A document by Qusai Al Shidi. Click on the document to view its contents.

In this study, we examine the accuracy of global geospace simulations by analyzing the relationship between the solar wind and its propagation parameters and the errors in auroral electrojet index AU and AL, ring current index SYM-H and the cross-polar cap potential (CPCP) in simulations. We show that generally the error distributions are wider for higher level of solar wind driving. Our results also show that the observing solar wind monitor distance from the Sun-Earth line and the phase front normal angle produce only minor effects on the error distributions, however, for oblique angles (<0.4) of the phase front normal there are noticable effects on the error distributions. Furthermore, we show that the results hold true also when using two magnetometer station recordings, one at subauroral and the other at auroral latitudes, which speak to the similarity of the error sources in local and global activity measures. These results are important elements in assessing the accuracy of the timing and magnitude of space weather events recorded on ground.

In this paper, we demonstrate the applicability of the data-driven and self-consistent solar energetic particle model, Solar-wind with FIeld-lines and Energetic-particles (SOFIE), to simulate acceleration and transport processes of solar energetic particles. SOFIE model is built upon the Space Weather Modeling Framework (SWMF) developed at the University of Michigan. In SOFIE, the background solar wind plasma in the solar corona and interplanetary space is calculated by the Aflv\’en Wave Solar-atmosphere Model(-Realtime) (AWSoM-R) driven by the near-real-time hourly updated Global Oscillation Network Group (GONG) solar magnetograms. In the background solar wind, coronal mass ejections (CMEs) are launched by placing an imbalanced magnetic flux rope on top of the parent active region, using the Eruptive Event Generator using Gibson-Low model (EEGGL). The acceleration and transport processes are modeled by the Multiple-Field-Line Advection Model for Particle Acceleration (M-FLAMPA). In this work, nine solar energetic particle events (Solar Heliospheric and INterplanetary Environment (SHINE) challenge/campaign events) are modeled. The three modules in SOFIE are validated and evaluated by comparing with observations, including the steady-state background solar wind properties, the white-light image of the CME, and the flux of solar energetic protons, at energies of $\ge$ 10 MeV.

We present new analysis methods of 3D MHD output data from the Space Weather Modeling Framework during a simulated storm event. Earth’s magnetosphere is identified in the simulation domain and divided based on magnetic topology and the bounding magnetopause definition. Volume energy contents and surface energy fluxes are analyzed for each subregion to track the energy transport in the system as the driving solar wind conditions change. Two energy pathways are revealed, one external and one internal. The external pathway between the magnetosheath and magnetosphere has magnetic energy flux entering the lobes and escaping through the closed field region and is consistent with previous work and theory. The internal pathway, which has never been studied in this manner, reveals magnetically dominated energy recirculating between open and closed field lines. The energy enters the lobes across the dayside magnetospheric cusps and escapes the lobes through the nightside plasmasheet boundary layer. This internal circulation directly controls the energy content in the lobes and the partitioning of the total energy between lobes and closed field line regions. Qualitative analysis of four-field junction neighborhoods indicate the internal circulation pathway is controlled via the reconnection X-line(s), and by extension, the IMF orientation. These results allow us to make clear and quantifiable arguments about the energy dynamics of Earth’s magnetosphere, and the role of the lobes as an expandable reservoir that cannot retain energy for long periods of time but can grow and shrink in energy content due to mismatch between incoming and outgoing energy flux.

J. Schmidt and Cairns (2019) have recently claimed that they can predict Coronal Mass Ejection (CME) arrival times with an accuracy of 0.9+-1.9 hours for four separate events. They also stated that the accuracy gets better with increased grid resolution. Here, we show that combining their results with the Richardson extrapolation (Richardson and Gaunt, 1927), which is a standard technique in computational fluid dynamics, could predict the CME arrival time with 0.2+-0.26 hours accuracy. The CME arrival time errors of this model would lie in a 95% confidence interval [-0.21,0.61] h. We also show that the probability of getting these accurate arrival time predictions with a model with a standard deviation exceeding 2 hours is less than 0.1%, indicating that these results cannot be due to random chance. This unprecedented accuracy is about 20 times better than the current state-of-the-art prediction of CME arrival times with an average error of about +-10 hours. Based on our analysis there are only two possibilities: the results shown by J. Schmidt and Cairns (2019) were not obtained from reproducible numerical simulations, or their method combined by the Richardson extrapolation is in fact providing CME arrival times with half an hour accuracy. We believe that this latter interpretation is very unlikely to hold true. We also discuss how the peer-review process apparently failed to even question the validity of the results presented by Schmidt and Cairns (2019).

