Collaborative Research: CMG-- Models, Tools and Analysis for Studies of the Magnetosphere and Upper Atmosphere

合作研究：CMG——磁层和高层大气研究的模型、工具和分析

• 批准号: 0934488
• 负责人: Stephan Sain
• 金额: $ 51.64万
• 依托单位: University Corporation For Atmospheric Res
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0934488&HistoricalAwards=false
• 关键词: Collaborative; Research; CMG; Models; Tools

The goal of this project is to improve models of the electrodynamic behavior of the magnetosphere, ionosphere, and thermosphere through statistical techniques for finding optimal values of unobserved parameters. Three such parameters will be estimated: 1) a coefficient of proportionality between electron energy and plasma temperature; 2) a parameter relating the flux of precipitating electrons to the electron energy and the plasma density; and 3) a parameter relating the energy gain of electrons traversing a potential drop to the plasma density, electron energy, and the strength of the electric current aligned with the magnetic field. While these parameters cannot be observed, optimal values for them can be estimated by comparing model output generated using specific parameter values against appropriate observations (magnetospheric storms as seen by the POLAR Ultraviolet Imager, for example). Models used to simulate the magnetosphere, ionosphere, and thermosphere are computationally expensive, so that only a limited number of parameter values can be tested. This research will develop strategies, based on hierarchical statistical modeling and the development of scalar error measures, to efficiently search the parameter space for optimal parameter values.Better models of the ionosphere, magnetosphere, and thermosphere are of interest for practical as well as scientific reasons. Strong currents during magnetic disturbances heat the upper atmosphere, causing it to expand upward and greatly increase the air density at satellite altitudes, which can significantly affect the satellite orbits. In addition, the electron density of the ionosphere is strongly affected by the electric fields on both quiet and disturbed days, and variations in this density have impacts on satellite networks used for communication and geolocation. Thus, research conducted here could contribute to an upper-atmospheric forecasting capability of great practical benefit.

该项目的目标是通过统计技术寻找未观测参数的最佳值来改进磁层、电离层和热层的电动力学行为模型。估计三个这样的参数：1)电子能量和等离子体温度之间的比例系数；2)沉淀电子通量与电子能量和等离子体密度的关系参数；3)电子穿过电位降的能量增益与等离子体密度、电子能量和与磁场对齐的电流强度有关的参数。虽然无法观测到这些参数，但可以通过比较使用特定参数值与适当观测（例如POLAR紫外线成像仪观测到的磁层风暴）产生的模型输出来估计它们的最佳值。用于模拟磁层、电离层和热层的模型计算成本很高，因此只能测试有限数量的参数值。本研究将开发基于分层统计建模和标量误差度量的策略，以有效地在参数空间中搜索最优参数值。更好的电离层、磁层和热层模型不仅具有科学意义，而且具有实际意义。磁扰动期间的强电流加热上层大气，使其向上膨胀，大大增加了卫星高度的空气密度，这可以显著影响卫星轨道。此外，电离层的电子密度在平静日和扰动日都受到电场的强烈影响，这种密度的变化对用于通信和地理定位的卫星网络有影响。因此，这里进行的研究有助于建立具有重大实际效益的高层大气预报能力。