Inference on core surface flow from observations and 3‐D dynamo modelling
Inference on core surface flow from observations and 3‐D dynamo modelling
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通过观测和 3D 发电机建模推断核心表面流动
DOI：
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发表时间：
2011
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通讯作者：
E. Thébault
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作者：
A. Fournier;J. Aubert;E. Thébault
摘要我们展示了如何使用地球发电机的 3D 自洽数值模型作为主观先验信息，用于根据地磁场及其长期变化确定地球核心表面流。这是通过标准卡尔曼滤波（或随机逆）程序估计观测值中隐藏的数值模型状态向量的部分来实现的，其中卡尔曼增益矩阵基于地球动力模型的多元统计。为了与观测值进行直接比较，输入这些统计数据的场变量按照最近在数值动力中脱颖而出的两个缩放定律进行缩放建模，表达了长期变化时间尺度和磁能密度对其各自控制参数的依赖性。我们使用嘈杂的合成数据进行测试实验，显示状态向量的隐藏部分具有良好到极好的恢复。然后，使用 2010 年发布的 IGRF 候选模型的地磁场模型进行核心表面流估计。估计的流量证实了美国地下存在对流柱，同时表现出高度的赤道对称性。我们建议这里考虑的离散状态估计问题（与经典的核心流问题相关）可以一般用作评估给定地球发电机模型的地球物理真实性程度的手段。更一般地说，这项研究为在更广泛的地磁数据同化背景下使用数值模型的标度定律和多元统计数据开辟了道路。
SUMMARY We show how a 3-D, self-consistent numerical model of the geodynamo can be used as the subjective prior information for the determination of Earth’s core surface flows from the geomagnetic field and its secular variation. This is achieved by estimating those parts of the numericalmodelstatevectorhiddenfromtheobservations,throughastandardKalmanfiltering (or stochastic inverse) procedure, where the Kalman gain matrix is based on the multivariate statisticsofthegeodynamomodel.Toallowforadirectcomparisonwithobservations,thefield variablesenteringthesestatisticsarescaledfollowingtwoofthescalinglawsthathaverecently come to the fore in numerical dynamo modelling, which express the dependency of the secular variation timescale and the magnetic energy density on their respective control parameters. We perform test experiments with noisy synthetic data, showing good to excellent recovery of the hidden parts of the state vector. A geomagnetic field model parent to a candidate model to the 2010 release of IGRF is then used for a core surface flow estimation. The estimated flow confirms the presence of convective columns underneath America, whereas exhibiting a high level of equatorial symmetry. We suggest that the discrete state estimation problem considered here (in connection with the classical core flow problem) could be used generically as a means to assess the degree of geophysical realism of a given geodynamo model. More generally, this study opens the way to using scaling laws and multivariate statistics from numerical models in the broader context of geomagnetic data assimilation.