Multi-satellite Approach of Monitoring Atmosphere/Magnetosphere Space Weather Interactions (2020 - )
Over the past few decades, high-precision magnetic satellite missions have been steadily providing new insights into the Earth’s magnetic field and the processes that underlie it. Yet we do not have a full picture of the geophysical mechanisms by which the geomagnetic field is created. Electric currents flowing along polar geomagnetic field lines are an important mechanism by which energy is transferred between the magnetosphere at several Earth radii and the ionosphere, the ionised part of the upper atmosphere, which lies at an altitude of 100-300 km altitude. These field-aligned currents (FAC) cannot be detected on ground, but they produce significant signatures in observations of the magnetic field made by Low-Earth-Orbiting satellites, sometimes even becoming visible as auroral lights. These currents are highly fluctuating and high-precision magnetic field missions such as CHAMP and Swarm have been used to characterize them.
An international consortium has started an initiative to make us of data from other sources. A number of satellites carry magnetometers that were not originally designed for scientific applications, but rather for the purposes of navigation – they include Cryosat, GOCE, GRACE-FO, and many others. A careful calibration of data from these magnetometers permits extractions of the signature of currents from the Earth's magnetic field. Combining such data from several satellites will provide an unprecedented level of global coverage and should strongly enhance our ability to particularly monitor the polar ionosphere and its interaction with the magnetosphere. This data and a higher level of coverage can play an important role in understanding short-lived magnetic storms, which have a large impact on a variety of domains.
To achieve this, we can draw on a global dataset that has been collected continuously from diverse satellite missions since the year 2000. Combining all of this data into a common mapping procedure is challenging: it comprises different sampling rates, signal amplitudes, noise levels, and latency. Combining data of different sampling rate, signal amplitude, noise level, and latency in one mapping procedure requires special care for data handling and inter-calibration to achieve an unbiased result uniformly valid over the globe. The amplitude of the signal detected is subject to variation and noise due to descending satellite orbits, the architectural settings of missions and naturally varying levels of solar flux. This means that particular attention must be paid to data handling and inter-calibration to achieve an unbiased result that is uniformly valid across the globe. Our initial results, based on data collected from for the GRACE-FO1 satellite, show that it is indeed possible to detect FACs by calibrating platform magnetometer data, as shown in the attached figures.
Peer-reviewed Publications (journal or conference)
- K. Styp-Rekowski, C. Stolle, I. Michaelis, and O. Kao (2021). Calibration of the GRACE-FO satellite platform magnetometers and co-estimation of intrinsic time shift in data.IEEE International Conference on Big Data, 5283-5290. https://doi.org/10.1109/BigData52589.2021.9671977
C. Stolle, I. Michaelis, C. Xiong, M. Rother, T. Usbeck, Y. Yamazaki, J. Rauberg, and K. Styp-Rekowski (2021). Observing Earth’s magnetic environment with the GRACE-FO mission.Earth, Planets and Space, 73, 51. https://doi.org/10.1186/s40623-021-01364-w
I. Michaelis, K. Styp-Rekowski, J. Rauberg, C. Stolle, and M. Korte (2022). Geomagnetic data from the GOCE satellite mission. Earth, Planets, and Space, 74, 135. https://doi.org/10.1186/s40623-022-01691-6
K. Styp-Rekowski, I. Michaelis, C. Stolle, J. Baerenzung, M. Korte, and O. Kao (2022). Machine Learning-based Calibration of the GOCE Satellite Platform Magnetometers. Earth, Planets, and Space, 74, 138. https://doi.org/10.1186/s40623-022-01695-2
Other (presentations at conferences or preprints)
- K. Styp-Rekowski, C. Stolle, O. Kao, and I. Michaelis. Satellite Platform Magnetometer Calibration Using Machine Learning. (Oral Presentation), Joint Scientific Assembly IAGA-IASPEI, Online, 21-27 August 2021.
- K. Styp-Rekowski, C. Stolle, O. Kao, and I. Michaelis. Automatic Calibration of Satellite Platform Magnetometers with Neural Network-based Time Shift Approximation. (Oral Presentation), Joint Scientific Assembly IAGA-IASPEI, Online, 21-27 August 2021.
- K. Styp-Rekowski, C. Stolle, I. Michaelis, and O. Kao. Machine Learning-based Information Extraction from Non-dedicated Sensors. (Oral Presentation), Photonics Days Berlin Brandenburg, Berlin, Germany, 4-7 October 2021.
- K. Styp-Rekowski, C. Stolle, I. Michaelis, and O. Kao. Calibration of GRACE-FO and GOCE Platform Magnetometers Using Machine Learning. (Oral Presentation), Swarm Data Quality Workshop, Athens, Greece, 11-16 October 2021.
- K. Styp-Rekowski, C. Stolle, I. Michaelis, and O. Kao. Calibration of the GRACE-FO Satellite Platform Magnetometers and Co-Estimation of Intrinsic Time Shift in Data. (Oral Presentation), IEEE Big Data 2021, Online, 15-18 December 2021.
- K. Styp-Rekowski, I. Michaelis, C. Stolle, and O. Kao. Magnetic Datasets from Non-dedicated Satellites. (Oral Presentation), Living Planet Symposium, Bonn, Germany, 23-27 May 2022.
- K. Styp-Rekowski, I. Michaelis, C. Stolle, and O. Kao. Physics-informed Neural Network for Platform Magnetometer Calibration. (Oral Presentation), Swarm Data Quality Workshop, Uppsala, Sweden, 10-14 October 2022.