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Space Weather: Spatio-Temporal Dynamics During Strong Solar Wind - Magnetosphere Coupling

Space Weather: Spatio-Temporal Dynamics During Strong Solar Wind - Magnetosphere Coupling
空间天气:强太阳风期间的时空动力学 - 磁层耦合
批准号:
0318629
负责人:
A Surjalal Sharma
金额:
$25.88万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2003
资助国家:
美国
项目状态:
已结题
起止时间:
2003-08-01 至 2007-07-31

项目摘要

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中文摘要
翻译
驱动磁暴的主要太阳风特征是动压和感应电场。在磁层中,等离子体对流和磁亚暴是影响环电流的主要过程。该项目将利用多航天器和多站地面测量的观测数据,研究导致磁层极端条件的不同因素。根据时间序列数据重建动力学的技术将用于建立磁层动力学的模型,特别是环电流的建立和衰减。全球风暴特征可以使用数据衍生模型进行预测。多尺度特征只能在统计意义上进行预测,但统计变化可以用来限制风暴的可预测性。该项目的主要任务将是研究风暴时间扰动的空间结构,风暴的全球和多尺度特征及其可预报性,以及太阳风变量(电场、动压、密度等)的相对作用。作为极端条件下的司机。地球磁尾中的应力磁场是储存能量的最重要形式之一,将利用多航天器的波瓣磁场、极帽大小和相关变量的数据来研究其对空间气象灾害的贡献。将要使用的动力学技术的一个重要特征是它们能够确定太阳风和磁层变量之间的因果关系。这些因果关系将被用来确定导致磁层极端条件的关键太阳风变量。
英文摘要
The main solar wind features that drive magnetic storms are the dynamic pressure and the induced electric field. In the magnetosphere, plasma convection and magnetic substorms are the dominant processes that influence the ring current. This project will use the observational data from multi-spacecraft and multi-station ground-based measurements to study the different factors that lead to the extreme conditions in the magnetosphere. The techniques of reconstruction of dynamics from time series data will be used to build models of magnetospheric dynamics, and in particular the ring current build-up and decay. Global storm features can be predicted using data-derived models. Multi-scale features can be predicted only in the statistical sense, but the statistical variations can be used to place limits on predictability of storms. The primary task for the project will be a study of the spatial structure of the storm time disturbances, the global and multi-scale features of storms and their predictability, and the relative role of solar wind variables (electric field, dynamic pressure, density, etc.) as drivers of extreme conditions. The stressed magnetic field in the Earth's magnetotail is one of the most important forms of stored energy and its contribution to space weather hazards will be studied using multi-spacecraft data of the lobe field, polar cap size and related variables. An important feature of the dynamical techniques that will be used is their ability to determine the causal relationship between the solar wind and magnetospheric variables. These causal relationships will be used to identify the key solar wind variables responsible for the extreme conditions in the magnetosphere.
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NSF Convergence Accelerator: Symposium on Predicting Extremes by Data-Driven Analytics
  • 批准号:
    2035365
  • 项目类别:
    Standard Grant
  • 资助金额:
    $10.0万
  • 财政年份:
    2020
  • 负责人:
    A Surjalal Sharma
  • 依托单位:
PREEVENTS: Workshop on Integrated Framework for Modeling and Prediction of Extreme Events; College Park, Maryland; Summer 2016
  • 批准号:
    1638499
  • 项目类别:
    Standard Grant
  • 资助金额:
    $5.0万
  • 财政年份:
    2016
  • 负责人:
    A Surjalal Sharma
  • 依托单位:
Workshop on the Impacts of Space Weather on Economic Vitality and National Security; College Park, Maryland
  • 批准号:
    1561232
  • 项目类别:
    Standard Grant
  • 资助金额:
    $3.0万
  • 财政年份:
    2015
  • 负责人:
    A Surjalal Sharma
  • 依托单位:
Low Frequency Waves in the Ionosphere During High Frequency (HF) Heating and Effects on the Ground and in the Magnetosphere
  • 批准号:
    1158206
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2013
  • 负责人:
    A Surjalal Sharma
  • 依托单位:
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