Assimilating geomagnetic data with dynamo simulations: a window to the Earth's core dynamics

地磁场的变化提供了了解地球动态铁芯的唯一窗口。我们建议开发一个数据同化框架，结合观测和数值发电机模拟，以产生一个现实的地球核心动力学模型。虽然数据同化通常用于天气预报，但其在地磁学中的应用仍处于起步阶段。特别的挑战在于，地磁数据是在远离核心发电机的地方测量的，而且相对较少的数据必须约束一个复杂的过程。在我们选择的方法中，Ensemble卡尔曼滤波器，观测和核心动态之间的联系是基于并行运行的几个数值发电机模拟的统计相关性。主要目标是a）提供对地球发电机模拟中的基本成分的理解，B）开发从1900年到今天的整个核心动力学的模型，以及c）使用该模型预测几十年的地磁场演化。广泛的数值研究将有助于确定不同的同化技术，发电机模型的复杂性，合奏大小和计算成本之间的最佳折衷。这项工作将与SPP的其他团队合作，帮助建立一个最新的综合视图，从磁层电流系统到在我们星球的深核运行的发电机过程，地球的各种磁性。

Variations in the geomagnetic field provide the only window into the dynamic iron core of planet Earth. We propose to develop a data assimilation framework that combines observations and numerical dynamo simulations in order to yield a realistic model of the Earth’s core dynamics. While data assimilation is routinely used in weather prediction, its application to geomagnetism is still in its infancy. The particular challenges lie in the fact that geomagnetic data are measured far away from the core dynamo and that relatively few data have to constrain a complex process. In our method of choice, the Ensemble Kalman Filter, the link between observations and core dynamics is based on the statistical correlations from several numerical dynamo simulations running in parallel. The main objectives are a) to provide an understanding of the essential ingredients in geodynamo simulations, b) to develop a model for the whole core dynamics from 1900 to today, and c) to use this model for forecasting the geomagnetic field evolution over several decades. Extensive numerical studies will serve to identify the best compromise between different assimilation techniques, dynamo model complexity, ensemble size, and computational costs. In collaboration with other SPP teams, this work will help to establish an up-to-date and integrated view of the Earth’s diverse magnetism from magnetospheric current systems to the dynamo process operating in our planet’s deep core.