叶璐,刘永柱,陈静. 2020. 集合预报多尺度奇异向量初值扰动方法研究[J]. 气象学报, (0):-, doi:10.11676/qxxb2020.042
集合预报多尺度奇异向量初值扰动方法研究
Study on multi-scale singular vector initial perturbation method for ensemble prediction
投稿时间:2019-08-19  修订日期:2019-12-27
DOI:10.11676/qxxb2020.042
中文关键词:  集合预报,多尺度初值扰动,奇异向量
英文关键词:Ensemble prediction,Multi-scale initial perturbation,Singular vector
基金项目:国家重点研发计划National Key R&D Program of China (2018YFC1507405)
作者单位E-mail
叶璐 中国气象科学研究院 yelu292589@163.com 
刘永柱 中国气象局数值预报中心 liuyzh@cma.cn 
陈静 中国气象局数值预报中心 chenj@cma.gov.cn 
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中文摘要:
      [目的]目前国际上采用的奇异向量集合预报初值扰动法对于初值不确定性的描述存在一定的不足,为了更有效地反映初始误差的时空多尺度特性,[资料和方法]基于GRAPES全球奇异向量计算技术,计算了不同空间分辨率及不同最优时间间隔的多个尺度的奇异向量(Singular Vectors,SVs),并采用基于高斯分布的线性组合法来构造基于多尺度奇异向量的扰动初值,以代表在相空间中增长最快的多尺度初值误差模态。通过2019年1月19日的初值扰动集合预报试验,对比分析了单一尺度SVs初值扰动法与多尺度初值扰动法的扰动特征以及集合预报效果。[结果]结果表明,多尺度奇异向量初值扰动法为区域集合预报提供初始扰动场是合理的,扰动的大小随时间增长,且在空间分布上较好地反映了当前大气的斜压不稳定特征。此外,多尺度奇异向量扰动可以描述一定的大尺度以及中小尺度运动误差特征,较单一尺度奇异向量扰动,能反映出更多初始场的不确定信息。检验分析表明,GRAPES多尺度奇异向量集合预报在集合一致性、连续等级概率评分、离群值等方面有一定的优势,相比于单一尺度奇异向量法,有较好的预报技巧。[结论]因此,基于GRAPES的多尺度奇异向量初值扰动法对于集合预报的预报效果等有一定的提高,能为构建一套完善的GRAPES区域奇异向量集合预报系统提供一定的科学依据和应用基础。
英文摘要:
      In order to further improve the GRAPES region ensemble prediction singular vector initial perturbation method. According to the multi-scale characteristics of atmospheric initial errors, the singular vectors with different spatial resolutions and different optimal time intervals are calculated from GRAPES global singular vectors (SVs).The linear combination method based on Gaussian distribution is used to construct the initial perturbations based on multi-scale singular vector to represent the fastest growing multi-scale initial errors mode in phase space. The perturbation characteristics and the ensemble prediction effect of the single-scale SVs initial perturbation method and multi-scale initial perturbation method are compared by conducting initial perturbation case experiments on January 19 2019.The main conclusions are as follows. It is reasonable for multi-scale singular vector initial perturbation method to provide initial perturbation field for regional ensemble prediction. The magnitude of the perturbation increases with time and the spatial distribution reflects the baroclinic instability of the atmosphere. In addition, multi-scale singular vector perturbations can effectively describe the error characteristics of large scale and small scale motions, and can reflect more uncertain information of initial field than single scale singular vector perturbations. The test analysis shows that GRAPES multi-scale singular vector ensemble forecast has certain advantages on consistency, CRPS, outlier score, and it’s forecasting skills are better than the single-scale singular vector methods. Therefore, the multi-scale singular vector initial perturbation method based on GRAPES can improve the forecasting effect of ensemble prediction, and it can provide a scientific basis and application basis for constructing a complete GRAPES regional singular vector ensemble forecasting system.
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