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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.
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1 Ministry of Emergency Management of China, National Institute of Natural Hazards, Beijing, China
2 Université de Paris, Institut de Physique du Globe de Paris, CNRS, Paris, France
3 Chinese Academy of Sciences, National Space Science Center, Beijing, China (GRID:grid.9227.e) (ISNI:0000000119573309)
4 Austrian Academy of Sciences, Space Research Institute, Graz, Austria (GRID:grid.4299.6) (ISNI:0000 0001 2169 3852)
5 Technical University of Denmark, DTU Space, National Space Institute, Kongens Lyngby, Denmark (GRID:grid.5170.3) (ISNI:0000 0001 2181 8870)
6 Ministry of Emergency Management of China, National Institute of Natural Hazards, Beijing, China (GRID:grid.5170.3)
7 China Earthquake Administration, Institute of Earthquake Forecasting, Beijing, China (GRID:grid.450296.c) (ISNI:0000 0000 9558 2971)
8 DFH Satellite Co. Ltd., Beijing, China (GRID:grid.450296.c)
9 Ministry of Emergency Management of China, National Institute of Natural Hazards, Beijing, China (GRID:grid.450296.c)
10 Graz University of Technology, Institute of Experimental Physics, Graz, Austria (GRID:grid.410413.3) (ISNI:0000 0001 2294 748X)
11 Beijing Special Engineering Design and Research Institute, Beijing, China (GRID:grid.410413.3)
12 China Centre for Resources Satellite Data and Application, Beijing, China (GRID:grid.506891.3)
13 Ministry of Emergency Management of China, National Institute of Natural Hazards, Beijing, China (GRID:grid.506891.3)
14 Hebei GEO University, Shijiazhuang, China (GRID:grid.443566.6) (ISNI:0000 0000 9730 5695)
15 Ministry of Emergency Management of China, National Institute of Natural Hazards, Beijing, China (GRID:grid.443566.6)
16 China Earthquake Administration, Institute of Geophysics, Beijing, China (GRID:grid.450296.c) (ISNI:0000 0000 9558 2971)