Issue |
J. Space Weather Space Clim.
Volume 15, 2025
|
|
---|---|---|
Article Number | 33 | |
Number of page(s) | 12 | |
DOI | https://doi.org/10.1051/swsc/2025031 | |
Published online | 31 July 2025 |
Technical Article
Statistical modelling of the variability of the electron radiation belts for long-term environment specification
1
DPHY, ONERA, Université de Toulouse, 31000 Toulouse, France
2
Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, 15236 Athens, Greece
3
Department of Physics, National and Kapodistrian University of Athens, 15784 Athens, Greece
4
European Space Research and Technology Centre (ESTEC), Space Environment and Effects Section, Keperlaan 1, 2200AG Noordwijk, The Netherlands
* Corresponding author: antoine.brunet@onera.fr
Received:
5
March
2025
Accepted:
5
July
2025
Specifying the electron radiation belts environment is crucial to enable satellite manufacturers and designers to adequately account for its harsh conditions, thereby mitigating risks of unforeseen malfunctions, data loss, and catastrophic failures that can impact spacecraft longevity. We present a novel statistical methodology for developing a radiation belt specification model that provides short- to long-term flux averages for spacecraft mission profiles, accounting for variability due to launch date and space weather/climate conditions. For this, we use an existing reanalysis database constructed using a physics-based radiation belt model and data assimilation. We analyse its flux distributions as well as the space and time correlation functions, and build a representative statistical model of the reanalysis database. Using this statistical representation, we build an innovative specification model prototype that is fast and easy to use, but can effectively be used for mission profiles at all timescales.
Key words: Radiation belts / Specification model / Statistical modelling
© A. Brunet et al., Published by EDP Sciences 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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