Collaborative Research: CMG: Geomagnetic Data Assimilation and Dynamo Modeling

了解地球磁场的起源仍然是地球科学中最具挑战性的问题之一，这个问题对于理解地球深层内部的动力学及其与地球系统其他部分的相互作用至关重要。近年来，我们对这一领域起源的理解在两个不同的方面取得了巨大进展。首先，数值地球动力学模拟已经发展到数值模型产生与地球磁场相似的形态和时间行为的磁场。第二，新一代卫星磁场测量与地磁场模拟的进展相结合，为地球动力学过程提供了新的观测视角。但是，这两种方法在很大程度上是相互孤立的。为了整合这些方法，需要进行合作研究：将地磁观测同化到地磁发电机建模中，以便(1)更好地了解和预测地磁场的变化，以及(2)对地磁在地核中产生的动力学提供额外的见解。在本项目中，将定义和开发一个数据同化系统，该系统将使用most核心动力学模型（模块化和可扩展模型）和地磁场观测数据。该系统基于集合卡尔曼滤波（EnsKF），这是一种从集合预报中估计预报误差协方差的最优同化方法。该方法不需要建立发电机模型的伴随函数，减少了误差协方差的计算量。本研究的智力优势包括：(1)首次建立了地磁/地动力场的同化系统；(2)利用地表观测和数值地核动力学模型预测地球磁环境的变化。该研究的广泛影响包括：(1)将地磁观测应用于数值发电机模拟研究；(2)地核动力学在地磁场模拟研究中的应用；(3)岩心动力学研究在地球自转变化、时变重力场等地球物理研究中的应用；(4)地磁同化教育培训新项目

Understanding the origin of the Earth's magnetic field remains one of the most challenging problems in Earth Science, a problem that is central to understanding the dynamics of the Earth's deep interior and its interactions with other parts of the Earth system. Recent years have seen great advances on two separate fronts in our understanding of the origin of the field. First, numerical geodynamo modeling has progressed to the point where numerical models generate magnetic fields of similar morphology and temporal behavior to the Earth's field. Second, the combination of a new generation of satellite magnetic field measurements and advances in geomagnetic field modeling are providing new observational insight into the geodynamo process. But, these two approaches exist in large part in isolation from one another. A collaborative research is necessary to integrate these approaches: assimilating geomagnetic observations into core dynamo modeling to (1) understand better and forecast variations of the geomagnetic field, and to (2) provide additional insight into the dynamics of magnetic field generation in the core.In this project, a data assimilation system will be defined and developed with the MoSST core dynamics model (a modular and scalable model) and observations of the geomagnetic field. The proposed system is based on the ensemble Kalman filter (EnsKF) which is an optimal assimilation method to estimate the forecast error covariance from an ensemble of forecasts. This method eliminates the need to develop an adjoint of the dynamo model, and reduces the computational cost of calculating the error covariances. The intellectual merits of the proposed research include (1) the first-ever assimilation system in geomagnetic/geodynamo field; (2) prediction of changes in Earth's magnetic environment with surface observation and numerical core dynamics model. The broader impacts of the proposed research include (1) application of geomagnetic observations to numerical dynamo modeling research; (2) application of core dynamics to geomagnetic field modeling research; (3) application of core dynamics research to other geophysical studies, e.g. Earth's rotation variation and time variable gravity field; (4) new education/training project in geomagnetic assimilation