Modeling AWSoM CMEs with EEGGL: A New Approach for Space Weather Forecasting

Abstract

The major source of destructive space weather is coronal mass ejections (CMEs). However, our understanding of CMEs and their propagation in the heliosphere is limited by the insufficient observations. Therefore, the development of first-principals numerical models plays a vital role in both theoretical investigation and providing space weather forecasts. Here, we present results of the simulation of CME propagation from the Sun to 1AU by combining the analytical Gibson & Low (GL) flux rope model with the state-of-art solar wind model AWSoM. We also provide an approach for transferring this research model to a space weather forecasting tool by demonstrating how the free parameters of the GL flux rope can be prescribed based on remote observations via the new Eruptive Event Generator by Gibson-Low (EEGGL) toolkit. This capability allows us to predict the long-term evolution of the CME in interplanetary space. We perform proof-of-concept case studies to show the capability of the model to capture physical processes that determine CME evolution while also reproducing many observed features both in the corona and at 1 AU. We discuss the potential and limitations of this model as a future space weather forecasting tool.

Full text

Modeling!AWSoM!CMEs!with!EEGGL:
!A!New!Approach!for!Space!Weather!
Forecasting
Meng Jin
1,2
, W. B. Manchester
3
, B. van der Holst
3
, I. Sokolov
3
, G. Toth
3
, A. Vourlidas
4
,
C. de Koning
5
, T. I. Gombosi
3
, R. E. Mullinix
6
, A. Taktakishvili
6
, A. Chulaki
6
1
UCAR/NASA Jack Eddy Postdoc Fellow
2
Lockheed Martin Solar and Astrophysics Lab, Palo Alto, CA
3
CLaSP, University of Michigan, Ann Arbor, MI
4
APL, Johns Hopkins University, Laurel, MD
5
CIRES, University of Colorado, Boulder, CO
6
CCMC, Goddard Space Flight Center, Greenbelt MD
AGU Fall Meeting 2015

CME Forecasting Models
•
Coronal mass ejections (CMEs) are a major source of potentially destructive
space weather conditions (geomagnetic storm, SEPs). Due to the limited
observations, models play a vital role both for scientific understanding and for
forecasting.
•
Empirical Forecasting Models: using near-Sun CME observations (e.g.,
Gopalswamy et al. 2001), using data-mining techniques (Riley et al. 2015)
•
Kinematic Models: 3-D Hakamada-Akasofu-Fry version 2 (HAFv.2) model
(Hakamada & Akasofu 1982; Fry et al. 2001); Cone model (Zhao et al., 2002;
Hayashi et al., 2006).
•
Kinematic + MHD Heliosphere Models: ENLIL heliosphere model with the CME
cone model (Xie et al 2004; Odstrcil et al. 2005); 3D MHD model by Han et al.
(1988) with the HAFv.2 model (Wu et al., 2007). The average error in the CAT (CME
Analysis Tool)-Wang-Sheeley-Arge (WSA)-ENLIL operational model is 7.5 hours
(Pizzo et al. 2011; Millward et al. 2013).
•
MHD Corona Models + Magnetically Driven Eruptions: Provide magnetic field
information at 1 AU (e.g., Usmanov & Dryer 1995, Wu et al. 1999, Feng et al. 2010,
Groth et al. 2000, Manchester et al. 2004, Lugaz et al. 2007, Toth et al. 2007, Cohen
et al. 2008, Shen et al. 2011, Zhou et al. 2012, Lionello et al. 2013)

A New Approach of Coupling AWSoM MHD with EEGGL
Input
Magnetogram
AWSoM
Observed
CME Speed
Steady-State
Solar Wind
AWSoM CME
EEGGL
•
The analytical flux rope parameters are derived from the observations.
•
Capability to trace the erupting magnetic field from the Sun to 1AU.
•
Extensive comparison with the CME observations near the Sun and in the
heliosphere.
AWSoM: Alfven-wave Solar Model
EEGGL: Eruptive Event Generator using Gibson-Low configuration

References: van der Holst et al. 2010, Manchester et al.
2012, Jin et al. 2012, Sokolov et al. 2013, Oran et al.
2013, Jin et al. 2013, van der Holst et al. 2014
•
Data-driven inner boundary
condition by synoptic
magnetograms.
•
Coronal heating and solar wind
accelerating by Alfven waves.
•
Physically consistent
treatment of wave reflection,
dissipation, and heat
partitioning between the
electrons and protons.
•
Model starts from upper
chromosphere including heat
conduction (both collisional
and collisionless) and radiative
cooling.
•
Adaptive mesh refinement
(AMR) to resolve structures
(e.g., current sheets, shocks).

