留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于SWARM-C卫星数据对HASDM模型的热层大气密度误差分析

吴尧 陈俊宇

吴尧, 陈俊宇. 基于SWARM-C卫星数据对HASDM模型的热层大气密度误差分析[J]. 空间科学学报. doi: 10.11728/cjss2026.01.2025-0012
引用本文: 吴尧, 陈俊宇. 基于SWARM-C卫星数据对HASDM模型的热层大气密度误差分析[J]. 空间科学学报. doi: 10.11728/cjss2026.01.2025-0012
WU Yao, CHEN Junyu. Error Analysis of Thermosphere Atmospheric Density for HASDM Method Based on SWARM-C Satellite Data for HASDM (in Chinese). Chinese Journal of Space Science, 2026, 46(1): 1-10 doi: 10.11728/cjss2026.01.2025-0012
Citation: WU Yao, CHEN Junyu. Error Analysis of Thermosphere Atmospheric Density for HASDM Method Based on SWARM-C Satellite Data for HASDM (in Chinese). Chinese Journal of Space Science, 2026, 46(1): 1-10 doi: 10.11728/cjss2026.01.2025-0012

基于SWARM-C卫星数据对HASDM模型的热层大气密度误差分析

doi: 10.11728/cjss2026.01.2025-0012 cstr: 32142.14.cjss.2025-0012
基金项目: 国家自然科学基金项目(12303081), 航空科学基金项目(20240058153001)和空间目标感知全国重点实验室2024年度开放基金项目(STA2024 ZCA0102, STA2024 ZCA0101)共同资助
详细信息
    作者简介:
    • 吴尧 男, 2001年1月出生于福建省福州市, 现为昆明理工大学国土资源工程学院硕士研究生, 主要研究方向高层大气密度的建模和修正. E-mail:wuyao@kust.stu.edu.cn
    通讯作者:
    • 陈俊宇 男, 1989年1月出生于云南省大理市, 现为昆明理工大学国土资源工程学院讲师, 硕士研究生导师. 主要研究方向为空间态势感知. E-mail:jychen@kust.edu.cn
  • 中图分类号: P228.1

Error Analysis of Thermosphere Atmospheric Density for HASDM Method Based on SWARM-C Satellite Data for HASDM

  • 摘要: 准确计算大气密度对卫星及空间碎片的精密轨道预报至关重要. 基于2014-2019年SWARM-C卫星加速度计反演的大气密度数据, 分析高精度大气模型(High Accuracy Satellite Drag Model, HASDM)的误差特性, 及其在不同空间环境下的性能差异. 结果显示, 太阳活动对HASDM影响显著, 中高太阳活动年模型平均偏差约为12.5%, 标准差约为0.2; 低太阳活动年偏差增大至约18.7%, 标准差增大至约0.4; 地磁活动期间, 模型整体偏差稳定在约17%左右, 标准差约达0.4; 纬度分布上, 极区偏差最低, 在5%~10%, 但南极高纬标准差高于北极; 赤道区域偏差最大, 在20%~30%; 地方时分布上, 03:00-06:00 LST与18:00-24:00 LST的误差峰值达20%; 磁暴期间, HASDM在初相易高估密度, 主相误差波动剧烈, 恢复相逐渐趋稳. 本研究为改进大气密度模型的太阳活动参数化和区域性校准提供了关键依据.

     

  • 图  1  HASDM与SWARM-C在不同纬度的大气密度特征与误差

    Figure  1.  Atmospheric density characteristics and errors of HASDM and SWARM-C in different latitudes

    图  2  不同太阳活动条件下HASDM与SWARM-C的纬度误差与大气密度

    Figure  2.  Latitude error and Atmospheric density between HASDM and SWARM-C under different solar activity level

    图  3  不同地磁活动条件下HASDM与SWARM-C的纬度误差与大气密度

    Figure  3.  Latitude error and atmospheric density between HASDM and SWARM-C under different geomagnetic activity level

    图  4  HASDM与SWARM-C在00:00-24:00 LST的大气密度分布与误差

    Figure  4.  Atmospheric density distribution and error of HASDM and SWARM-C at 00:00-24:00 LST

