Space Weather: Dynamics of Regional and Local High-Latitude Geomagnetic Disturbances

该项目将进一步发展最初由美国国家科学基金会早期资助的模型，以建立地面磁场波动的空间天气模型。目前的模型是一个线性动力学模型，训练了芬兰磁力计阵列、IMAGE、地磁PC指数和最近太阳风输入的历史测量数据。这些元素代表了地磁活动和全球磁层的状态，以及通过日侧磁重联获得的能量的量度。该模型对纬度55-90度的水平（2D）油田进行了估计。磁场的动力学近似为振荡和生长/衰减，它们由PC指数和太阳风输入驱动。所提出的模型将被训练以预测完整的三维地磁场。将单独开发一个类似的模型，以磁场的时间导数来预测磁场波动。时间导数特别重要，因为地磁感应电流是由磁场的突然变化驱动的，它可以影响配电网。这些模型将是非线性的，这意味着它们的系数将由地磁活动水平参数化。从线性动力学到非线性动力学的过渡将提高预测精度。将研究太阳风和行星际磁场输入对预测精度的影响。太阳风-磁层耦合过程不仅取决于纬度，而且取决于当地时间。模型的每个位置都可以由不同的太阳风/IMF输入函数驱动。该模型可以动态地适应太阳风条件的变化，但重要的是要根据正确的地磁条件进行定期修正。数据同化将用于摄取实时地磁测量数据。由于该模型可以写成局部场（或其波动）的滤波器，因此将其扩展为包括卡尔曼滤波器。卡尔曼分量将调整模型的状态，使其迅速收敛于测量点的实际条件。地磁场的动态模型有几个重要的应用。首先，了解地磁响应及其对太阳风输入和磁层结构的依赖是磁层等离子体物理学中的一个重要问题。该模型可以作为一种工具来量化这种复杂的耦合作为行星际条件和磁层状态的函数。此外，该模型的某些方面可以作为学生磁层和电离层等离子体物理训练的一部分。

This project will further develop a model originally funded with earlier NSF support to a space weather model for the fluctuations of ground magnetic field. The current model is a linear dynamical model trained on historic measurements taken by the Finnish magnetometer array, IMAGE, the geomagnetic PC index, and the recent history of the solar wind inputs. These elements represent the state of geomagnetic activity and of the global magnetosphere, and a measure of the energy available through dayside magnetic reconnection. The model produces an estimate of the horizontal (2D) field at latitudes 55-90 degrees. The dynamics of the field are approximated as oscillation and growth/decay and they are driven by the PC index and solar wind inputs. The proposed model will be trained to predict the full 3D ground magnetic field. A similar model will be separately developed to predict the field fluctuations in terms of the time derivatives of the magnetic field. Time derivatives are of particular importance because geomagnetically induced currents, which can effect the electric power distribution grid, are driven by sudden changes in the magnetic field. The models will be nonlinear, meaning that their coefficients will be parameterized by the geomagnetic activity level. The transition from linear to nonlinear dynamics will increase the prediction accuracy. The effects of the solar wind and interplanetary magnetic field (IMF) inputs on prediction accuracy will be examined. The solar wind-magnetosphere coupling process depends on local time as well as on latitude. Each location of the model can be driven by a distinct solar wind/IMF input function. The proposed model can dynamically adapt to changing solar wind conditions, but it is important that it be regularly corrected by the correct present state of geomagnetic conditions. Data assimilation will be used to ingest real-time geomagnetic measurements. Because the model can be written as a filter for the local field (or its fluctuations), it will be extended to include a Kalman filter. The Kalman component will adjust the state of the model so that it rapidly converges on realistic conditions at the points of measurement. A dynamic model for the ground magnetic field has several important applications. First, understanding the geomagnetic response and its dependence on solar wind input and the magnetospheric configuration is an important question in magnetospheric plasma physics. The model can be used as a tool to quantify this complex coupling as a function of interplanetary conditions and the magnetospheric state. In addition, some aspects of the model can be used as part of student training on magnetospheric and ionospheric plasma physics.