Accounting for correlated data errors in geomagnetic field modeling using Swarm magnetic observations

Studies of Earth’s magnetic field and its sources rely on accurate geomagnetic field models derived from ground and satellite-based magnetic data. During the field model estimation, data errors are usually assumed to be uncorrelated in time and independent of position. However, limitations in the field model parameterization, especially regarding ionospheric and magnetospheric fields, lead to data errors that are not only larger than the expected measurement noise but are also correlated in time and vary with position. As a result, the obtained model uncertainties are often underestimated, making it more challenging to evaluate the reliability of recovered signals in the field models.

This study investigates the effect of including correlated data errors in field modeling. The approach involves building a stochastic data error model to treat correlated errors due to unmodeled magnetospheric fields within the CHAOS geomagnetic field modeling framework. The error model parameters are estimated using empirical covariances computed from vector residuals between the satellite magnetic observations made by the Swarm satellites and the CHAOS geomagnetic field model. Field modeling experiments are performed with and without including the data error covariances described in the stochastic error model.

The inclusion of data error covariances due to unmodeled magnetospheric fields leads to only small changes in the estimated internal field, but also a noticeable increase in model uncertainty for the sectoral coefficients. This highlights the significant impact of unmodeled magnetospheric fields and the importance of accurately defining data errors, including the covariances between observations, for interpreting the retrieved magnetic signals in geomagnetic field modeling.