EAGER: Direct Assimilation of Low-altitude Magnetic Perturbations in a Global Magnetosphere Model

EAGER项目，探索新开发的先进数据同化方法，并在地球磁层的全球磁流体动力学模拟中实施这些方法。 虽然全球MHD磁层模型已经成熟到可以用于预测空间天气过程的程度，但它们面临的基本问题是任何现实物理系统的所有数值模型所共有的：边界/初始条件不充分或不明确，以及可能缺少物理条件。 为了解决这个问题，大气气象学界在过去的50年里一直在发展“数据同化”的方法。 在全球磁层建模中，数据同化方法的开发和实施受到系统巨大空间尺度和现有测量不可避免的时空覆盖率差的阻碍。 MHD中的数据同化特别具有挑战性，因为主要波模式是非线性的，非色散的，并且高度各向异性。 此外，现代全球MHD代码使用非常大的网格，高达数千万的细胞，导致巨大的协方差矩阵，不能解决经典的数据同化方法。 该项目将利用主要的新的电离层观测资产，特别是来自70颗卫星的铱星座的高频率磁强计数据。 由NSF AMPERE项目提供的低空（~800 km）磁扰动观测具有真正的全球连续覆盖，将用于指定全球MHD模型的关键内边界条件。 通过这种方式，该项目将尝试为全球磁层MHD模拟开发有史以来第一个数据同化方法。 如果成功，该项目的结果将为全球MHD模拟的变革性进展奠定基础，无论是作为科学探究的工具还是空间天气预报。

An EAGER project to explore newly developed advanced data assimilation methods and implement them in a global magneto-hydro-dynamic (MHD) simulation of the Earth's magnetosphere. While global MHD magnetospheric models have matured to the point when they can be used for prediction of space weather processes, the fundamental problem that they face is common to all numerical models of any realistic physical system: insufficiently or poorly specified boundary/initial conditions and, possibly, missing physics. To tackle this problem, the atmospheric weather community has been developing methods of "data assimilation" for the past ~50 years. In global magnetospheric modeling, the development and implementation of data assimilation methods is hampered by the enormous spatial scales of the system and inevitably poor spatiotemporal coverage of available measurements. Data assimilation in MHD is particularly challenging because the primary wave modes are non-linear, non-dispersive, and highly anisotropic. In addition, modern global MHD codes utilize very large grids with up to tens of millions of cells leading to huge covariance matrices that cannot be solved by classical data assimilation approaches. The project will utilize major new ionospheric observational assets, especially high-cadence magnetometer data from the 70-satellite Iridium constellation. The low-altitude (~800 km) observations of magnetic perturbations with truly global continuous coverage that are now provided by the NSF AMPERE project, will be used to specify a key inner boundary condition for global MHD models. In this way, the project will attempt the development of the first-ever data assimilation methodology for global magnetospheric MHD simulations. If successful, results from this project will lay the ground for transformative advances in global MHD simulations, both as tools of scientific inquiry and for space weather forecasting.