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Advances in Techniques of Quantitative Precipitation Forecast
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摘要: 对21世纪以来定量降水预报技术流程中的数值模式预报、统计后处理、检验评估和预报员作用4个方面的研究工作进行了归纳，主要进展包括：业务全球模式对于降水的预报能力持续提升，而发展高分辨率模式 (尤其是对流尺度模式) 和集合预报是提高定量降水预报精准化水平的主要途径，且将两者相结合以促进短期降水预报是发展趋势；统计后处理技术已发展到应用数据挖掘方法对海量预报数据中有效信息进行提取和集成，而再预报资料的出现将进一步促进统计后处理技术的发展；为解决评估精细化定量降水预报面临的新问题，多种新的检验技术得到发展和应用，如极端降水检验评分、空间检验技术及概率检验方法等；预报员在模式和后处理方法上能够提供的附加值越来越有限，但在预报流程中仍将处于核心地位，其角色将逐渐向帮助用户进行决策方向转变。文章指出，定量降水预报技术的发展所面临的挑战包括大气水汽观测及同化技术改进、暖区和复杂地形下暴雨预报等科学问题的解决。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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图 1 现代化的定量降水预报技术流程
Fig. 1 Modern technique flow of quantitative precipitation forecast
表 1 定量降水订正与集成技术概要
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
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表 2 新的定量降水检验评分技术概要
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
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表 3 定量降水预报的4类空间检验技术概要
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
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