GSTDTAP
项目编号1702147
GEM: Multi-scale Empirical Geomagnetic Field Modeling
Mikhail Sitnov
主持机构Johns Hopkins University
项目开始年2017
2017-09-01
项目结束日期2020-08-31
资助机构US-NSF
项目类别Standard Grant
项目经费335193(USD)
国家美国
语种英语
英文摘要Sophisticated empirical models of the Earth's magnetic field have been built over decades of data collected from spaceborne instruments. These models are a necessary compliment to the physics-based first-principles models. The project goals are to improve one such empirical model of the magnetosphere to include longer, solar cycle time scales and shorter, substorm time scales. This will vastly improve understanding of the near-Earth environment, which is important for ultimately protecting modern technology. The effort will promote hands-on research training for a graduate student and a summer student.

This project is to fundamentally upgrade the storm-scale empirical geomagnetic field TS07D model to include geomagnetic variations on time scales both longer and shorter than those of storms. The former are caused by the solar cycle variations of the solar radiation (quantified by the F10.7 flux). The latter represent substorm effects quantified by auroral indices. The work will address what the impact of the solar irradiance on the structure of the geomagnetic field and underlying current system is, and how substorm thinning and dipolarization of the magnetotail affect the empirical picture of the geomagnetic field. The new model upgrade, which is aimed to answer these questions, employs multiple expansions of equatorial currents with different current sheet thickness values as well as an improved description of the field aligned current system. It combines the system science approach, when the evolution of a few global parameters of the magnetosphere is reconstructed empirically, with the physics-based approach, which is used to constrain spatio-temporal scales and structure of the empirical model.
文献类型项目
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/71851
专题环境与发展全球科技态势
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Mikhail Sitnov.GEM: Multi-scale Empirical Geomagnetic Field Modeling.2017.
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