Proton aurora are the most commonly observed yet least studied type of aurora at Mars. In order to better understand the physics and driving processes of Martian proton aurora, we undertake a multi-model comparison campaign. We compare results from four different proton/hydrogen precipitation models with unique abilities to represent Martian proton aurora: Jolitz model (3-D Monte Carlo), Kallio model (3-D Monte Carlo), Bisikalo/Shematovich et al. model (1-D kinetic Monte Carlo), and Gronoff et al. model (1-D kinetic). This campaign is divided into two steps: an inter-model comparison and a data-model comparison. The inter-model comparison entails modeling five different representative cases using similar constraints in order to better understand the capabilities and limitations of each of the models. Through this step we find that the two primary variables affecting proton aurora are the incident solar wind particle flux and velocity. In the data-model comparison, we assess the robustness of each model based on its ability to reproduce a MAVEN/IUVS proton aurora observation. All models are able to effectively simulate the data. Variations in modeled intensity and peak altitude can be attributed to differences in model capabilities/solving techniques and input assumptions (e.g., cross sections, 3-D versus 1-D solvers, and implementation of the relevant physics and processes). The good match between the observations and multiple models gives a measure of confidence that the appropriate physical processes and their associated parameters have been correctly identified and provides insight into the key physics that should be incorporated in future models.
JGR: Space Physics
ESS Open Archive
Grant or Award Name
NASA grant 80NSSC20K1348, Russian Science Foundation grant # 22-12-00364, NSF awards ACI-1532235 and ACI-1532236, Austrian Science Fund (FWF) project P35954-N
Scholarly Commons Citation
Andrea C. G. Hughes, Michael Scott Chaffin, Edwin J. Mierkiewicz, et al. Advancing Our Understanding of Martian Proton Aurora through a Coordinated Multi-Model Comparison Campaign. ESS Open Archive. July 23, 2023.