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数据同化在空间天气学中的应用

张晗可 沈芳

张晗可, 沈芳. 数据同化在空间天气学中的应用[J]. 空间科学学报, 2022, 42(3): 422-436. doi: 10.11728/cjss2022.03.210611069
引用本文: 张晗可, 沈芳. 数据同化在空间天气学中的应用[J]. 空间科学学报, 2022, 42(3): 422-436. doi: 10.11728/cjss2022.03.210611069
ZHANG Hanke, SHEN Fang. Application of Data Assimilation in Space Weather (in Chinese). Chinese Journal of Space Science, 2022, 42(3): 422-436. DOI: 10.11728/cjss2022.03.210611069
Citation: ZHANG Hanke, SHEN Fang. Application of Data Assimilation in Space Weather (in Chinese). Chinese Journal of Space Science, 2022, 42(3): 422-436. DOI: 10.11728/cjss2022.03.210611069

数据同化在空间天气学中的应用

doi: 10.11728/cjss2022.03.210611069
基金项目: 国家自然科学基金项目(41774184,41974202),中国科学院战略性先导科技专项(XDB 41000000)和中国科学院国家空间科学中心“攀登计划”项目共同资助
详细信息
    作者简介:

    张晗可:E-mail:hkzhang@spaceweather.ac.cn

    通讯作者:

    沈芳,E-mail:fshen@spaceweather.ac.cn

  • 中图分类号: P353

Application of Data Assimilation in Space Weather

  • 摘要: 由太阳活动引起的耀斑和日冕物质抛射等短时间尺度变化的空间天气事件会影响并危害地球磁层、电离层、中高层大气、卫星运行安全以及人类健康,因此对这些空间天气事件的预测显得尤为重要。数据同化在稀疏观测和异步采集的情况下能够增加模型的预测能力,对模型变量进行自洽分析。在数值预报中引入数据同化方法,能够提高预测可信度。本文从数据同化方法的角度出发,主要分析了数据同化目前在大气、电离层、磁层、太阳及其他行星科学研究中的应用,并初步讨论了数据同化未来在空间天气方面的应用。

     

  • 图  1  数据同化方法分类

    Figure  1.  Data assimilation method classification

    图  2  通用ADAPT模型框架

    Figure  2.  General ADAPT model framework

    图  3  ETKF与LETKF同化效果的比较

    Figure  3.  Comparison of ETKF and LETKF assimilation effects

    图  4  ENLS与LETKF同化效果的比较

    Figure  4.  Comparison of ENLS and LETFK assimilation effects

    图  5  BRaVDA方案工作原理。内部边界(白色圆圈)使用来自地球轨道后方位置(黄星)的观测信息更新,更新后的模型条件(紫色区域)保留在模型域中,从而影响地球位置(黑色圆圈)的预测

    Figure  5.  A schematic of how the BRaVDA scheme works. The inner boundary (the white circle) is updated using information from observations from a position behind Earth in its orbit (the yellow star). The updated model conditions (the purple regions) persist in the model domain such that they impact forecasts at Earth’s location (the black circle)

    图  6  位于模型网格中心的真值(红线)、观测值(黑色虚线)和分析值(蓝线)

    Figure  6.  Comparison of the truth (red), observations (black dashed lines), and the analysis (blue) at the observation point located in the center of the model grid

    图  7  预测流程

    Figure  7.  Forecast flow chart

    图  8  数据同化系统误差来源及误差表现(箭头及序号表示模型向前演进的同化流程)

    Figure  8.  Error sources and error characteristics of data assimilation systems (Arrows and serial numbers indicate the assimilation process as the model evolves forward)

    表  1  数据同化模型

    Table  1.   Data assimilation models

    同化模型应用范围
    全球同化电离层模型–有限带宽 中纬度到低纬度电离层
    全球电离层测量同化–高斯马尔
    可夫
    中纬度到低纬度电离层
    全球同化电离层模型–四维变分 带驱动的中纬度到低纬度电离层
    全球电离层测量同化–全物理 带驱动的中纬度到低纬度电离层
    等离子体层
    中–低纬度电动力学–数据同化 带驱动的中纬度到低纬度电离层
    电离层动力学和电动力学–数据
    同化
    带驱动的高纬度电离层
    全球热层模型–数据同化 全球热层模型数据同化
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-08
  • 录用日期:  2021-10-29
  • 修回日期:  2022-03-01
  • 网络出版日期:  2022-05-24

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