Banner Advertisement

Associated Research Papers

SolPhys_Lugaz2009.pdf - Lugaz et al. Solar Terrestrial Simulation in the STEREO Era: The January 24-25, 2007 Eruptions

ApJ_659_788.pdf - Lugaz et al. Numerical Investigation of the Homologous Coronal Mass Ejection Events from Active Region 9236

ApJL_668_L87.pdf – Roussev et al. New Physical Insight on the Changes in Magnetic Topology During Coronal Mass Ejections: Case Studies for the 2002 April 21 and August 24 Events

Cohen_JASTP_2007.pdf – Cohen et al. Validation of a Global 3D Heliospheric Model With Observations for the May 12, 1997

JGR_109_A02107.pdf - Manchester et al. Modeling a space weather event from the Sun to the Earth: CME generation and interplanetary propagation

 

    Research


    Simulations of Specific Events

    In order to get a better understanding of coronal mass ejection (CME) events that occur on the Sun, C2H2 researchers create numerical simulations to model real CMEs observed by satellites. Reproducing observed results through simulations helps explain and constrain the underlying physics, boundary conditions, and processes behind a CME’s creation and evolution. To illustrate how numerical simulations are carried out, we describe a case in which a series of CMEs was modeled by C2H2 researchers.


    X-class flare from AR 9236

    Noe Lugaz et al. ( ApJ 659: 788-800) simulated multiple coronal mass ejection events that occurred in succession on November 24th, 2000 from NOAA active region 9236. Over a period of 3.5 days from November 23 – 27, the Sun experienced 5 X-class flares, 3 M-class flares, and 10 halo CMEs, making it one of the most active periods during the last solar cycle. These events were observed by many instruments including the LASCO C3 coronograph, STEREO, and Wind.

    MODELING OVERVIEW

    For the study, Lugaz et al. chose three CMEs that happened close enough together to interact, but far enough apart in time from other CMEs so that they could be considered an independent system.

    The initial state (see all figures in section below), prior to the CME ejection, was calculated by choosing a solar wind model, reconstructing the coronal magnetic field from MDI magnetogram data, and choosing a method for CME intiation. The simulations were then carried out within the Space Weather Modeling Framework, a model of the space weather environment developed by CSEM.

    The CMEs were then “erupted” at the appropriate times and their velocities, magnetic field strengths, pressures, temperatures, and densities were calculated as they propagated through 3D space. In order to compare with real data from LASCO’s C3 coronagraph, synthetic white light images of the CMEs were created. The propagation direction, velocities, and times of interaction of the observed and modeled data were compared in this way.

    To investigate the CME ejecta at the location of Earth, the model was propagated out to 1AU. The interactions between the three CMEs were observed as they traveled through interplanetary space. Synthetic observations of the model were created to compare with data collected by the satellites Wind and ACE.

    In the model, the characteristics of the CMEs were known everywhere in space, so it was possible to create three-dimensional visualizations of the CME fronts extending into space from the surface of the Sun. This would not be possible with observational data alone.

    Through modeling CMEs and other solar characteristics and comparing with actual data, astronomers can explore the physical parameters of a system, the way a system evolves at all times and in all space instead of just those sampled by a satellite, gain knowledge about the underlying physical processes that create the behaviors we see, and check the accuracy of the methods we use to calculate values from real observations.

    A more detailed description of the actual model applied to these CME events and in depth comparison with real data is included below.

     

    SPECIFIC DESCRIPTION OF THE PAPER:
    Numerical Investigations of the Honologous Coronal Mass Ejection Events from Active Region 9236

    Lugaz et al. chose to simulate three of the CMEs (out of the 10) because they were separated from the other CMEs by enough time to be considered a separate system. The model strove to reproduce the CMEs’ initiation from the solar surface, their 3D propagation and evolution through interplanetary space out to 1AU, and their interaction during transit. In the simulation, the coronal magnetic field was reconstructed from MDI magnetogram data, the steady-state solar wind was based on a varying polytropic index model, and the ejections were initiated using out-of-equilibrium semicylindrical flux ropes with a size smaller than the active region. The simulations were carried out with the Space Weather Modeling Framework, a model of the space weather environment developed by CSEM.

    The modeled CMEs were the first three to be ejected during this period of activity. They had initial velocities larger than 1000 km/s. The three following CMEs erupted over half a day later with slower initial velocities, so it was determined that the second set of CMEs did not have enough time to catch up and interact with the first three. In the real observations, the three modeled CMEs were preceded at the Earth by a single shock wave. This shock wave was not included in this simulation, possibly explaining why the modeled shocks arrived at Earth 10 hours later than the real shocks observed by Wind.

    The activity from November 23-27 led to complex ejecta observed at Earth for 4 days starting on November 26. Two shocks arrived on November 26, one on November 28, and the last on November 29.

