of short-term precipitation forecast by developing operational high-resolution model ensembles is the international tendency. Objective calibration and integration as well as gridding post-processing make up the statistical post-processing technique of the QPF, which now reach a level that applies multiple approaches of data mining to extract and integrate more useful information from massive data, and the emergence of reforecast dataset will further promote the development of statistical post-processing. In terms of verification and evaluation, to solve new problems in assessing the fine level and accuracy of the QPF, a variety of new verification approaches are developed and applied, such as the new score for verifying the precipitation forecast of different climate backgrounds, extremes and multiple types, spatial verification methods for avoiding dual punishments of traditional methods, as well as the probability verification methods for verifying the stability, sharpness, and resolution of the probability forecast. In the aspect of forecaster's subjective modification, although the value added to model and post-processing methods become more and more limited, forecasters still play a core role, gradually changing to help users make decision. The development of QPF techniques still face challenges of solving scientific problems such as the observation of atmosphere moisture and data assimilation methods, as well as heavy rain forecast in warm section and complex topography.
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    References(161)

  • Fig. 1  Modern technique flow of quantitative precipitation forecast

    DownLoad: Full-Size Img PowerPoint

    Table  1  A brief summary of QPF calibration and integration

    名称 主要工作 技术特点
    模式输出统计方法
    (MOS)    		 Antolik[80]2000年回顾了美
    国国家天气局的统计定量降
    水预报    		 ·利用预报因子和降水建立回归方程;
    ·可以针对站点或是格点的降水进行建模，而精
      细化的格点MOS方法依赖于高分辨率的定量
      降水估测产品;
    ·对有降水量的简单的偏差订正不适用于无降
      水的情况
    基于配料法的
    强降水等级预报    		 Doswell[81]1996年引入气象研
    究，并用于暴洪预报；
    张小玲等[82]2010年应用于我国
    的暴雨预报    		 ·依据预报量和指示量之间的物理联系建立相
      应的预报方程，而不是简单依赖于回归分析;
    ·具有清晰的物理意义
    概率匹配集合
    平均方法    		 Elbert[48]2001年首先提出并应
    用于多模式“Poor Man”的集成；
    Fang等[52]2013年针对我国台湾
    登陆台风的强降水提出了改进的
    频率匹配方法    		 ·相对于单一模式能够提高降水预报技巧，消减
      系统性的误差；
    ·既能保留降水集合平均的空间分布，也能保留
      集合成员的概率分布，好于集合平均方法；
    ·过滤掉了不可预报的小尺度信息[83]
    频率订正技术 Zhu等[84]提出并应用于NCEP的
    GFS和GEFS模式的QPF预报    		 ·利用观测降水量的频率分布校正预报降水量
      的频率分布；
    ·有效减小了降水预报误差，降水的分布型也更
      加准确
    DownLoad: Download CSV

    Table  2  A brief summary of new QPF verification scores

    检验目的 方法概要
    检验不同气候背景的降水预报 ·Rodwell等[119-120]发展了概率空间稳定相当误差 (SEEPS) 评分方法，
      该方法通过降水长期气候特征分布确定的降水等级阈值，便于比较不
      同区域和不同季节的模式QPF性能；
    ·目前成为ECMWF、芬兰等气象部门主要检验评分手段，陈法敬等[121]
      也将该方法用于我国降水的预报检验
    检验极端降水的预报能力 ·Stephenson等[122]提出用极端依赖评分 (EDS) 检验小概率事件;
    ·EDS不断得到改进，发展了稳定EDS (SEDS) 评分方法[123]，以及基于
      极端依赖指数和稳定极端依赖指数多变量的评分方法[124]，可较好地
      分辨不同模式对极端事件的预报能力[114, 125]
    对比不同类型的预报评估 ·Mason等[126]和Weigel等[127]提出通用辨别评分 (GDS) 用于度量两种
      不同实况能被相应的预报正确分辨的可能性大小，适用于不同预报类
      型的对比
    DownLoad: Download CSV

    Table  3  A brief summary of QPF spatial verification methods

    方法名称 方法概要
    场变形技术 ·将误差分解为位置、量级和残余误差，给出较为清晰的物理意义;
    ·由Hoffman等[129]在检验方法中首先引入，标志着空间检验技术开端
    特征检验技术 ·识别降水目标，然后进行配对和对比，评估目标个体之间的属性差异;
    ·采用不同方法识别降水目标，如简单阈值[130]、圆柱卷积滤波[131]等
    尺度分离技术 ·采用空间滤波分解尺度，评估模式对不同尺度上降水结构的预报能力;
    ·因采用的滤波方法不同而有所差异，如Casati等[132]基于二维小波发展
      了强度-尺度检验技术，该方法已应用于QPF临近预报检验中[133]
    邻域检验技术 ·对比检验考虑了邻近空间或时间范围内的预报和观测降水事件[134];
    ·包含了降水固有的时空分布不确定性，尤其适合高分辨率模式的检验
    DownLoad: Download CSV
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