Geomagnetic data assimilation based on an ensemble Kalman smoother: Synthetic tests

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ID: 331561
2026
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Abstract
Summary Geomagnetic data assimilation provides a framework for estimating the Earth’s core dynamics by combining geomagnetic observations with numerical geodynamo models. While the ensemble Kalman filter (EnKF) has been widely used as a sequential assimilation method, whether the ensemble Kalman smoother (EnKS), which additionally exploits future observations, improves estimates of the core state in fully nonlinear geodynamo models remains unclear. Here we compare the EnKS with the EnKF using synthetic observation experiments in three-dimensional geodynamo simulations under identical localization and inflation strategies. Under perfect-model conditions, the EnKS converges more rapidly and recovers additional flow modes, yielding more accurate estimates of both the internal velocity field and the core-surface flow. In contrast, the EnKS exhibits filter divergence in the magnetic field during the late stages of assimilation, reflecting its greater sensitivity to sampling errors. We then examine the influence of model error by introducing viscosity differences between the truth and assimilation models while maintaining otherwise nearly identical dynamics. Viscosity-induced model error degrades the EnKS, leading to reduced accuracy in recovering the dominant velocity modes and renewed filter divergence, although the large-scale core-surface flow remains well recovered. These results demonstrate that the EnKS can outperform the EnKF when the ensemble adequately represents the forecast-error covariance, but its more extensive use of observational information also makes it more susceptible to sampling and model errors. Improving the description of error covariances is therefore essential for realizing the full potential of EnKS-based geomagnetic data assimilation.
Reference Key
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Authors Zhipeng Zeng, Yufeng Lin
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Journal geophysical journal international
Year 2026
DOI
10.1093/gji/ggag413
URL
https://doi.org/10.1093/gji/ggag413
Keywords Keywords not found

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