# Why are ELEvoHI CME arrival predictions different if based on STEREO-A or
STEREO-B heliospheric imager observations?

###### Abstract

Accurate forecasting of the arrival time and arrival speed of coronal mass
ejections (CMEs) is a unsolved problem in space weather research. In this
study, a comparison of the predicted arrival times and speeds for each CME
based, independently, on the inputs from the two STEREO vantage points is
carried out. We perform hindcasts using ELlipse Evolution model based on
Heliospheric Imager observations (ELEvoHI) ensemble modelling. An estimate of
the ambient solar wind conditions is obtained by the Wang-Sheeley-
Arge/Heliospheric Upwind eXtrapolation (WSA/HUX) model combination that serves
as input to ELEvoHI. We carefully select 12 CMEs between February 2010 and
July 2012 that show clear signatures in both STEREO-A and STEREO-B HI time-
elongation maps, that propagate close to the ecliptic plane, and that have
corresponding in situ signatures at Earth. We find a mean arrival time
difference of 6.5 hrs between predictions from the two different viewpoints,
which can reach up to 9.5 hrs for individual CMEs, while the mean arrival
speed difference is 63 km s-1. An ambient solar wind with a large speed
variance leads to larger differences in the STEREO-A and STEREO-B CME arrival
time predictions ($cc~{}=~{}0.92$). Additionally, we compare the predicted
arrivals, from both spacecraft, to the actual in situ arrivals at Earth and
find a mean absolute error of 7.5 $\pm$ 9.5 hrs for the arrival time and 87
$\pm$ 111 km s-1 for the arrival speed. There is no tendency for one
spacecraft to provide more accurate arrival predictions than the other.

Space Weather

Space Research Institute, Austrian Academy of Sciences, Schmiedlstraße 6, 8042
Graz, Austria University of Graz, Institute of Physics, Universitätsplatz 5,
8010 Graz, Austria Institute of Geodesy, Graz University of Technology,
Steyrergasse 30, 8010 Graz, Austria Conrad Observatory, Zentralanstalt für
Meteorologie und Geodynamik, Vienna, Austria RAL Space, Rutherford Appleton
Laboratory, Harwell Campus, Didcot OX11 0QX, UK Department of Meteorology,
University of Reading, Reading, UK

Jürgen<EMAIL_ADDRESS>
A comparison of CME arrival time and speed predictions from two vantage points
was carried out using ELEvoHI

A highly structured ambient solar wind flow leads to larger arrival time
differences between STA and STB predictions

The assumption of a rigid CME front in ELEvoHI and other HI-based methods is
most probably too simplistic

##  1 Introduction

Understanding the dynamics of coronal mass ejections (CMEs) in the heliosphere
is a key aspect of space weather research. CMEs are huge clouds of energetic
and magnetized plasma (Hundhausen ., 1994) erupting from the solar corona that
may reach speeds of up to 3000 km s-1. When they hit Earth, CMEs can produce
strong geomagnetic storms (Gosling ., 1990; Srivastava Venkatakrishnan, 2004;
Richardson Cane, 2012; Kilpua ., 2012) causing communication and navigation
system problems, damaging satellites and can even cause power outages (Cannon,
2013). The need for accurate predictions of CMEs, both CME arrival time and
speed, is becoming increasingly important (Owens ., 2020), because humankind,
more than ever, depends on advanced technology.

Shortly after their eruption, CMEs can be observed in coronagraph images. Two
of the few space-borne coronagraphs in operation are the Large Angle and
Spectrometric Coronagraph (LASCO) C2 and C3 on-board the Solar and
Heliospheric Observatory (SoHO; Brueckner ., 1995). SoHO is situated in a
Lissajous orbit around Lagrange point 1 (L1), about 1.5 million km upstream of
Earth in the Sun-Earth line.

The launch of the Solar Terrestrial Relations Observatory (STEREO; Kaiser .,
2008) twin-spacecraft mission in 2006 provided an unprecedented opportunity to
observe CMEs from off the Sun-Earth line. The two spacecraft orbit the Sun
slightly closer (STEREO Ahead; STA) and slightly further (STEREO Behind; STB)
than Earth, leading to a separation of each spacecraft by about 22° per year
from Earth in opposite directions. Both spacecraft are equipped with the In-
situ Measurements of Particles and CME Transients (IMPACT; Luhmann ., 2008)
instrument package to measure solar wind speed, density and magnetic field and
additionally host a suite of imagers, such as the COR1 and COR2 (Howard .,
2008) coronagraphs and the heliospheric imagers, HI1 and HI2 (Eyles ., 2009).
The wide-angle HI cameras provide observations of the heliosphere that allow
us to track a CME from close to the Sun out to the orbit of Earth,
particularly in the ecliptic plane.

