Space Weather: Advancement and Validation of Real-Time Assimilative Mapping of Ionospheric Electrodynamics (AMIE) for Space Weather Applications

对高纬度电离层电势模式和极光结构及时掌握现实的知识对于空间气象研究和业务需要都很重要。该项目将推进电离层电动力学规范和预测的最新技术水平。具体而言，将开发一个新的、随时间变化的高纬度电势经验模型。将使用电离层电动力学同化测绘技术推导出各个电势模式。自1997年以来，已经进行了超过500万次AMIE反演，这些反演将构成数据库的核心。已经使用和将要使用的主要数据集是对地球表面磁场变化的测量。其他数据来源将包括来自Sondrestrom和Millstone Hill雷达的非相干散射雷达数据和来自电离层探测器的电子密度数据。建模的一个新特点是将全天空白色光图像纳入实时AMIE。这将极大地帮助极光的位置，范围和强度的实时规范。此外，它将允许通过电导的改善更好地规范电势。AMIE技术将通过引入自适应网格细化（AMR）来改进。AMR将由极地地球物理数据的间隔控制，以便在有多个数据源的区域，AMIE将以适当的比例解决特征。经验模型将包括极盖电位降的饱和效应。目前的经验模型没有考虑到饱和效应，这种效应发生在太阳风驱动力非常强的时候，因此他们预测在这样的时期，极盖电位会不切实际地大幅下降。为了验证经验模型，将开发一个自动化系统，在该系统中，实时AMIE模式连续地与DMSP颗粒沉淀和电势数据进行比较。然后将实时AMIE结果与经验模型进行比较。调查结果将上载于互联网，供市民查阅。该项目还具有教育/外联和多样性影响。一位来自底特律一个少数民族占主导地位的地区的高中科学教师将参加该项目，并将建立一个自动电子邮件系统，以便在当晚可能看到极光时通知订户。底特律地区的教师将使用这一工具帮助学生更多地了解近地空间环境。

A realistic timely knowledge of the high latitude ionospheric electric potential pattern and auroral configuration is important for both research and operational space weather needs. This project will advance the state of the art in specification and prediction of the ionospheric electrodynamics. Specifically, a new, time-dependent, empirical model of the high latitude electric potential will be developed. Individual electric potential patterns will be derived using the Assimilative Mapping of Ionospheric Electrodynamics (AMIE) technique. Since 1997, over 5 million AMIE inversions which have been performed and these inversions will form the core of the database. The primary data sets that have been used and will be used are measurements of the variations in the magnetic field at the earth's surface. Additional data sources will include incoherent scatter radar data from the Sondrestrom and Millstone Hill radars and electron density data derived from ionospheric sounders. A new feature of the modeling will be the inclusion of all-sky white light images into real-time AMIE. This will dramatically help the real-time specification of the auroral location, extent, and strength. In addition, it will allow a better specification of the electric potential through the improvement in the conductance. The AMIE technique will be improved by incorporating adaptive mesh refinement (AMR). The AMR will be controlled by the spacing of the polar geophysical data, such that in regions in which there are multiple data sources, AMIE will resolve features to the appropriate scale. The empirical model will include the effect of saturation of the polar cap potential drop. No current empirical model of the potential takes the saturation effect, which occurs when the solar wind driver is exceptionally strong, into account, and they therefore predict unrealistically large polar cap potential drops during such periods. In order to validate the empirical model, an automated system in which real-time AMIE patterns are continuously compared to DMSP particle precipitation and electric potential data will be developed. The real-time AMIE results will then be compared with the empirical model. The results will be posted on a web site for public inspection. The project also has an educational/outreach and diversity impact. A high school science teacher from a predominantly minority region of Detroit will take part in project, and an automated e-mail system will be set up whereby subscribers will be notified when it is likely that aurora may be viewed that particular night. Teachers in the Detroit area will use this tool to help students learn more about the near-Earth space environment.