A document by Zhenguang Huang. Click on the document to view its contents.

J. Schmidt and Cairns (2019) have recently shown that they can predict Coronal Mass Ejection (CME) arrival times with an accuracy of 0.9+-1.9 hours for four separate events. They also showed that the accuracy gets better with increased grid resolution. Here, we further improve these results by using the Richardson extrapolation (Richardson and Gaunt, 1927), which is a standard technique in computational fluid dynamics, and predict the CME arrival time with 0.2+-0.26 hours accuracy. The CME arrival time errors of the new model lie in a 95% confidence interval [-0.21,0.61] h. We also show that the probability of getting these accurate arrival time predictions with a model with a standard deviation exceeding 2 hours is less than 0.1%, indicating that the excellent results cannot be due to random chance, and the Richardson extrapolation has indeed improved the original model by J. Schmidt and Cairns (2019). This unprecedented accuracy is about 40 times better than the current state-of-the-art prediction of CME arrival times with an average error of about +-10 hours. The new model uses information available within a few hours after the CME eruption and it can run much faster than real-time on a couple of CPU cores. Based on the result, we recommend the new model to be transitioned to operations as soon as possible to better protect our space-born and ground-based assets from the harmful effects of space weather.

Modeling the impact of space weather events such as coronal mass ejections (CMEs) is crucial to protecting critical infrastructure. The Space Weather Modeling Framework (SWMF) 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 (DA). 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 (PCE) 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.

To simulate solar Coronal Mass Ejections (CMEs), 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 (SWMF) 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 STEREO spacecraft. This work complements our prior AWSoM validation study for solar minimum conditions Sachdeva et al. (2019), 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.

The magnetohydrodynamics with embedded particle-in-cell (MHD-EPIC) model has been successfully applied to global magnetospheric simulations in recent years. However, the PIC region was restricted to be one or more static boxes, which is not always sufficient to cover the whole physical structure of interest efficiently. The FLexible Exascale Kinetic Simulator (FLEKS), which is a new PIC code and allows a dynamic PIC region of any shape, is designed to break this restriction. FLEKS is usually used as the PIC component of the MHD with adaptively embedded particle-in-cell (MHD-AEPIC) model. FLEKS supports dynamically activating or deactivating cells to fit the regions of interest during a simulation. An adaptive time-stepping scheme is also introduced to improve the accuracy and efficiency of a long simulation. The particle number per cell may increase or decrease significantly and lead to load imbalance and large statistical noise in the cells with fewer particles. A particle splitting scheme and a particle merging algorithm are designed to limit the change of the particle number and hence improve the accuracy of the simulation as well as load balancing. Both particle splitting and particle merging conserve the total mass, momentum, and energy. FLEKS also contains a test-particle module to enable tracking particle trajectories due to the time-dependent electromagnetic field that is obtained from a global simulation.

In this work, we develop gradient boosting machines (GBMs) for forecasting the SYM-H index multiple hours ahead using different combinations of solar wind and interplanetary magnetic field (IMF) parameters, derived parameters, and past SYM-H values. Using Shapley Additive Explanation (SHAP) values to quantify the contributions from each input to predictions of the SYM-H index from GBMs, we show that our predictions are consistent with physical understanding while also providing insight into the complex relationship between the solar wind and Earth’s ring current. We also perform a direct comparison between GBMs and neural networks presented in prior publications for forecasting the SYM-H index by training, validating, and testing them on the same data. We find that the GBMs have a comparable root mean squared error as the best published black-box neural network schemes and GBMs have better Heidke Skill Scores at predicting strong storms.

We use the Space Weather Modeling Framework (SWMF) 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 hours starting from 6 hours prior to the start of the Dst depression. The simulation output of ground magnetic variations 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.