CMEs are optically thin structures that expand rapidly, and decreasing density
lowers the line-of-sight integrated intensity in white-light data. As a
consequence, the tracking of CME fronts and the interpretation of HI image
data is difficult. Furthermore, the plane-of-sky assumption is not valid, and
we must assume a certain longitudinal extent of the CME frontal shape.

CMEs may be influenced by different phenomena in the heliosphere, e.g.
magnetic forces close to the Sun, high-speed solar wind streams, or by other
CMEs (Lugaz ., 2012; Kay Opher, 2015; Möstl ., 2015). The ambient solar wind
can also affect the kinematic and morphological characteristics of CMEs (e.g.
Gosling ., 1990; Gopalswamy ., 2000; Manoharan ., 2004). A CME originating at
a speed much faster than the ambient solar wind speed is likely to experience
deceleration while slow CMEs may accelerate during their propagation
(Richardson Cane, 2010; Manoharan Mujiber Rahman, 2011). Hence, not only the
propagation direction but also the kinematics and shape of CMEs can be altered
(e.g. Savani ., 2010; Zuccarello ., 2012; YD. Liu ., 2014; Rollett ., 2014;
Ruffenach ., 2015; Kay Nieves-Chinchilla, 2020). By tracking CMEs far out in
the heliosphere, we get an understanding of their interaction with the ambient
solar wind and co-rotating interaction regions.

Over the last decades, a vast number of CME prediction models have been
developed. They include empirical models, e.g. Effective Acceleration Model
(EAM; Paouris Mavromichalaki, 2017), which use relationships between
observable parameters and the transit time. There are also drag-based models,
(e.g. DBM; Vršnak . 2013, DBEM; Dumbović . 2018, ANTEATR; Kay . 2020), that
make use of physics-based equations and account for drag between the ambient
solar wind and the CME. Other models make use of HI images, which require
techniques to convert the measured elongation into radial distance. For
example, the fixed phi fitting (FPF; Sheeley ., 1999; Rouillard ., 2008)
technique considers a CME as a single point, propagating at a constant speed,
and provides an estimate of the constant direction of the CME propagation
relative to the observer from the apparent acceleration within a sequence of
HI images. The harmonic mean fitting (HMF; Lugaz, 2010; Möstl ., 2011) method
is similar except that it describes a CME as a circle that remains attached to
the Sun-center. The self-similar-expansion fitting (SSEF; Lugaz ., 2010;
Davies ., 2012; Möstl Davies, 2013) technique describes a CME as a circle
having an increasing radius as it propagates away from the Sun in such a way
that it maintains a constant angular width. FPF and HMF are extremes of the
SSEF technique with a half width of 0° and 90°, respectively. More
sophisticated models combine both the drag-based approach and HI observations
(e.g. DBM fitting; Žic . 2015, Ellipse Evolution model based on HI
observations, ELEvoHI; Rollett . 2016; Amerstorfer . 2018). Finally, numerical
models, which are computational heavy, solve magnetohydrodynamic (MHD)
equations (e.g., ENLIL; Odstrcil . 2004, EUHFORIA; Pomoell Poedts 2018)
simulating the ambient solar wind in the full heliosphere based on synoptic
photospheric magnetic-field maps. CMEs are then injected into these models to
provide predictions regarding the arrival time and arrival speed at different
locations in the heliosphere.

As noted above, ELEvoHI aims to predict the arrival time and arrival speed of
CMEs. The model assumes an elliptical shape for the CME front and incorporates
the drag exerted by the ambient solar wind. Also, different sources of ambient
solar wind speed (e.g. provided by numerical models) can serve as input to
ELEvoHI (Amerstorfer ., 2021). In its latest version, the model can be used
with STEREO-A HI beacon mode data to provide near real-time CME arrival
predictions.

