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Modeling observations of solar coronal mass ejections with heliospheric imagers verified with the Heliophysics System Observatory

机译:由日光物理学系统天文台验证的日冕层成像仪对太阳日冕物质抛射的模拟观测

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摘要

We present an advance toward accurately predicting the arrivals of coronal mass ejections (CMEs) at the terrestrial planets, including Earth. For the first time, we are able to assess a CME prediction model using data over two thirds of a solar cycle of observations with the Heliophysics System Observatory. We validate modeling results of 1337 CMEs observed with the Solar Terrestrial Relations Observatory (STEREO) heliospheric imagers (HI) (science data) from 8 years of observations by five in situ observing spacecraft. We use the self-similar expansion model for CME fronts assuming 60° longitudinal width, constant speed, and constant propagation direction. With these assumptions we find that 23%–35% of all CMEs that were predicted to hit a certain spacecraft lead to clear in situ signatures, so that for one correct prediction, two to three false alarms would have been issued. In addition, we find that the prediction accuracy does not degrade with the HI longitudinal separation from Earth. Predicted arrival times are on average within 2.6±16.6 h difference of the in situ arrival time, similar to analytical and numerical modeling, and a true skill statistic of 0.21. We also discuss various factors that may improve the accuracy of space weather forecasting using wide-angle heliospheric imager observations. These results form a first-order approximated baseline of the prediction accuracy that is possible with HI and other methods used for data by an operational space weather mission at the Sun-Earth L5 point.
机译:我们在准确预测日冕物质抛射(CME)到达包括地球在内的地球行星方面取得了进展。我们首次能够使用太阳物理系统天文台利用太阳太阳观测周期三分之二的数据评估CME预测模型。我们通过对5台原位观测航天器进行的8年观测,验证了利用太阳陆地关系天文台(STEREO)日球成像仪(HI)(科学数据)观测到的1337个CME的建模结果。我们对CME前沿使用自相似扩展模型,假设纵向宽度为60°,速度恒定,传播方向恒定。基于这些假设,我们发现被预测会撞到某航天器的所有CME中有23%–35%导致清晰的原位信号,因此对于一个正确的预测,将发出两到三个错误警报。此外,我们发现,随着HI与地球的纵向分离,预测精度不会降低。预计的到站时间平均为原地到站时间的2.​​6±16.6 h,与分析和数值建模相似,真实技能统计为0.21。我们还将讨论各种因素,这些因素可能会提高使用广角日球成像仪观测结果进行空间天气预报的准确性。这些结果形成了HI和其他方法的预测精度的一阶近似基线,而HI和其他方法可通过在太阳地球L5点进行的太空气象任务进行。

著录项

  • 来源
    《Space Weather》 |2017年第7期|955-970|共16页
  • 作者单位

    Space Research Institute, Austrian Academy of Sciences, Graz, Austria, IGAM-Kanzelhöhe Observatory, Institute of Physics, University of Graz, Graz, Austria;

    Department of Physics, University of Helsinki, Helsinki, Finland;

    Space Research Institute, Austrian Academy of Sciences, Graz, Austria, IGAM-Kanzelhöhe Observatory, Institute of Physics, University of Graz, Graz, Austria;

    Department of Physics, University of Helsinki, Helsinki, Finland;

    RAL Space, Rutherford Appleton Laboratory, Harwell, UK;

    RAL Space, Rutherford Appleton Laboratory, Harwell, UK;

    RAL Space, Rutherford Appleton Laboratory, Harwell, UK, University College London, London, UK;

    Institute of Atmospheric Physics CAS, Prague, Czech Republic;

    Blackett Laboratory, Imperial College London, London, UK;

    Blackett Laboratory, Imperial College London, London, UK;

    Blackett Laboratory, Imperial College London, London, UK;

    Institute for Astrophysics, University of Göttingen, Göttingen, Germany;

    IGAM-Kanzelhöhe Observatory, Institute of Physics, University of Graz, Graz, Austria;

    Space Research Institute, Austrian Academy of Sciences, Graz, Austria;

    Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, New Hampshire, USA;

    Applied Physics Laboratory, The Johns Hopkins University, Laurel, Maryland, USA;

    Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, British Columbia, Canada;

    Solar Terrestrial Center of Excellence-SIDC, Royal Observatory of Belgium, Brussels, Belgium;

    Institut de Recherche en Astrophysique et Planétologie, Université de Toulouse (UPS), Toulouse, France, Centre National de la Recherche Scientifique, Toulouse, France;

    School of Physics, Trinity College Dublin, Ireland;

    Heliophysics Science Division, GSFC/NASA, Greenbelt, Maryland, USA;

    Space Research Institute, Austrian Academy of Sciences, Graz, Austria;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Space vehicles; Earth; Observatories; Wind forecasting;

    机译:航天器;地球;天文台;风预报;

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