1. The CSES Global Geomagnetic Field Model (CGGM): An IGRF type global geomagnetic field model based on data from the China Seismo-Electromagnetic Satellite
- Author
-
Xudong Zhao, Pierre Vigneron, Xuemin Zhang, Andreas Pollinger, Jingbo Yu, Feng Guo, Jie Wang, Werner Magnes, Xinghong Zhu, Shigeng Yuan, Bin Zhou, Roland Lammegger, Jianpin Huang, Lars Tøffner-Clausen, Nils Olsen, Yingyan Wu, Gauthier Hulot, Yanyan Yang, Jianpin Dai, Lanwei Wang, Xuhui Shen, Bingjun Cheng, Jun Lin, Zeren Zhima, National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing, 100085, China, Université de Paris, Institut de physique du globe de Paris, CNRS, F-75005 Paris, France, National Space Science Center, Chinese Academy of Sciences, Beijing, 100190, China, Space Research Institute, Austrian Academy of Sciences, Graz, 8042, Austria, DTU Space, National Space Institute, Technical University of Denmark, 2800 Kongens Lyngby, Denmark, Institute of Earthquake Forecasting, China Earthquake Administration, Beijing, 100036, China, DFH Satellite Co. Ltd., Beijing, 100081, China, Institute of Earthquake Forecasting, China Earthquake Administration, Beijing, 100036, Institute of Experimental Physics, Graz University of Technology, Graz, 8010, Austria, Beijing Special Engineering Design and research Institute, Beijing, 100028, China, China Centre for Resources Satellite Data and Application, Beijing, 100094, China, Hebei GEO University, Shijiazhuang 050031, China, Institute of Geophysics, China Earthquake Administration, Beijing, 100081, China, Institut de Physique du Globe de Paris (IPGP), Institut national des sciences de l'Univers (INSU - CNRS)-IPG PARIS-Université de La Réunion (UR)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Space Research Institute of Austrian Academy of Sciences (IWF), Austrian Academy of Sciences (OeAW), and Graz University of Technology [Graz] (TU Graz)
- Subjects
010504 meteorology & atmospheric sciences ,Computer science ,[SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph] ,lcsh:Geodesy ,010502 geochemistry & geophysics ,01 natural sciences ,CSES ,Space magnetometry ,0105 earth and related environmental sciences ,lcsh:QB275-343 ,CGGM ,Epoch (reference date) ,lcsh:QE1-996.5 ,lcsh:Geography. Anthropology. Recreation ,Swarm behaviour ,Geology ,Geomagnetism ,Geodesy ,Field (geography) ,Secular variation ,IGRF ,lcsh:Geology ,Data set ,Earth's magnetic field ,lcsh:G ,Space and Planetary Science ,Data quality ,Satellite - Abstract
Using magnetic field data from the China Seismo-Electromagnetic Satellite (CSES) mission, we derive a global geomagnetic field model, which we call the CSES Global Geomagnetic Field Model (CGGM). This model describes the Earth’s magnetic main field and its linear temporal evolution over the time period between March 2018 and September 2019. As the CSES mission was not originally designed for main field modelling, we carefully assess the ability of the CSES orbits and data to provide relevant data for such a purpose. A number of issues are identified, and an appropriate modelling approach is found to mitigate these. The resulting CGGM model appears to be of high enough quality, and it is next used as a parent model to produce a main field model extrapolated to epoch 2020.0, which was eventually submitted on October 1, 2019 as one of the IGRF-13 2020 candidate models. This CGGM candidate model, the first ever produced by a Chinese-led team, is also the only one relying on a data set completely independent from that used by all other candidate models. A successful validation of this candidate model is performed by comparison with the final (now published) IGRF-13 2020 model and all other candidate models. Comparisons of the secular variation predicted by the CGGM parent model with the final IGRF-13 2020–2025 predictive secular variation also reveal a remarkable agreement. This shows that, despite their current limitations, CSES magnetic data can already be used to produce useful IGRF 2020 and 2020–2025 secular variation candidate models to contribute to the official IGRF-13 2020 and predictive secular variation models for the coming 2020–2025 time period. These very encouraging results show that additional efforts to improve the CSES magnetic data quality could make these data very useful for long-term monitoring of the main field and possibly other magnetic field sources, in complement to the data provided by missions such as the ESA Swarm mission.
- Published
- 2021