This study assesses ELEvoHI to evaluate arrival time and speed predictions of
past CMEs using STEREO HI science-quality data. We perform ELEvoHI ensemble
predictions for 12 CMEs, where each CME is modeled using input data from STA
and STB, separately. In an idealized case, in which a CME with an elliptical
front propagates in an ambient solar wind that is constant in space and time,
one would expect to get similar results for the arrival time and arrival speed
from the two different vantage points. Instead of inferring the propagation
directions of the events under study from HI images (e.g. FPF, SSEF), as was
done by Amerstorfer . (2018), we make use of coronagraph images and perform
Graduated Cylindrical Shell (GCS; AFR. Thernisien ., 2006; A. Thernisien .,
2009) reconstruction for each CME based on multi-vantage point coronagraph
data. Additionally, we apply a combination of the Wang-Sheeley-Arge (Arge .,
2003) and the Heliospheric Upwind eXtrapolation (Riley Lionello, 2011; Owens
Riley, 2017) model (WSA/HUX model combination; Reiss ., 2019, 2020) to get an
estimate of the ambient solar wind conditions in the heliosphere through which
the CME propagates. With the additional information about the propagation
direction of the CME and the modeled ambient solar wind, ELEvoHI is more
likely to give better arrival time and arrival speed predictions.

In Section 2, we describe our data selection process, including the data
products, and list all of the studied CMEs. Section 3 deals with the ELEvoHI
setup and how the input data required by the model is obtained. In Section 4,
we present our results and give reasons for the difference in the model
predictions based on STA and STB input data. The discussion and further
implementations of the model are included in Section 5.

##  2 Data Preparation

Table 1: List of selected CMEs. ID and Date correspond to the unique
identifier and the time of the first appearance of the CME in HI1 imagery,
from the HELCATS catalog, for STA and STB spacecraft. ICMECAT ID is the
identifier of the interplanetary coronal mass ejection (ICME) from an updated
version of the HELCATS ICMECAT (Möstl ., 2017), ICME date is the start time of
the detected ICME and $v_{\mathrm{ICME}}$ is the measured in situ arrival
speed obtained from the HELCATS ICMECAT.

Nr. | ID STA | Date STA | ID STB | Date STB | ICMECAT ID | ICME date | $v_{\mathrm{ICME}}$
---|---|---|---|---|---|---|---
|  |  |  |  |  |  |  [km s-1]
1 |  HCME_A__ 20100203_01 |  2010-02-03 14:49 |  HCME_B__ 20100203_01 |  2010-02-03 20:49 |  ICME_Wind_ NASA_20100207_01 |  2010-02-07 18:04b) |  406$\pm$2
2 |  HCME_A__ 20100319_01 |  2010-03-19 22:09 |  HCME_B__ 20100319_01 |  2010-03-19 20:09 |  ICME_Wind_ MOESTL_20100323_01 |  2010-03-23 22:29c) |  292$\pm$12
3 |  HCME_A__ 20100403_01 |  2010-04-03 12:09 |  HCME_B__ 20100403_01 |  2010-04-03 12:09 |  ICME_Wind_ NASA_20100405_01 |  2010-04-05 07:55a) |  734$\pm$18
4 |  HCME_A__ 20100408_01 |  2010-04-08 06:49 |  HCME_B__ 20100408_01 |  2010-04-08 07:29 |  ICME_Wind_ NASA_20100411_01 |  2010-04-11 12:20a) |  432$\pm$17
5 |  HCME_A__ 20100523_01 |  2010-05-23 22:09 |  HCME_B__ 20100524_01 |  2010-05-24 00:09 |  ICME_Wind_ NASA_20100528_01 |  2010-05-28 01:52a) |  370$\pm$10
6 |  HCME_A__ 20101026_01 |  2010-10-26 15:29 |  HCME_B__ 20101026_01 |  2010-10-26 16:10 |  ICME_Wind_ MOESTL_20101030_01 |  2010-10-30 09:15b) |  380$\pm$9
7 |  HCME_A__ 20110130_01 |  2011-01-30 20:09 |  HCME_B__ 20110130_01 |  2011-01-30 18:49 |  ICME_Wind_ MOESTL_20110204_01 |  2011-02-04 01:50a) |  375$\pm$9
8 |  HCME_A__ 20110214_02 |  2011-02-14 22:49 |  HCME_B__ 20110214_02 |  2011-02-14 22:09 |  ICME_Wind_ MOESTL_20110218_01 |  2011-02-18 00:48a) |  493$\pm$25
9 |  HCME_A__ 20110906_02 |  2011-09-06 23:29 |  HCME_B__ 20110907_01 |  2011-09-07 03:29 |  ICME_Wind_ MOESTL_20110909_01 |  2011-09-09 11:46a) |  417$\pm$20
10 |  HCME_A__ 20120123_01 |  2012-01-23 04:49 |  HCME_B__ 20120123_01 |  2012-01-23 05:29 |  ICME_Wind_ MOESTL_20120124_01 |  2012-01-24 14:36a) |  613$\pm$36
11 |  HCME_A__ 20120614_01 |  2012-06-14 16:09 |  HCME_B__ 20120614_01 |  2012-06-14 16:09 |  ICME_Wind_ MOESTL_20120616_01 |  2012-06-16 19:34a) |  489$\pm$29
12 |  HCME_A__ 20120712_02 |  2012-07-12 18:49 |  HCME_B__ 20120712_01 |  2012-07-12 18:09 |  ICME_Wind_ MOESTL_20120714_01 |  2012-07-14 17:38a) |  615$\pm$37

