Collaborative Research: CMG: Geomagnetic Data Assimilation and Dynamo Modeling

合作研究：CMG：地磁数据同化和发电机建模

• 批准号: 0327843
• 负责人: Jeremy Bloxham
• 金额: --
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0327843&HistoricalAwards=false
• 关键词: Collaborative; Research; CMG; Geomagnetic; Data

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

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，这个问题对于了解地球深层内部的动态及其与地球系统其他部分的相互作用至关重要。近年来，在我们对该领域起源的理解方面，在两个不同的战线上取得了巨大进展。首先，数值地球发电机建模已经发展到数值模型产生与地球磁场具有相似形态和时间行为的磁场的程度。 第二，新一代卫星磁场测量与地磁场建模方面的进展相结合，为地球发电机过程提供了新的观测见解。但是，这两种方法在很大程度上是相互孤立的。 有必要进行合作研究，以整合这些方法：将地磁观测同化到地核发电机模型中，以（1）更好地理解和预测地磁场的变化，以及（2）提供对地核中磁场生成动力学的额外见解。在该项目中，将利用MoSST核心动力学模型（一个模块化和可扩展的模型）和地磁场观测来确定和开发一个数据同化系统。建议的系统是基于集合卡尔曼滤波（EnsKF），这是一个最佳的同化方法来估计预报误差协方差从集合的预测。该方法消除了需要开发的发电机模型的伴随，并减少了计算误差协方差的计算成本。这项研究的智力价值包括：（1）第一个地磁/地球发电机场同化系统;（2）利用地面观测和数值核心动力学模型预测地球磁环境的变化。 建议研究的更广泛影响包括（1）将地磁观测应用于数值发电机模型研究;（2）将核心动力学应用于地磁场模型研究;（3）将核心动力学研究应用于其他地球物理研究，例如地球自转变化和时变重力场;（4）新的地磁同化教育/培训项目