李俊,杜钧,刘羽. 2015. 北京“7.21”特大暴雨不同集合预报方案的对比试验[J]. 气象学报, 73(1):50-71, doi:10.11676/qxxb2015.008
北京“7.21”特大暴雨不同集合预报方案的对比试验
A comparison of initial condition-, multi-physics- and stochastic physics-based ensembles in predicting Beijing "7.21" excessive storm rain event
投稿时间:2014-03-03  修订日期:2014-08-25
DOI:10.11676/qxxb2015.008
中文关键词:  北京“7.21”暴雨  集合预报  初值扰动  多物理过程  随机物理扰动
英文关键词:Beijing “7.21” torrential rain  Ensemble forecast  IC perturbation  Multi-physics  Stochastic physics
基金项目:国家自然科学基金项目(41275107)。
作者单位
李俊 中国气象局武汉暴雨研究所, 武汉, 430074 
杜钧 美国国家海洋大气局国家环境预报中心, 华盛顿, 美国 
刘羽 中国气象局武汉暴雨研究所, 武汉, 430074 
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中文摘要:
      采用6套扰动方案(初值、多物理、3组随机物理和初值与随机物理的混合)对2012年7月21日(“7.21”)北京特大暴雨过程进行了集合降水预报试验,检验了不同方案的集合平均预报、集合区间预报和概率预报较控制预报改进的相对程度,分析了它们对该过程时空不确定性的预报能力、不同扰动方法的离散度贡献以及不同尺度扰动对预报误差的贡献等。结果表明:(1)所有集合方案特别是初值扰动、多物理和混合扰动的集合预报相对控制预报在暴雨强度和位置上都有较显著的改进,并为用户决策提供了包括预报不确定性在内的更多预报信息。(2)3组随机物理产生的集合预报离散度很相似, 都远小于初值扰动和多物理方案产生的离散度, 并且主要集中在强降水中心附近, 因此在初值扰动的基础上加入随机扰动,可以提高强降水中心的离散度, 但对强降水中心以外的地区作用甚微;尺度分析进一步表明随机物理产生的离散度贡献主要集中在较小尺度上(<320 km),在更小的尺度上(<160 km)它甚至可以与初值和多物理扰动的贡献相当,而初值扰动和多物理过程的贡献可以比随机物理过程多延伸400—500 km直到较大的尺度(如>1000 km), 其中多物理过程在较小尺度上(<100 km)可比初值扰动贡献更大, 并且能部分消除预报系统偏差。(3) 所有集合扰动方案所产生的离散度尺度谱都与实际预报误差尺度谱分布一致, 即随空间尺度增大而减小,但在幅度上都小于预报误差(离散度不够大),并且这种差异随着空间尺度的减小而加速增大,在小尺度上相差甚大。
英文摘要:
      Using the historical Beijing "7.21" extreme precipitation event as an example, the six ensemble schemes (the initial condition (IC), multi-physics (MULTI), 3 stochastic physics as well as a combination of IC and stochastic-physics (COM) were compared in the following three aspects of heavy precipitation forecasts: the performance of ensemble means, ensemble ranges and probabilities with respect to the control forecast, characteristics of ensemble spreads, and spread-forecast error relations. The results show that: (1) In spite of the existence of large systematic forecast error, all the ensembles, especially the IC, MULTI and COM, are able to noticeably improve torrential-rain prediction over the control forecast in both intensity and location, and provide more complete information including the forecast uncertainty for users to make a better decision. (2) Forecast spreads of the three stochastic physics ensembles are similar to each other, generally much less than those of the IC and MULTI ensembles and mainly concentrated near the center of the severe rainfall area. As a result, the ensemble spread is enhanced in the vicinity of the severe precipitation area but little is changed elsewhere after stochastic physics is employed in addition to IC perturbations, which leads to virtually no improvement to the overall spread over a larger domain comparing to that of the IC ensemble. By decomposing spread over the spatial scales, it further shows that the forecast diversity contributed by the stochastic physics is mainly in the smaller-scale (< 320 km, it could reach to a similar level to those by the IC and MULTI ensembles at scale < 160 km), while the contribution from the IC and MULTI perturbations to spread could extend another 400-500 km reaching to larger scales such as > 1000 km; at smaller scales (< 500 km),multi-physics technique could produce larger precipitation spread than IC perturbation does, another advantage of multi-physics approach over other approaches is that it could partially reduce forecast bias. And, (3) the spread spectrum is similar to the forecast error spectrum over spatial scales for all the ensembles, i.e., decreasing with the increase of the spatial scale. However, the magnitude of the spread spectrum is smaller than that of the forecast error spectrum (indicating under-dispersion), this departure increases rapidly with the decrease of spatial scale and becomes large over the small scales.
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