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 (DA) 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 Alfv\’en 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$\pm$3.8 hours to 5.1$\pm$3.0 hours. 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.

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.

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.

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.

Many current research efforts undertake the solar flare classification task using the Space-weather HMI Active Region Patch (SHARP) parameters as the predictors. The SHARP parameters are scalar quantities based on spatial average or integration of physical quantities derived from the vector magnetic field, which loses information of the two-dimensional spatial distribution of the field and related quantities. In this paper, we construct two new sets of spatial features to expand the feature set used for the flare classification task. The first set uses the idea of topological data analysis to summarize the geometric information of the distributions of various SHARP parameters. The second set utilizes tools coming from spatial statistics to analyze the vertical magnetic field component Br and summarize its spatial variations and clustering patterns. All features are constructed within regions near the polarity inversion lines (PILs) and classification performances using the new features are compared against the SHARP parameters (also along PIL). We found that using the new features can improve the skill of the flare classification model and new features tend to have higher feature importance, especially the spatial statistics features. This potentially suggests that even using a single channel Br, instead of all SHARP parameters, one can still derive strongly predictive features for flare classification.

5 For the first time, we simulate the detailed spectral line emission from a solar active 6 region (AR) with the Alfvén Wave Solar Model (AWSoM). We select an AR appearing 7 near disk center on 2018 July 13 and use an NSO/HMI synoptic magnetogram to specify 8 the magnetic field at the model’s inner boundary. To resolve smaller-scale magnetic 9 features, we apply adaptive mesh refinement to resolve the AR with a horizontal spatial 10 resolution of 0.35 • (4.5 Mm), four times higher than the background corona. We then 11 apply the SPECTRUM code informed with CHIANTI spectral emissivities to calculate 12 spectral lines forming at temperatures ranging from 0.5 to 3 MK. Comparisons are 13 made between the simulated line intensities and those observed by the Hinode/EIS 14 instrument where we find close agreement (about 20% relative error for both loop top 15 and footpoints at a temperature of about 1.5 MK) across a wide range of loop sizes and 16 temperatures. We also simulate and compare Doppler velocities and find that simulated 17 flow patterns are of comparable magnitude to what is observed. Our results demonstrate 18 the broad applicability of the low-frequency Alfvén wave balanced turbulence theory 19 for explaining the heating of coronal loops. 20

We present a data-driven and self-consistent SEP model, SOFIE, to simulate the acceleration and transport processes of energetic particles using the Space Weather Modeling Framework (SWMF). In this model, the background solar wind plasma in the solar corona and interplanetary space are modeled by the Alfven Wave Solar-atmosphere Model(-Realtime) (AWSoM(-R)) driven by the near-real-time hourly updated GONG (bihourly ADAPT-GONG) magnetograms. In the background solar wind, the CMEs are launched employing the Eruptive Event Generator using Gibson-Low configuration (EEGGL), by inserting a flux rope estimated from the free magnetic energy in the active region. The acceleration and transport processes are then modeled self-consistently by the multiple magnetic field line tracker (M-FLAMPA) and the Adaptive Mesh Particle Simulator (AMPS). We will demonstrate the capability of SOFIE to demystify the acceleration processes by the CME-driven shock in the low corona and the modulation of energetic particles by the solar wind structures. Besides, using selected historical SEP events, e.g. 2013 Apr 11 event, we will illustrate the progresses toward a faster-than-real-time prediction of SEPs.