Characterizing magnetopause shadowing effects in the outer electron radiation belt during geomagnetic storms. Issue 10 (10th October 2016)

• Record Type: Journal Article
• Authors: Herrera, D.; Maget, V. F.; Sicard‐Piet, A.
• Is Part Of: Journal of geophysical research. Volume 121:Issue 10(2016:Oct.)
• Page Start: 9517
• Page End: 9530
• Publication Date: 2016-10-10
• Subjects: magnetopause shadowing -- dropouts -- radiation belts modeling; Magnetospheric physics -- Periodicals; Space environment -- Periodicals; Cosmic physics -- Periodicals; Planets -- Atmospheres -- Periodicals; Heliosphere (Astrophysics) -- Periodicals; Geophysics -- Periodicals; 523.01
• DOI: 10.1002/2016JA022825
• Languages: English
• ISSNs: 2169-9380

Abstract

Abstract: Relativistic electrons dynamics is still challenging to predict during the main phase of a storm. In particular, three dimensions radiation belt models, for which temporal resolution is limited, fail in predicting their behavior, especially when dropouts occur. In this paper we present a new model of magnetopause shadowing losses to be incorporated into the ONERA Salammbô code in order to improve the model accuracy. We show in this paper that above a few hundred keVs, magnetopause shadowing is the first contribution to losses in the outer electron belt during dropout events. Global variations of Earth‐magnetopause distance and relativistic electron flux have been analyzed to establish the correlation between the magnetopause shadowing and dropouts on the outer electron radiation belt during geomagnetic storms. To that purpose, a Superposed Epoch Analysis has been done using NOAA Polar‐orbiting Operational Environmental Satellite 15 measurements. First, a list of 67 Stream Interfaces has been used to validate the method, and then the Superposed Epoch Analysis has been run over more than one solar cycle. Our results show that the model of magnetopause location we have developed fits well with a Superposed Epoch Analysis performed and that we are able to define a criteria based on it that detect intense dropouts. Finally, we have included this model in the Salammbô code, and we present here the improvements obtained as well as the validation made. Key Points: The prime role of magnetopause shadowing during intense dropouts is highlighted in this work A simplistic but functional model of magnetopause shadowing is introduced An improved version of Salammbô code is presented, taking into account magnetopause shadowing effect‐induced losses