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 Method
摘要点击 396  全文点击 336  投稿时间:2018-08-29  
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基金:  国家重点研发计划2018YFC1506402,“全球变化与海气相互作用”专项GASI-IPOVAI-06,国家自然科学基金项目41525017
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中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室,北京100029;中国科学院大学,北京100049;河南大学数学与统计学院,河南开封475004
段晚锁and DUAN Wansuo中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室,北京100029;中国科学院大学,北京100049
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,43(4):915-929,doi:10.3878/j.issn.1006-9895.1810.18219.
Citation:WANG Ye,and DUAN Wansuo.2019.Influences of Initial Perturbation Amplitudes and Ensemble Sizes on the Ensemble Forecasts Made by CNOPs Method[J].Chinese Journal of Atmospheric Sciences (in Chinese),43(4):915-929,doi:10.3878/j.issn.1006-9895.1810.18219.
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,简称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,简称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——预报误差关系和更为平坦的Talagrand图(Talagrand diagram)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:
      The initial perturbation amplitude and ensemble size are important for ensemble forecast. The present study explores the impacts of initial perturbation amplitude and ensemble size on the ensemble forecast skill using a new strategy that applies orthogonal conditional nonlinear optimal perturbations (CNOPs) to the Lorenz-96 model. It is found that due to the effect of nonlinearity, the CNOPs-based ensemble forecast can achieve higher skills when the initial perturbation amplitude is appropriately smaller than the amplitude of initial analysis errors, 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 forecast]. The results also show that an appropriate ensemble size is helpful for achieving higher skills in ensemble forecast. A better spread-skill relationship and a much flatter Talagrand diagram are found in CNOPs-based ensemble forecast, which indicates the reliability of the corresponding ensemble forecast system and makes the above results much solid. It is therefore inferred that the highest skill of CNOPs-based ensemble forecast is mostly likely achieved when initial perturbation amplitudes are properly smaller than those of initial analysis errors and the ensemble size is appropriate.
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