Simulating Long‐Term Dynamics of Radiation Belt Electrons Using DREAM3D Model

Lee, Sang‐Yun; Tu, Weichao; Cunningham, Gregory S.; Cowee, Misa M.; Wang, D.; SHPRITS, YURI; Henderson, Michael G.; Reeves, Geoffrey D.

doi:10.1029/2023JA032286

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Simulating Long‐Term Dynamics of Radiation Belt Electrons Using DREAM3D Model

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Lee, Sang‐Yun
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Tu, Weichao
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Cunningham, Gregory S.
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Cowee, Misa M.
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Wang, D.
2.7 Space Physics and Space Weather, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

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SHPRITS, YURI
2.7 Space Physics and Space Weather, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Henderson, Michael G.
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Reeves, Geoffrey D.
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Citation

Lee, S., Tu, W., Cunningham, G. S., Cowee, M. M., Wang, D., SHPRITS, Y., Henderson, M. G., Reeves, G. D. (2024): Simulating Long‐Term Dynamics of Radiation Belt Electrons Using DREAM3D Model. - Journal of Geophysical Research: Space Physics, 129, 2, e2023JA032286.
https://doi.org/10.1029/2023JA032286

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5025471

Abstract

We compared the performance of DREAM3D simulations in reproducing the long‐term radiation belt dynamics observed by Van Allen Probes over the entire year of 2017 with various boundary conditions (BCs) and model inputs. Specifically, we investigated the effects of three different outer boundary conditions, two different low‐energy boundary conditions for seed electrons, four different radial diffusion (RD) coefficients (DLL), four hiss wave models, and two chorus wave models from the literature. Using the outer boundary condition driven by GOES data, our benchmark simulation generally well reproduces the observed radiation belt dynamics inside L* = 6, with a better model performance at lower μ than higher μ, where μ is the first adiabatic invariant. By varying the boundary conditions and inputs, we find that: (a) The data‐driven outer boundary condition is critical to the model performance, while adding in the data‐driven seed population doesn't further improve the performance. (b) The model shows comparable performance with DLL from Brautigam and Albert (2000, https://doi.org/10.1029/1999ja900344), Ozeke et al. (2014, https://doi.org/10.1002/2013ja019204), and Liu et al. (2016, https://doi.org/10.1002/2015gl067398), while with DLL from Ali et al. (2016, https://doi.org/10.1002/2016ja023002) the model shows less RD compared to data. (c) The model performance is similar with data‐based hiss models, but the results show faster loss is still needed inside the plasmasphere. (d) The model performs similarly with the two different chorus models, but better capturing the electron enhancement at higher μ using the Wang et al. (2019, https://doi.org/10.1029/2018ja026183) model due to its stronger wave power, since local heating for higher energy electrons is under‐reproduced in the current model.