    图  5  不同太阳活动条件下HASDM与SWARM-C不同LST的大气密度与误差

    Figure  5.  Atmospheric density and error of HASDM and SWARM-C at different LST under different solar activity level

    图  6  不同地磁活动条件下HASDM与SWARM-C在不同LST的大气密度与误差

    Figure  6.  Atmospheric density and error of HASDM and SWARM-C at different LST under different geomagnetic activity level

    图  7  2015年观测到的两个典型磁暴事件Dst指数与大气密度随时间的变化情况

    Figure  7.  Variation of the Dst index and atmospheric density with DOY for the two typical geomagnetic storm events observed in 2015

    表  1  不同太阳活动水平下HASDM与SWARM-C的ME,SD和RMSE

    Table  1.   ME, SD and RMSE of HASDM relative to SWARM-C under different solar activity levels

    F10.7 太阳活动水平 $ \text{ME} $/
    (%)
    $ \text{SD} $ $ \text{RMSE} $(×10–13)/
    $ (\text{kg}\cdot {\mathrm{m}}^{-3}) $
    F10.7 $ < $ 100 低水平 18.72 0.434 0.711
    100 $ \leq $ F10.7 $ < $ 150 中水平 12.19 0.197 1.593
    150 $ \leq $ F10.7 高水平 12.83 0.177 1.951
    下载: 导出CSV

    表  2  不同地磁活动水平下HASDM与SWARM-C的ME,SD和RMSE

    Table  2.   ME, SD and RMSE of HASDM relative to SWARM-C under different geomagnetic activity levels

    $ Ap $ 地磁活动水平 $ \text{ME} $/
    (%)
    $ \text{SD} $ $ \text{RMSE} $ (×10–13)/
    $ (\text{kg}\cdot {\mathrm{m}}^{-3}) $
    Ap $ < $ 20 低水平 16.64 0.375 1.114
    20 $ \leq $ Ap $ < $ 50 中水平 16.83 0.377 1.956
    50 $ \leq $ Ap 高水平 17.55 0.344 1.136
    下载: 导出CSV

    表  3  HASDM与SWARM-C在磁暴不同时期下的ME,SD和RMSE

    Table  3.   ME, SD and RMSE of HASDM relative to SWARM-C during different phases of geomagnetic storm

    2015年(DOY) 磁暴时期 ME/(%) SD $ \text{RMSE} $(×10–13)/
    $ (\text{kg}\cdot {\mathrm{m}}^{-3}) $

