| 集合预报多尺度奇异向量初值扰动方法研究 |
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| 引用本文： | 叶璐,刘永柱,陈静,夏宇,王静.集合预报多尺度奇异向量初值扰动方法研究[J].气象学报,2020,78(4):648-664. |
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| 作者姓名： | 叶璐 刘永柱 陈静 夏宇 王静 |
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| 作者单位： | 1.中国气象科学研究院，北京，100081 |
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| 基金项目： | 国家重点研发计划项目（2018YFC1507405） |
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| 摘 要： | 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，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，并采用基于高斯分布的线性组合法来构造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奇异向量初值扰动法与多尺度初值扰动法的扰动特征及集合预报效果。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的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性信息。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。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。
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| 关 键 词： | 集合预报 多尺度初值扰动 奇异向量 |
| 收稿时间： | 2019/8/20 0:00:00 |
| 修稿时间： | 2020/3/26 0:00:00 |
A study on multi-scale singular vector initial perturbation method for ensemble prediction |
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| YE Lu,LIU Yongzhu,CHEN Jing,XIA Yu and WANG Jing.A study on multi-scale singular vector initial perturbation method for ensemble prediction[J].Acta Meteorologica Sinica,2020,78(4):648-664. |
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| Authors: | YE Lu LIU Yongzhu CHEN Jing XIA Yu and WANG Jing |
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| Institution: | 1.Chinese Academy of Meteorological Sciences，Beijing 100081，China2.Numerical Weather Prediction Center of China Meteorological Administration，Beijing 100081，China3.Nanjing University of Information Science and Technology，Nanjing 210044，China4.Institute of Atmospheric Physics，Chinese Academy of Sciences，Beijing 100029，China |
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| Abstract: | This study aims to further improve the GRAPES(Global Regional Assimilation and Prediction Enhanced System) region ensemble prediction singular vector（SV） initial perturbation method. According to the multi-scale characteristics of atmospheric initial errors, the singular vectors with different spatial resolutions and optimal time intervals are calculated from GRAPES global singular vectors. The linear combination method based on Gaussian distribution is used to construct initial perturbations based on multi-scale singular vectors to represent the fastest growing multi-scale initial errors mode in phase space. The perturbation characteristics and the ensemble prediction effects of the single-scale singular vectors initial perturbation method and multi-scale initial perturbation method are compared by conducting initial perturbation experiments for a case occurred on 19 January 2019. The main conclusions are as follows: The multi-scale singular vector initial perturbation method is able to provide reasonable 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 analysis of experiments results shows that GRAPES multi-scale singular vector ensemble forecast has certain advantages on consistency, CRPS （continuous ranked probability score）, outlier score, etc., and its forecasting skills are better than the single-scale singular vector method. 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 evidence and application basis for constructing a complete GRAPES regional singular vector ensemble forecasting system. |
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| Keywords: | Ensemble prediction Multi-scale initial perturbation Singular vector |
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