    SETTING UP THE ENVIRONMENT

    A solar wind model developed by Roussev et al. (2003) based on magnetogram data and a polytropic index changing with radial distance from the Sun was used to set up the interplanetary environment prior to the CMEs. The solar magnetic field was reconstructed from a 90 harmonic Legendre polynomial expansion based on magnetogram data from the Michelson Doppler Imager (MDI). The initial magnetic field in the corona was obtained by assuming that the magnetic field is potential and by placing the source surface (30Rsun) above the coronal surface (24Rsun). MHD equations are applied to solve for the non-potential part of the coronal magnetic field leading to the dynamic formation of helmet streamers. Free parameters in the solar wind model were chosen to yield the solar wind conditions observed at Earth just prior to the ejections, while maintaining reasonable Alfvenic speeds in the lower corona. Keeping the Alfvenic speeds realistic is very important for simulating realistic CME-shock dynamics.

    The modeled and observed steady-state conditions are shown in the figures above. The average magnitude of the simulated speed, density, and temperature are in good agreement with the observed values, but the time-variation of the model does not follow the observed variation in the solar wind. Density and temperature in low-density periods are overestimated while they are underestimated during high-density periods. The simulated magnetic field is also weaker by a factor of 2. The differences between the simulated and actual values should lead to the simulated shocks being slower than the observed ones (as is true).

    CME INITIATION

    To set up the initiation of the CME, a semicircular flux rope is modeled. An azimuthal current is added to create force-free magnetic field inside the flux rope. This rope is then superimposed on the ambient magnetic field calculated for the solar corona. The flux rope is not in force-free equilibrium with the background field. The mass inside the flux rope is adjusted to match the mass of the CME as observed by LASCO. While this model isn’t sufficient to actually create a CME, it has been shown that out-of-equilibrium flux rope models successfully reproduce CME propagation during the first half hour after ejection.

    The flux ropes are generated at the times of the CME eruptions. The CMEs rapidly decelerate over half an hour and reach speeds that are in good agreement with LASCO images. Because the first ejection removes some mass from the corona, the second and third ejections experience less deceleration.

    COMPARISON BETWEEN THE DATA AND THE MODEL

    White-light images of the three ejections are created and then “observed” by synthetically recreating what LASCO’s C3 coronagraph would see. Qualitatively, the simulation images match the real images. For the first CME, both sets of observations show that it propagates in the northwest and western quadrants and that there is a brightness enhancement in the southern quadrant. In the simulation, the second and third ejecta move more quickly than in real life and begin interacting with the first CME too soon, creating bright areas in the simulated data that do not exist in the real data.

    The front shockwave of the simulated CMEs is tracked in three dimensions and compared with observations. The shockwave positions are in very good agreement with the real data. The speed of the first ejection in the Sun-Earth line is also estimated using the white light images. The speed calculated from these simulated white light images is largely overestimated compared to the actual simulation values because the leading edge of the CME is propagating in the northwest quadrant. In other words, these calculations show that method currently used to calculate speed along the Sun-Earth line may not be accurate if the material is not traveling along the line of sight.

    The CME ejecta was tracked out to the location of the Earth at 1AU and compared with data from Wind and ACE. In the simulation, a forward shock hits the Earth followed by the complex ejecta. Then, an enhanced magnetic field occurs about 6 hours after the passage of the shock. The complex ejecta ends with a fast reverse shock. As mentioned above, the shocks and ejecta arrive 10 hours later than the observed data which can be explained by the fact that a prior shock was left out of the modeling. For comparison purposes, everything is shifted forward by 10 hours.

    The simulation and the observed data differ by a factor of 2 or 3 for many of the characteristics, such as density and temperature. However, the position of the density maxima at the shock front and the temperature variation inside the clouds are reproduced. The reverse shock seen in the simulated data is believed to be an artifact of the CME and solar wind models as none have ever been observed.

    A three dimensional visualization of the shock fronts as they propagate through space is produced in the simulation. The first shock propagates through the undisturbed solar wind, slowing down to about half its initial velocity. The second shock is ejected a few hours later, but travels faster because the solar wind has been rarified. The simulation shows that, essentially, the second shock overtakes the first shock. After the interaction, the forward shock has the speed, direction, compression ratio, and jump in pressure across the shock front of the second shock. The third shock is ejected only 6.5 hours later and propagates into a very disturbed medium. When the third shock reaches the discontinuity at the back of the second shock, the third shock because a fast compression MHD wave.

    None of these shock properties are observable by satellites, so white-light images were constructed for comparison. While STEREO was not in orbit at the time of these events, synthetic images of what it might have observed were created. These synthetic images indicate what STEREO might see in future observations of CME interactions.

    Modeling actual events allows astronomers to explore characteristics that are difficult to observe or cannot be calculated using data from current satellites. Creating synthetic images similar to what satellites may see and then extrapolating values, such as speed and direction, is an important test. In this way, we can compare the extrapolated values from the synthetic images to the actual values in the model and get an idea of how accurate our extrapolations really are. Models can also predict behaviors to look for in future observations.