    76~83
    初相 38.9 0.448 5.024
    主相 30.7 0.270 6.901
    恢复相 12.0 0.145 1.864

    173~178
    初相 53.7 0.310 4.699
    主相 5.9 0.372 8.206
    恢复相 9.7 0.312 1.424
    下载: 导出CSV
  • [1] BOWMAN B, TOBISKA W K, MARCOS F, et al. A new empirical thermospheric density model JB2008 using new solar and geomagnetic indices[C]//AIAA/AAS Astrodynamics Specialist Conference and Exhibit. Honolulu: AIAA, 2008
    [2] BRUINSMA S, BONIFACE C. The operational and research DTM-2020 thermosphere models[J]. Journal of Space Weather and Space Climate, 2021, 11: 47 doi: 10.1051/swsc/2021032
    [3] PICONE J M, HEDIN A E, DROB D P, et al. NRLMSISE-00 empirical model of the atmosphere: statistical comparisons and scientific issues[J]. Journal of Geophysical Research: Space Physics, 2002, 107(A12): SIA 15-1-SIA 15-16
    [4] DENG Y, FULLER-ROWELL T J, RIDLEY A J, et al. Theoretical study: influence of different energy sources on the cusp neutral density enhancement[J]. Journal of Geophysical Research: Space Physics, 2013, 118(5): 2340-2349 doi: 10.1002/jgra.50197
    [5] FEDRIZZI M, FULLER-ROWELL T J, CODRESCU M V. Global joule heating index derived from thermospheric density physics-based modeling and observations[J]. Space Weather, 2012, 10(3): S03001 doi: 10.1029/2011sw000724
    [6] BRUINSMA S L, FORBES J M. Anomalous behavior of the thermosphere during solar minimum observed by CHAMP and GRACE[J]. Journal of Geophysical Research: Space Physics, 2010, 115(A11): A11323 doi: 10.1029/2010ja015605
    [7] SOLOMON S C, WOODS T N, DIDKOVSKY L V, et al. Anomalously low solar extreme-uLSTraviolet irradiance and thermospheric density during solar minimum[J]. Geophysical Research Letters, 2010, 37(16): L16103 doi: 10.1029/2010gl044468
    [8] STORZ M F, BOWMAN B R, BRANSON M J I, et al. High accuracy satellite drag model (HASDM)[J]. Advances in Space Research, 2005, 36(12): 2497-2505 doi: 10.1016/j.asr.2004.02.020
    [9] TOBISKA W K, BOWMAN B R, BOUWER S D, et al. The SET HASDM density database[J]. Space Weather, 2021, 19(4): e2020SW002682 doi: 10.1029/2020SW002682
    [10] LICATA R J, MEHTA P M, TOBISKA W K, et al. Qualitative and quantitative assessment of the SET HASDM database[J]. Space Weather, 2021, 19(8): e2021SW002798 doi: 10.1029/2021SW002798
    [11] BRUINSMA S L, DOORNBOS E, BOWMAN B R. Validation of GOCE densities and evaluation of thermosphere models[J]. Advances in Space Research, 2014, 54(4): 576-585 doi: 10.1016/j.asr.2014.04.008
    [12] BRUINSMA S, SIEMES C, EMMERT J T, et al. Description and comparison of 21st century thermosphere data[J]. Advances in Space Research, 2023, 72(12): 5476-5489 doi: 10.1016/j.asr.2022.09.038
    [13] MIAO Juan, LIU Siqing, LI Zhitao, et al. Correlation of thermosphere density variation with different solar and geomagnetic indices[J]. Manned Spaceflight, 2012, 18(5): 24-30 (苗娟, 刘四清, 李志涛, 等. 热层大气密度变化特征与太阳辐射和地磁指数的相关性分析[J]. 载人航天, 2012, 18(5): 24-30 doi: 10.3969/j.issn.1674-5825.2012.05.008

    MIAO Juan, LIU Siqing, LI Zhitao, et al. Correlation of thermosphere density variation with different solar and geomagnetic indices[J]. Manned Spaceflight, 2012, 18(5): 24-30 doi: 10.3969/j.issn.1674-5825.2012.05.008
    [14] WENG Libin, FANG Hanxian, JI Chunhua, et al. Comparison between the CHAMP/STAR derived thermospheric density and the NRLMSISE-00 model[J]. Chinese Journal of Space Science, 2012, 32(5): 713-719 (翁利斌, 方涵先, 季春华, 等. 基于卫星加速度数据反演的热层大气密度与NRLMSISE-00模式结果的比较研究[J]. 空间科学学报, 2012, 32(5): 713-719

    WENG Libin, FANG Hanxian, JI Chunhua, et al. Comparison between the CHAMP/STAR derived thermospheric density and the NRLMSISE-00 model[J]. Chinese Journal of Space Science, 2012, 32(5): 713-719
    [15] CHEN Xuxing, HU Xiong, XIAO Cunying, et al. Comparison of the thermospheric densities between GRACE/CHAMP satellites data and NRLMSISE-00 model[J]. Chinese Journal of Space Science, 2013, 33(5): 509-517 (陈旭杏, 胡雄, 肖存英, 等. NRLMSISE-00大气模型与GRACE和CHAMP卫星大气密度数据的对比分析[J]. 空间科学学报, 2013, 33(5): 509-517 doi: 10.11728/cjss2013.05.509

    CHEN Xuxing, HU Xiong, XIAO Cunying, et al. Comparison of the thermospheric densities between GRACE/CHAMP satellites data and NRLMSISE-00 model[J]. Chinese Journal of Space Science, 2013, 33(5): 509-517 doi: 10.11728/cjss2013.05.509
    [16] LIU Wei, WANG Ronglan, LIU Siqing, et al. Error analysis of typical atmospheric density model[J]Chinese Journal of Space Science, 2017, 37(5): 538-546 (刘卫, 王荣兰, 刘四清, 等. 典型热层密度模式误差分析[J]. 空间科学学报, 2017, 37(5): 538-546

