A Novel Neural Network‐Based Approach to Derive a Local Geomagnetic Baseline for Future Robust Characterization of Geomagnetic Indices at Mid‐Latitude

Abstract Geomagnetic indices derived from ground magnetic measurements characterize the intensity of solar‐terrestrial interaction. Global magnetic indices derived from multiple magnetic observatories at mid‐latitude have commonly been used for space weather operations. Yet, their temporal cadence is low and their intensity scale is crude. To derive a new generation of geomagnetic indices, it is desirable to establish a geomagnetic baseline that defines the quiet‐level of activity without solar‐driven perturbations. We present a new approach for deriving a baseline that represents the time‐dependent quiet variations focusing on data from Chambon‐la‐Forêt, France. Using a filtering technique, the measurements are first decomposed into the above‐diurnal (24 hr) variation and the sum of 24, 12, 8, and 6 hr filters, called the daily variation. Using parameters that correlate with the ionospheric solar‐quiet (Sq) currents, we predict the daily “quiet” variation that excludes the effects of solar‐transient perturbations. Here, we train long short‐term memory neural networks using at least 11 years of data at 1 hr cadence. This predicted daily quiet variation is combined with linear extrapolation of the secular trend associated with the intrinsic geomagnetic variability, which dominates the above‐diurnal variation, to yield a local geomagnetic baseline. Unlike the existing baselines, our baseline is insensitive to geomagnetic storms. It is thus suitable for future definitions of geomagnetic indices that accurately reflect the intensity of solar‐driven perturbations. Our methodology is quick to implement and scalable, making it suitable for real‐time operation. Strategies for operational forecasting of our geomagnetic baseline 1 day and 27 days in advance are presented.