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Abstract Block-Adaptive-Tree Solar-wind Roe-type Upwind Scheme (BATSRUS), our state-of-the-art extended magnetohydrodynamic code, is the most used and one of the most resource-consuming models in the Space Weather Modeling Framework. It has always been our objective to improve its efficiency and speed with emerging techniques, such as GPU acceleration. To utilize the GPU nodes on modern supercomputers, we port BATSRUS to GPUs with the OpenACC API. Porting the code to a single GPU requires rewriting and optimizing the most used functionalities of the original code into a new solver, which accounts for around 1% of the entire program in length. To port it to multiple GPUs, we implement a new message-passing algorithm to support its unique block-adaptive grid feature. We conduct weak scaling tests on as many as 256 GPUs and find good performance. The program has 50%–60% parallel efficiency on up to 256 GPUs and up to 95% efficiency within a single node (four GPUs). Running large problems on more than one node has reduced efficiency due to hardware bottlenecks. We also demonstrate our ability to run representative magnetospheric simulations on GPUs. The performance for a single A100 GPU is about the same as 270 AMD “Rome” CPU cores (2.1 128-core nodes), and it runs 3.6 times faster than real time. The simulation can run 6.9 times faster than real time on four A100 GPUs. 

Abstract Forecasting the arrival time of Earth‐directed coronal mass ejections (CMEs) via physics‐based simulations is an essential but challenging task in space weather research due to the complexity of the underlying physics and limited remote and in situ observations of these events. Data assimilation techniques can assist in constraining free model parameters and reduce the uncertainty in subsequent model predictions. In this study, we show that CME simulations conducted with the Space Weather Modeling Framework (SWMF) can be assimilated with SOHO LASCO white‐light (WL) observations and solar wind observations at L1 prior to the CME eruption to improve the prediction of CME arrival time. The L1 observations are used to constrain the model of the solar wind background into which the CME is launched. Average speed of CME shock front over propagation angles are extracted from both synthetic WL images from the Alfvén Wave Solar atmosphere Model (AWSoM) and the WL observations. We observe a strong rank correlation between the average WL speed and CME arrival time, with the Spearman's rank correlation coefficients larger than 0.90 for three events occurring during different phases of the solar cycle. This enables us to develop a Bayesian framework to filter ensemble simulations using WL observations, which is found to reduce the mean absolute error of CME arrival time prediction from about 13.4 to 5.1 hr. The results show the potential of assimilating readily available L1 and WL observations within hours of the CME eruption to construct optimal ensembles of Sun‐to‐Earth CME simulations. 

Abstract A potential field solution is widely used to extrapolate the coronal magnetic field above the Sun’s surface to a certain height. This model applies the current-free approximation and assumes that the magnetic field is entirely radial beyond the source surface height, which is defined as the radial distance from the center of the Sun. Even though the source surface is commonly specified at 2.5R_s(solar radii), previous studies have suggested that this value is not optimal in all cases. In this study, we propose a novel approach to specify the source surface height by comparing the areas of the open magnetic field regions from the potential field solution with predictions made by a magnetohydrodynamic model, in our case the Alfvén Wave Solar atmosphere Model. We find that the adjusted source surface height is significantly less than 2.5R_snear solar minimum and slightly larger than 2.5R_snear solar maximum. We also report that the adjusted source surface height can provide a better open flux agreement with the observations near the solar minimum, while the comparison near the solar maximum is slightly worse. 

Abstract Magnetic switchbacks are rapid high-amplitude reversals of the radial magnetic field in the solar wind that do not involve a heliospheric current sheet crossing. First seen sporadically in the 1970s in Mariner and Helios data, switchbacks were later observed by the Ulysses spacecraft beyond 1 au and have been recently discovered to be a typical component of solar wind fluctuations in the inner heliosphere by the Parker Solar Probe spacecraft. While switchbacks are now well understood to be spherically polarized Alfvén waves thanks to Parker Solar Probe observations, their formation has been an intriguing and unsolved puzzle. Here we provide a simple yet predictive theory for the formation of these magnetic reversals: the switchbacks are produced by the distortion and twisting of circularly polarized Alfvén waves by a transversely varying radial wave propagation velocity. We provide an analytic expression for the magnetic field variation, establish the necessary and sufficient conditions for the formation of switchbacks, and show that the proposed mechanism works in a realistic solar wind scenario. We also show that the theoretical predictions are in excellent agreement with observations, and the high-amplitude radial oscillations are strongly correlated with the shear of the wave propagation speed. The correlation coefficient is around 0.3–0.5 for both encounter 1 and encounter 12. The probability of this being a lucky coincidence is essentially zero withp-values below 0.1%. 

