GEM: Inner Radiation Belt Data Assimilation

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。该项目将确定短期动力学如何影响地球内辐射带（范艾伦带）的长期行为。现有的基于物理学的内辐射带模型是在先前的NSF空间天气奖下开发的，将是分析的起点。利用数据同化技术，将15年的太阳质子俘获和地磁风暴相关损失的现场观测结果纳入内带的into模型。这将综合确定粒子的捕获和损失特征，作为能量、俯仰角、与地球的径向距离、地磁和太阳风参数的函数。多元统计技术将应用于这次太阳周期再分析，以了解短期内带空间天气如何导致长期内带气候。这种模拟、观测和数据同化统计技术的结合从未应用于内带来回答气候学问题。该项目将利用最近在内带建模方面取得的科学进展，并将为卫星设计界解决主要的辐射环境危害。内带的太阳周期再分析将直接提供给正在进行的更精确的辐射规格模型的开发，解决航天器工程界最优先的辐射环境需求。研究和运营之间的这种独特联系也发展了社区和跨学科之间的伙伴关系，并确保了结果的广泛传播。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This project will determine how short-term dynamics influences the long-term behavior of Earth's inner radiation belt (Van Allen belt). An existing physics based inner radiation belt model developed under a previous NSF Space Weather award will be the starting point of the analysis. Fifteen (15) years of in-situ observations of solar proton trapping and geomagnetic storm-associated losses will be incorporated into the into model of the inner belt using data assimilation techniques. This will comprehensively determine the trapping and loss signatures of the particles as functions of energy, pitch angle, radial distance from the Earth, and geomagnetic and solar wind parameters. Multivariate statistical techniques will be applied to this solar-cycle reanalysis to understand how short-term, inner belt space weather leads to long-term, inner belt climate. This combination of simulations, observations, and data-assimilation statistical techniques has never been applied to the inner belt to answer climatological questions. The project will capitalize on recent scientific progress made in the modeling of the inner belt and will address the primary radiation environment hazard for the satellite design community. The solar-cycle reanalysis of the inner belt will feed directly into the on-going development of more accurate radiation specification models, addressing the top priority radiation environment need of the spacecraft engineering community. This unique connection between research and operations also develops partnerships between communities and across disciplines, and ensures broad dissemination of the results.