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search.filters.applied.f.access: openAccess × Author: Stolle, Claudia × Start date: 2023 × End date: 2022 × DDC: 550 × Type: Article × WGL Institute: IAP ×

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    Using Principal Component Analysis of Satellite and Ground Magnetic Data to Model the Equatorial Electrojet and Derive Its Tidal Composition
    (Hoboken, NJ : Wiley, 2022) Soares, Gabriel; Yamazaki, Yosuke; Morschhauser, Achim; Matzka, Jürgen; Pinheiro, Katia J.; Stolle, Claudia; Alken, Patrick; Yoshikawa, Akimasa; Hozumi, Kornyanat; Kulkarni, Atul; Supnithi, Pornchai
    The intensity of the equatorial electrojet (EEJ) shows temporal and spatial variability that is not yet fully understood nor accurately modeled. Atmospheric solar tides are among the main drivers of this variability but determining different tidal components and their respective time series is challenging. It requires good temporal and spatial coverage with observations, which, previously could only be achieved by accumulating data over many years. Here, we propose a new technique for modeling the EEJ based on principal component analysis (PCA) of a hybrid ground-satellite geomagnetic data set. The proposed PCA-based model (PCEEJ) represents the observed EEJ better than the climatological EEJM-2 model, especially when there is good local time separation among the satellites involved. The amplitudes of various solar tidal modes are determined from PCEEJ based tidal equation fitting. This allows to evaluate interannual and intraannual changes of solar tidal signatures in the EEJ. On average, the obtained time series of migrating and nonmigrating tides agree with the average climatology available from earlier work. A comparison of tidal signatures in the EEJ with tides derived from neutral atmosphere temperature observations show a remarkable correlation for nonmigrating tides such as DE3, DE2, DE4, and SW4. The results indicate that it is possible to obtain a meaningful EEJ spectrum related to solar tides for a relatively short time interval of 70 days.
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    Machine learning-based calibration of the GOCE satellite platform magnetometers
    (Heidelberg : Springer, 2022) Styp-Rekowski, Kevin; Michaelis, Ingo; Stolle, Claudia; Baerenzung, Julien; Korte, Monika; Kao, Odej
    Additional datasets from space-based observations of the Earth’s magnetic field are of high value to space physics and geomagnetism. The use of platform magnetometers from non-dedicated satellites has recently successfully provided additional spatial and temporal coverage of the magnetic field. The Gravity and steady-state Ocean Circulation Explorer (GOCE) mission was launched in March 2009 and ended in November 2013 with the purpose of measuring the Earth’s gravity field. It also carried three platform magnetometers onboard. Careful calibration of the platform magnetometers can remove artificial disturbances caused by other satellite payload systems, improving the quality of the measurements. In this work, a machine learning-based approach is presented that uses neural networks to achieve a calibration that can incorporate a variety of collected information about the satellite system. The evaluation has shown that the approach is able to significantly reduce the calibration residual with a mean absolute residual of about 6.47nT for low- and mid-latitudes. In addition, the calibrated platform magnetometer data can be used for reconstructing the lithospheric field, due to the low altitude of the mission, and also observing other magnetic phenomena such as geomagnetic storms. Furthermore, the inclusion of the calibrated platform magnetometer data also allows improvement of geomagnetic field models. The calibrated dataset is published alongside this work. Graphical Abstract: [Figure not available: see fulltext.].