An Ensemble Algorithm Based Component for Geomagnetic Data Assimilation

Geomagnetic data assimilation is one of the most recent developments in geomagnetic studies. It combines geodynamo model outputs and surface geomagnetic observations to provide more accurate estimates of the core dynamic state and provide accurate geomagnetic secular variation forecasting. To facilitate geomagnetic data assimilation studies, we develop a stand- alone data assimilation component for the geomagnetic community. This component is used to calculate the forecast error covariance matrices and the gain matrix from a given geodynamo solution, which can then be used for sequential geomagnetic data assimilation. This component is very flexible and can be executed independently. It can also be easily integrated with arbitrary dynamo models.

Abstract

Geomagnetic data assimilation is one of the most recent developments in geomagnetic studies. It combines geodynamo model outputs and surface geomagnetic observations to provide more accurate estimates of the core dynamic state and provide accurate geomagnetic secular variation forecasting. To facilitate geomagnetic data assimilation studies, we develop a stand- alone data assimilation component for the geomagnetic community. This component is used to calculate the forecast error covariance matrices and the gain matrix from a given geodynamo solution, which can then be used for sequential geomagnetic data assimilation. This component is very flexible and can be executed independently. It can also be easily integrated with arbitrary dynamo models.

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