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一种全球临近空间大气密度建模方法及应用

程旋 肖存英 杨钧烽 胡雄 闫召爱 柳丹

程旋, 肖存英, 杨钧烽, 等 . 一种全球临近空间大气密度建模方法及应用[J]. 北京航空航天大学学报, 2020, 46(12): 2227-2235. doi: 10.13700/j.bh.1001-5965.2019.0614
引用本文: 程旋, 肖存英, 杨钧烽, 等 . 一种全球临近空间大气密度建模方法及应用[J]. 北京航空航天大学学报, 2020, 46(12): 2227-2235. doi: 10.13700/j.bh.1001-5965.2019.0614
CHENG Xuan, XIAO Cunying, YANG Junfeng, et al. A modeling method and its application of global atmospheric density in near space[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(12): 2227-2235. doi: 10.13700/j.bh.1001-5965.2019.0614(in Chinese)
Citation: CHENG Xuan, XIAO Cunying, YANG Junfeng, et al. A modeling method and its application of global atmospheric density in near space[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(12): 2227-2235. doi: 10.13700/j.bh.1001-5965.2019.0614(in Chinese)

一种全球临近空间大气密度建模方法及应用

doi: 10.13700/j.bh.1001-5965.2019.0614
基金项目: 

中国科学院A类战略性先导科技专项 XDA17010301

国家自然科学基金 11872128

国家自然科学基金 91952111

国家空间科学中心“青年科技创新”课题 Y9211FAF3S

详细信息
    作者简介:

    程旋   男,博士研究生。主要研究方向:临近空间大气建模及应用

    肖存英   女,博士,教授,博士生导师。主要研究方向:临近空间大气环境

    通讯作者:

    肖存英, E-mail: xiaocunying@bnu.edu.cn

  • 中图分类号: V419;V219;P351;P421

A modeling method and its application of global atmospheric density in near space

Funds: 

Strategic Priority Research Program of the Chinese Academy of Sciences XDA17010301

National Natural Science Foundation of China 11872128

National Natural Science Foundation of China 91952111

Youth Science and Technology Innovation Foundation of NSSC Y9211FAF3S

More Information
  • 摘要:

    基于TIMED/SABER卫星2002—2018年观测的20~100 km大气密度数据,统计获得多年月平均值和标准偏差的全球网格数据。利用网格数据,分析了大气密度的变化特征。以网格数据为基准,计算了USSA76的相对偏差,分析了USSA76相对偏差的分布特征。以网格数据为驱动,将大气密度表征为平均值与大尺度扰动量和小尺度扰动量的加和,大尺度扰动和小尺度扰动分别采用余弦函数和一阶自回归模型表征,初步建立了全球临近空间大气密度模型。通过对比模型仿真值与激光雷达观测值,表明模型仿真值与观测值具有较好的吻合度,验证了建模方法的可行性。利用蒙特卡罗方法可再现给定轨迹上所有可能的大气状态。

     

  • 图 1  1月份和7月份大气密度月平均值相对于年平均值的变化量在120°E随纬度和高度的分布

    Figure 1.  Latitude-altitude distribution of variation of atmospheric density relative to annual average density on 120°E in January and July

    图 2  1月份和7月份大气密度标准偏差相对于年平均值的变化量在120°E随纬度和高度的分布

    Figure 2.  Latitude-altitude distribution of variation of atmospheric density standard deviations relative to annual average density on 120°E in January and July

    图 3  1月份和7月份USSA76相对于TIMED/SABER大气密度的偏差在120°E随纬度和高度的分布

    Figure 3.  Latitude-altitude distribution of relative error between USSA76 and TIMED/SABER atmospheric density on 120°E in January and July

    图 4  100次蒙特卡罗仿真的大气密度相对于月平均值的变化量随高度的变化

    Figure 4.  Variation of atmospheric density of 100 Monte Carlo simulations relative to monthly mean density with height

    图 5  一次仿真结果与激光雷达观测值的比较

    Figure 5.  Comparison between one-time simulation result and lidar observation values

    图 6  100次蒙特卡罗仿真试验大气密度相关性检验

    Figure 6.  Atmospheric density correlation test of 100 Monte Carlo simulation

    图 7  假设的飞行器再入轨迹

    Figure 7.  An assumed trajectory of a reentry vehicle

    图 8  沿轨迹的大气密度仿真结果

    Figure 8.  Simulation result of atmospheric density along trajectory

    图 9  沿轨迹大气密度小尺度扰动量、大尺度扰动量和总扰动量的仿真结果

    Figure 9.  Simulation result of small-scale perturbances, large-scale perturbances and total perturbances of atmospheric density along trajectory

    图 10  大尺度扰动和小尺度扰动对总扰动的贡献

    Figure 10.  Contribution of large-scale and small-scale perturbances to total perturbances

    表  1  100次蒙特卡罗仿真结果与激光雷达观测结果之间的平均相对偏差

    Table  1.   Mean relative error between 100 Monte Carlo simulation and lidar observation results

    高度/km 相对偏差/%
    40 -1.85
    45 -1.83
    50 -2.26
    55 -2.44
    60 -3.90
    65 -5.09
    70 0.31
    75 -0.39
    80 7.18
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-05
  • 录用日期:  2020-03-26
  • 网络出版日期:  2020-12-20

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