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Abstract We describe our first attempt to systematically simulate the solar wind during different phases of the last solar cycle with the Alfvén Wave Solar atmosphere Model (AWSoM) developed at the University of Michigan. Key to this study is the determination of the optimal values of one of the most important input parameters of the model, the Poynting flux parameter, which prescribes the energy flux passing through the chromospheric boundary of the model in the form of Alfvén wave turbulence. It is found that the optimal value of the Poynting flux parameter is correlated with the area of the open magnetic field regions with the Spearman’s correlation coefficient of 0.96 and anticorrelated with the average unsigned radial component of the magnetic field with the Spearman’s correlation coefficient of −0.91. Moreover, the Poynting flux in the open field regions is approximately constant in the last solar cycle, which needs to be validated with observations and can shed light on how Alfvén wave turbulence accelerates the solar wind during different phases of the solar cycle. Our results can also be used to set the Poynting flux parameter for real-time solar wind simulations with AWSoM. 

Abstract We perform a geomagnetic event simulation using a newly developed magnetohydrodynamic with adaptively embedded particle‐in‐cell (MHD‐AEPIC) model. We have developed effective criteria to identify reconnection sites in the magnetotail and cover them with the PIC model. The MHD‐AEPIC simulation results are compared with Hall MHD and ideal MHD simulations to study the impacts of kinetic reconnection at multiple physical scales. At the global scale, the three models produce very similar SYM‐H and SuperMag Electrojet indexes, which indicates that the global magnetic field configurations from the three models are very close to each other. We also compare the ionospheric solver results and all three models generate similar polar cap potentials and field‐aligned currents. At the mesoscale, we compare the simulations with in situ Geotail observations in the tail. All three models produce reasonable agreement with the Geotail observations. At the kinetic scales, the MHD‐AEPIC simulation can produce a crescent shape distribution of the electron velocity space at the electron diffusion region, which agrees very well with MMS observations near a tail reconnection site. These electron scale kinetic features are not available in either the Hall MHD or ideal MHD models. Overall, the MHD‐AEPIC model compares well with observations at all scales, it works robustly, and the computational cost is acceptable due to the adaptive adjustment of the PIC domain. It remains to be determined whether kinetic physics can play a more significant role in other types of events, including but not limited to substorms. 

Abstract The vast size of the Sun’s heliosphere, combined with sparse spacecraft measurements over that large domain, makes numerical modeling a critical tool to predict solar wind conditions where there are no measurements. This study models the solar wind propagation in 2D using the BATSRUS MHD solver to form the MSWIM2D data set of solar wind in the outer heliosphere. Representing the solar wind from 1 to 75 au in the ecliptic plane, a continuous model run from 1995–present has been performed. The results are available for free athttp://csem.engin.umich.edu/mswim2d/. The web interface extracts output at desired locations and times. In addition to solar wind ions, the model includes neutrals coming from the interstellar medium to reproduce the slowing of the solar wind in the outer heliosphere and to extend the utility of the model to larger radial distances. The inclusion of neutral hydrogen is critical to recreating the solar wind accurately outside of ∼4 au. The inner boundary is filled by interpolating and time-shifting in situ observations from L1 and STEREO spacecraft when available. Using multiple spacecraft provides a more accurate boundary condition than a single spacecraft with time shifting alone. Validations of MSWIM2D are performed using MAVEN and New Horizons observations. The results demonstrate the efficacy of this model to propagate the solar wind to large distances and obtain practical, useful solar wind predictions. For example, the rms error of solar wind speed prediction at Mars is only 66 km s⁻¹and at Pluto is a mere 25 km s⁻¹.