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Journal of Space Weather and Space Climate, volume 8, pages A04

Two solar proton fluence models based on ground level enhancement observations

Osku Raukunen 1
R. Vainio 1
Allan J. Tylka 2
William F. Dietrich 3
P. Jiggens 4
D. Heynderickx 5
Mark Dierckxsens 6
Norma Crosby 6
Urs Ganse 7
Robert Siipola 1
Show full list: 10 authors
Publication typeJournal Article
Publication date2018-01-26
scimago Q2
SJR1.056
CiteScore6.9
Impact factor3.4
ISSN21157251
Space and Planetary Science
Atmospheric Science
Abstract

Solar energetic particles (SEPs) constitute an important component of the radiation environment in interplanetary space. Accurate modeling of SEP events is crucial for the mitigation of radiation hazards in spacecraft design. In this study we present two new statistical models of high energy solar proton fluences based on ground level enhancement (GLE) observations during solar cycles 19–24. As the basis of our modeling, we utilize a four parameter double power law function (known as the Band function) fits to integral GLE fluence spectra in rigidity. In the first model, the integral and differential fluences for protons with energies between 10 MeV and 1 GeV are calculated using the fits, and the distributions of the fluences at certain energies are modeled with an exponentially cut-off power law function. In the second model, we use a more advanced methodology: by investigating the distributions and relationships of the spectral fit parameters we find that they can be modeled as two independent and two dependent variables. Therefore, instead of modeling the fluences separately at different energies, we can model the shape of the fluence spectrum. We present examples of modeling results and show that the two methodologies agree well except for a short mission duration (1 year) at low confidence level. We also show that there is a reasonable agreement between our models and three well-known solar proton models (JPL, ESP and SEPEM), despite the differences in both the modeling methodologies and the data used to construct the models.

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