doi:  10.3878/j.issn.1006-9895.1810.18219
初始扰动振幅和集合样本数对CNOPs集合预报的影响

Influences of initial perturbation amplitudes and ensemble sizes on the ensemble forecasts made by CNOPs
摘要点击 20  全文点击 11  投稿时间:2018-08-29  修订日期:2018-10-10
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基金:  国家自然科学基金41525017,“全球变化与海气相互作用”专项GASI-IPOVAI-06
中文关键词:  集合预报,初始误差,条件非线性最优扰动,集合样本数
英文关键词:  Ensemble forecast, Initial error, Conditional nonlinear optimal perturbations, Ensemble size
     
作者中文名作者英文名单位
汪叶Wang Ye大气物理研究所
段晚锁Duan Wansuo大气物理研究所
引用:汪叶,段晚锁.2019.初始扰动振幅和集合样本数对CNOPs集合预报的影响[J].大气科学
Citation:Wang Ye,Duan Wansuo.2019.Influences of initial perturbation amplitudes and ensemble sizes on the ensemble forecasts made by CNOPs[J].Chinese Journal of Atmospheric Sciences (in Chinese)
中文摘要:
      初始扰动振幅的大小和集合样本数对于集合预报取得更高预报技巧具有重要意义。本文将正交条件非线性最优扰动方法(orthogonal conditional nonlinear optimal perturbations, CNOPs)应用于概念模型Lorenz-96模式探讨了初始扰动振幅和集合样本数对集合预报技巧的影响,从而为使用更复杂模式进行集合预报提供指导。结果表明,由于CNOPs扮演了非线性系统中的最优初始扰动,从而使得当初始扰动振幅小于初始分析误差的大小时,CNOPs集合预报获得更高的预报技巧,并且CNOPs集合预报的最高预报技巧总是高于奇异向量法(singular vectors, SVs)集合预报的最高预报技巧。结果还表明,CNOPs集合预报倾向于具有一个合适的样本数时,达到最高技巧。更好的集合离散度--预报误差关系进一步证明了CNOPs集合预报系统的可靠性,从而夯实了上述结果的合理性。因此,针对CNOPs集合预报,本文认为采用一个适当小于初始分析误差的初始扰动振幅和一个合适的集合样本数,有利于CNOPs集合预报达到最高预报技巧。
Abstract:
      What amplitudes of initial perturbations and ensemble sizes are adopted to achieve the highest skill of ensemble forecast have been always confusing meteorologists. For a new strategy of the ensemble forecast made by orthogonal conditional nonlinear optimal perturbations (CNOPs), the present study explores the impacts of initial perturbation amplitudes and ensemble sizes on the ensemble forecast skill with the Lorenz-96 model. It is found that, due to the effect of nonlinearity, the CNOPs-based ensemble forecasts require the initial perturbations with amplitudes appropriately smaller than those of initial analysis errors to achieve a higher skill, and the highest skill of the CNOPs-based ensemble forecasts is always higher than that of its linear counterpart (i.e. singular vectors (SVs)-based ensemble forecasts). The results also show that an appropriate ensemble size is helpful for attaining higher skills in ensemble forecasts. A better spread-skill relationship is found in CNOPs-based ensemble forecasts and indicates its reliability of the corresponding ensemble forecast system, which makes the above results much solid. It is therefore inferred that initial perturbations with amplitudes properly smaller than those of initial analysis errors and an appropriate ensemble size are most likely to achieve highest skill of CNOPs-based ensemble forecasts.
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