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Published on: July 30, 2020
Localization of the Cluster satellites in the geospace environment
Benjamin Grison1, Fabien Darrouzet2, Romain Maggiolo2
1Institute of Atmospheric Physics of the Czech Academy of Sciences (IAP), Department of Space Physics, 14100, Prague, Czech Republic. grison@ufa.cas.cz.
A new dataset labels Cluster spacecraft positions in Earth's geospace, aiding data analysis. This Geospace Region and Magnetospheric Boundary identification (GRMB) dataset maps spacecraft locations for better understanding of space physics.
Area of Science:
- Space Physics
- Geophysics
- Magnetospheric Physics
Background:
- Geospace, Earth's magnetized environment, is dynamic and spatially complex.
- Spacecraft positioning requires understanding the inhomogeneous geospace environment.
- Accurate location data is crucial for analyzing spacecraft observations.
Purpose of the Study:
- Introduce the Geospace Region and Magnetospheric Boundary identification (GRMB) dataset.
- Provide continuous, labelled positions for Cluster spacecraft throughout its mission.
- Enhance the analysis of Cluster mission data by contextualizing it within the local geospace environment.
Main Methods:
- Manual selection of spacecraft positions based on 44 Cluster data products.
- Development of 15 distinct labels for geospace regions, from plasmasphere to solar wind.
- Validation of dataset consistency over seven years using reference lists and physical properties.
Main Results:
- The GRMB dataset offers labelled positions for Cluster spacecraft across its entire mission.
- The dataset encompasses 15 distinct geospace regions, including plasmasphere, lobe, plasmasheet, magnetosheath, and solar wind.
- Cluster spacecraft spent approximately 15% of its mission time in the lobe, plasmasheet, plasmasheet transition region, magnetosheath, and solar wind.
Conclusions:
- The GRMB dataset provides a valuable, validated resource for geospace research.
- Continuous environmental labelling significantly supports the interpretation of spacecraft data.
- The dataset facilitates a deeper understanding of the spatial and temporal dynamics of geospace regions.
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