Abstract We explore the performance of the Alfvén Wave Solar atmosphere Model with near-real-time (NRT) synoptic maps of the photospheric vector magnetic field. These maps, produced by assimilating data from the Helioseismic Magnetic Imager (HMI) on board the Solar Dynamics Observatory, use a different method developed at the National Solar Observatory (NSO) to provide a near contemporaneous source of data to drive numerical models. Here, we apply these NSO-HMI-NRT maps to simulate three full Carrington rotations: 2107.69 (centered on the 2011 March 7 20:12 CME event), 2123.5 (centered on 2012 May 11), and 2219.12 (centered on the 2019 July 2 solar eclipse), which together cover various activity levels for solar cycle 24. We show the simulation results, which reproduce both extreme ultraviolet emission from the low corona while simultaneously matching in situ observations at 1 au as well as quantify the total unsigned open magnetic flux from these maps. 

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 use the Space Weather Modeling Framework Geospace configuration to simulate a total of 122 storms from the period 2010–2019. With the focus on the storm main phase, each storm period was run for 54 hr starting from 6 hr prior to the start of the Dst depression. The simulation output of ground magnetic variations, ΔB_Hin particular, were compared with ground magnetometer station data provided by SuperMAG to statistically assess the Geospace model regional magnetic perturbation prediction performance. Our results show that the regional predictions at mid‐latitudes are quite accurate, but the high‐latitude regional disturbances are still difficult to predict. 

Abstract Modeling the impact of space weather events such as coronal mass ejections (CMEs) is crucial to protecting critical infrastructure. The Space Weather Modeling Framework is a state‐of‐the‐art framework that offers full Sun‐to‐Earth simulations by computing the background solar wind, CME propagation, and magnetospheric impact. However, reliable long‐term predictions of CME events require uncertainty quantification (UQ) and data assimilation. We take the first steps by performing global sensitivity analysis (GSA) and UQ for background solar wind simulations produced by the Alfvén Wave Solar atmosphere Model (AWSoM) for two Carrington rotations: CR2152 (solar maximum) and CR2208 (solar minimum). We conduct GSA by computing Sobol' indices that quantify contributions from model parameter uncertainty to the variance of solar wind speed and density at 1 au, both crucial quantities for CME propagation and strength. Sobol' indices also allow us to rank and retain only the most important parameters, which aids in the construction of smaller ensembles for the reduced‐dimension parameter space. We present an efficient procedure for computing the Sobol' indices using polynomial chaos expansion surrogates and space‐filling designs. The PCEs further enable inexpensive forward UQ. Overall, we identify three important model parameters: the multiplicative factor applied to the magnetogram, Poynting flux per magnetic field strength constant used at the inner boundary, and the coefficient of the perpendicular correlation length in the turbulent cascade model in AWSoM. 

Abstract To simulate solar coronal mass ejections (CMEs) and predict their time of arrival and geomagnetic impact, it is important to accurately model the background solar wind conditions in which CMEs propagate. We use the Alfvén Wave Solar atmosphere Model (AWSoM) within the the Space Weather Modeling Framework to simulate solar maximum conditions during two Carrington rotations and produce solar wind background conditions comparable to the observations. We describe the inner boundary conditions for AWSoM using the ADAPT global magnetic maps and validate the simulated results with EUV observations in the low corona and measured plasma parameters at L1 as well as at the position of the Solar Terrestrial Relations Observatory spacecraft. This work complements our prior AWSoM validation study for solar minimum conditions and shows that during periods of higher magnetic activity, AWSoM can reproduce the solar plasma conditions (using properly adjusted photospheric Poynting flux) suitable for providing proper initial conditions for launching CMEs.