Parameterization of the spatial and temporal distribution of radial diffusion coefficients in the outer Van Allen belt

被引:0
|
作者
Katsavrias, C. [1 ]
Aminalragia-Giamini, S. [1 ,2 ]
Nasi, A. [1 ,3 ]
Papadimitriou, C. [1 ,2 ]
Daglis, I. A. [1 ,3 ]
机构
[1] Natl & Kapodistrian Univ Athens, Dept Phys, Athens, Greece
[2] Space Applicat & Res Consultancy SPARC, Athens, Greece
[3] Hellen Space Ctr, Athens, Greece
关键词
Van Allen radiation belts; Plasma astrophysics; Alfve<acute accent>n waves; Solar-terrestrial interactions; Radial diffusion; Relativistic electrons; RELATIVISTIC ELECTRONS; GEOMAGNETIC STORM; LOSSES;
D O I
10.1016/j.asr.2024.06.074
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
Radial diffusion in planetary radiation belts is a dominant transport mechanism resulting in the energization and losses of charged particles by large-scale electromagnetic fluctuations. In this work we exploit the extensive radial diffusion coefficients (DLL) database created in the framework of the Horizon 2020 SafeSpace project, which spans 9 years of hourly DLL coefficients, to investigate the spatiotemporal distributions of the coefficients. Our results indicate that the radial distribution of the magnetic and electric component of the DLL, as well as their sum, the total DLL, can be well described by a power law function of L* in the [4.3-7.7] range. We show that the L*dependent spectral index varies significantly and is far from constant as assumed and implemented in many semi-empirical models. We examine the quasi-periodic behavior of the radial profiles of the DLL throughout most of the 24th Solar cycle, which the data cover, and find an approximately 420 days dominant periodicity. This periodic behavior is linked (in terms of cross-wavelet analysis) with solar activity, nevertheless, its origin remains unclear. The uncovered features are important for understanding DLL behavior and drivers as well as for current and future modelling efforts. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
引用
收藏
页码:3597 / 3611
页数:15
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