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AGS-PRF: Exploring the Equatorial Solar Wind from Photosphere to Heliosphere Along Solar Cycles

AGS-PRF: Exploring the Equatorial Solar Wind from Photosphere to Heliosphere Along Solar Cycles
AGS-PRF:沿着太阳周期探索从光球层到日光层的赤道太阳风
批准号:
1432100
负责人:
Liang Zhao
金额:
$8.6万
依托单位:
依托单位国家:
美国
项目类别:
Fellowship Award
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-09-01 至 2016-08-31

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中文摘要
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英文摘要
This is a 2-year postgraduate research fellowship project which aims to provide improved understanding of how the slow and fast solar wind heating and acceleration occurs in response to the strength of the solar cycle. The research work will be conducted at the highly collaborative environment of the Atmospheric, Oceanic and Space Science Department at the University of Michigan. The PI will incorporate the research results into the undergraduate and graduate level courses in the University of Michigan. The PI is a recent PhD graduate and this research project will support the continued training and career advancement of an early-career female scientist in the Atmospheric and Geospace sciences. Therefore, the project will enhance the participation of women in sciences, technology, engineering, and mathematics (STEM) disciplines.This 2-year research project will uniquely combine the analysis of in-situ measurement of solar wind properties and of spectroscopic observations of solar wind source regions, taking advantage of the strengths of both types of observations and different analysis techniques. The empirical models to be derived from the matching of in-situ measurements and remote sensing observations will be unprecedented in their detail, and they will continuously cover the entire distance from the solar wind source region to the freeze-in height. These empirical models will provide a benchmark that will allow discrimination between competing solar wind heating and acceleration models and scenarios and will greatly advance our understanding of the solar wind phenomenon and its dependence on solar cycle phase and strength. Moreover, by using two different types of techniques and data, this research work will serve as a link between two Space Sciences communities, and the outcome of this project will benefit both sides.
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Collaborative Research: OAC Core: Distributed Graph Learning Cyberinfrastructure for Large-scale Spatiotemporal Prediction
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CAREER: Uncovering Solar Wind Composition, Acceleration, and Origin through Observations, Modeling, and Machine Learning Methods
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SHINE: Understanding the Physical Connection of the in-situ Properties and Coronal Origins of the Solar Wind with a Novel Artificial Intelligence Investigation
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