The comparison
between observations
and synthesized EUV
images of the steady
state solar wind model
Top panels:
Observational images
from SDO AIA 211,
STEREO A EUVI 171, and
STEREO B EUVI 195. The
observation time is 2011
March 7 ∼20:00 UT.
Middle panels:
Synthesized EUV images
of the model.
Bottom panels:
Quantitative comparison
between the model and
observation for different
structures of the Sun.

Gibson-Low Flux Rope
•
Analytical profiles of the GL flux rope are
obtained by finding a solution to the
magnetohydrostatic equation and the
solenoidal condition (Gibson & Low
1998) through mathematical stretching
transformation.
•
The transformed flux rope appears as a
tear-drop shape of twisted magnetic flux.
•
Lorentz forces are introduced, which
leads to a density-depleted cavity in the
upper portion and a dense core at the
lower portion of the flux rope (3-part CME
density structure).
The GL Flux Rope is determined by 4 parameters:
•
a (Fixed): determines the shape of the flux rope
•
r
1
(Fixed): determines the initial position of the flux rope before it is stretched
•
r
0
: determines the size of the flux rope
•
a
1
: determines the magnetic strength of the flux rope

With different GL radius/
strength parameters, a
linear relationship is
found between the flux
rope poloidal flux and the
CME speed near the Sun.
With the same flux
rope parameters, the
CME speed is
inversely related to
the average Br
around the PIL of the
active region.

An Example of EEGGL Setting
Observations
CME Speed
PIL Location
PIL Length
Weighted
Centers
Field
Strength
near PIL
GL Location
GL
Orientation
GL Size
GL Field
Strength
AWSoM
AR 11164
Weighted Centers
Polarity Inversion Line (PIL)
GL Flux Rope Location

•
Red: Flux rope field lines;
•
White: Large-scale helmet streamers;
•
Green: Field lines from surrounding active regions and open field lines.
2011 February 15 CME

Plasma Beta
•
Plasma beta at 2.5 Rs
•
The eruption changes the
current sheet location
therefore the large-scale
magnetic configuration
significantly.
Plasma Density
•
Plasma density at 42 Mm
•
Waves reflection from the
north and south polar
coronal hole boundary.
Dopplergram
•
Radial velocity at 42 Mm
•
Downward flow due to the
expansion of the CME.
•
Observed by Hinode/EIS
(Harra et al. 2011)

EUV Waves (2011 March 7 CME)
Model
Both the simulation and observation images are produced by tri-ratio running
difference method. The tricolor channels are AIA 211 (red), AIA 193 (green), and
AIA 171 (blue). The ratio in each channel is identically scaled to 0.8-1.2 for both
observation and simulation.
Observation

White-Light Images (2011 March 7 Event)
LASCO C2
STA COR1
STB COR1

White-Light Images (2011 March 7 Event)
LASCO C3
STA COR2
STB COR2

CME Evolution in the Heliosphere
(2011 March 7 Event)
Proton Temperature
Proton Density
STA
STA

1 AU Comparison (2011 March 7)
•
The second velocity and density peaks after the shock is due to the
numerical reconnection.
•
The lower compression ratio in the model is caused by the elevated proton
temperature in the CIR region.
Ux
Uy
Uz
Bx
By
Bz
Velocity
Density
Tp
B

2012 July 12 CME Event
In-situ Comparison
IMF Bz Evolution
10 nT
-10 nT
Velocity
Density
Temperature
Bz

Remarks
•
We present this newly developed tool for simulating CMEs from the Sun to the
Earth. With increasing number of events studied, it will be possible to obtain
better empirical relations to constrain the EEGGL model. We will also utilize
vector magnetogram to specify the helicity of the GL flux rope.
•
The computation expense using current AWSoM model is high (~40,000 CPU
hours on Pleiades). The new model AWSoM-R (Threaded Field Line Model;
Sokolov et al. 2015) may run 10~100 times faster so that real-time simulation
will be possible at CCMC.
•
The results will also improve our understanding about the erupting CME
structure and its evolution in the heliosphere therefore provide valuable
information for improving the current flux rope models used in the numerical
simulations.
•
There are issues with developing a forecasting model: We do not have full
coverage of magnetic field observation on the Sun; We do not have direct
observation of the erupting CME magnetic structures. How these “missing
data” will influence our modeling capability needs to be understand in the
future.