    LIU Wei, WANG Ronglan, LIU Siqing, et al. Error analysis of typical atmospheric density model[J]Chinese Journal of Space Science, 2017, 37(5): 538-546
    [17] NAZARENKO A I, CEFOLA P J, YURASOV V. Estimating atmosphere density variations to improve LEO orbit prediction accuracy[J]. Advances in the Astronautical Sciences, 1998, 99(2): 1235-1256
    [18] MARCOS F A, RENDRA M J, GRIFFIN J M, et al. Precision low earth orbit determination using atmospheric density calibration[J]. The Journal of the Astronautical Sciences, 1998, 46(4): 395-409 doi: 10.1007/BF03546389
    [19] TOBISKA W K, WOODS T, EPARVIER F, et al. The SOLAR2000 empirical solar irradiance model and forecast tool[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2000, 62(14): 1233-1250 doi: 10.1016/S1364-6826(00)00070-5
    [20] TOBISKA W K, KNIPP D, BURKE W J, et al. The anemomilos prediction methodology for Dst[J]. Space Weather, 2013, 11(9): 490-508 doi: 10.1002/swe.20094
    [21] OLSEN N, FRIIS-CHRISTENSEN E, FLOBERGHAGEN R, et al. The swarm satellite constellation application and research facility (SCARF) and swarm data products[J]. Earth, Planets and Space, 2013, 65(11): 1189-1200 doi: 10.5047/eps.2013.07.001
    [22] YIN L R, WANG L, TIAN J W, et al. Atmospheric density inversion based on Swarm-C satellite accelerometer[J]. Applied Sciences, 2023, 13(6): 3610-3622 doi: 10.3390/app13063610
    [23] IORFIDA E, DARAS I, HAAGMANS R, et al. Swarm A and C accelerometers: data validation and scientific interpretation[J]. Earth and Space Science, 2023, 10(2): e2022EA002458 doi: 10.1029/2022EA002458
    [24] YIN L R, WANG L, ZHENG W F, et al. Evaluation of empirical atmospheric models using Swarm-C satellite data[J]. Atmosphere, 2022, 13(2): 294-308 doi: 10.3390/atmos13020294
    [25] SIEMES C, BORRIES C, BRUINSMA S, et al. New thermosphere neutral mass density and crosswind datasets from CHAMP, GRACE, and GRACE-FO[J]. Journal of Space Weather and Space Climate, 2023, 13: 16 doi: 10.1051/swsc/2023014
    [26] WANG Hongbo. The application of satellite borne accelerometer data to the study of upper atmosphere[J]. Acta Astronomica Sinica, 2010, 51(4): 435-436 (汪宏波. 星载加速仪数据在高层大气研究中的应用[J]. 天文学报, 2010, 51(4): 435-436

    WANG Hongbo. The application of satellite borne accelerometer data to the study of upper atmosphere[J]. Acta Astronomica Sinica, 2010, 51(4): 435-436
    [27] TOBISKA W K, BOUWER S D, BOWMAN B R. The development of new solar indices for use in thermospheric density modeling[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2008, 70(5): 803-819 doi: 10.1016/j.jastp.2007.11.001
    [28] HUTCHINSON J A, WRIGHT D M, MILAN S E. Geomagnetic storms over the last solar cycle: a superposed epoch analysis[J]. Journal of Geophysical Research: Space Physics, 2011, 116(A9): A09211 doi: 10.1029/2011ja016463
    [29] YOKOYAMA N, KAMIDE Y. Statistical nature of geomagnetic storms[J]. Journal of Geophysical Research: Space Physics, 1997, 102(A7): 14215-14222 doi: 10.1029/97JA00903
  • 加载中
图(7) / 表(3)
计量
  • 文章访问数:  318
  • HTML全文浏览量:  43
  • PDF下载量:  34
  • 被引次数: 

    0(来源:Crossref)

    0(来源:其他)

出版历程
  • 收稿日期:  2025-01-09
  • 修回日期:  2025-05-29
  • 网络出版日期:  2025-05-30

目录

    /

    返回文章
    返回