  * a)

shock arrival time

  * b)

time of density enhancement

  * c)

time of the magnetic flux rope

We select a period between February 2010 and July 2012 during which the STEREO
spacecraft had a separation angle from Earth of about 65° to 120°
respectively, from which we study 12 CMEs. The HELCATS HICAT CME catalog lists
about 700 entries over this time range (Harrison ., 2018). However, our list
is constrained to 12 events, since the CMEs have to:

  * 1)

be observed by HI on both STA and STB spacecraft (as listed in the HIJoinCAT;
Barnes2020)

  * 2)

propagate close to the ecliptic plane,

  * 3)

have a corresponding in situ signature at Earth,

  * 4)

be able to be tracked unambiguously in time-elongation maps.

Table 1 contains the list of selected CMEs with their unique identifier and
the time of their first observation in HI1 images (according to the HELCATS
catalog Version 6). The interplanetary CME (ICME) times and speeds are taken
from version 2.0 of the HELCATS ICMECAT catalog (Möstl ., 2020, see also the
links in the data section). The ICMECAT assimilates ICME catalogs from
different spacecraft into one consistent list, and was first published in
Möstl . (2017). The ICME date as observed by the Wind spacecraft is defined by
the shock arrival time, or, if no shock is present, the start of a density
enhancement in front of the magnetic flux rope (MFR). If neither is observed,
the ICME start time is taken as the start time of the MFR. The corresponding
ICME speed is the mean proton bulk speed of either the sheath region, the
density enhancement ahead of the MFR, or the speed of the MFR itself. The
spread in the speed over the given interval for each event is indicated in
Table 1 by a standard deviation. For Table 1, some times in the ICMECAT were
originally taken from the Wind ICME catalog (Nieves-Chinchilla ., 2018), while
other events that were not present in the Wind catalog were added by Möstl .
(2020) to the HELCATS ICMECAT.

To run ELEvoHI, we make use of several data products. Most important are
images from HI onboard STEREO. The HI instrument on each STEREO spacecraft
consists of two white-light wide-angle imagers, HI1 and HI2. HI1 has a field-
of-view (FOV) extending from 4° – 24° elongation (angle from Sun center) in
the ecliptic and HI2 has an angular FOV extending from 18.8° – 88.8°
elongation in the ecliptic. The nominal cadence of the HI1 and HI2 science
data is 40 minutes and 120 minutes, respectively. The science image bin size
is 70 arc sec for HI1 and 4 arc min for HI2. For the additional input
parameters to ELEvoHI, we developed the Ecliptic cut Angles from GCS for
ELEvoHI tool (EAGEL, see Section 3.1). EAGEL ideally uses coronagraph images
from STEREO COR1/COR2 and from LASCO C2/C3 onboard SoHO, but images from at
least two different viewpoints are required. The FOV of COR1 ranges from 1.4 –
4 R⊙ and COR2, from 2 – 15 R⊙, while C2 has a FOV of 1.5 – 6 R⊙ and C3, 3.7 –
30 R⊙ (all quoted in the plane-of-sky). The cadence of the coronagraph science
images is about 15 minutes.

##  3 Methods

###  3.1 EAGEL (Ecliptic cut Angles from GCS for ELEvoHI)

Figure 1: GCS reconstruction (left) and ecliptic cut of the wireframe (right)
for event $\\#$5\. a) Top row from left to right: STB/COR2, LASCO/C2,
STA/COR2. Bottom row: same as top row but with the GCS wire frame overlaid. b)
Ecliptic cut (black) of the GCS wire frame. Red and green lines show the
boundaries selected by either EAGEL or the user. The yellow line defines the
ecliptic propagation direction with respect to Earth, $\phi_{Earth}$, of the
CME. The half width, $\lambda$, is the angle between one boundary and
$\phi_{Earth}$. The blue arrow indicates the direction to Earth.

In this section, we present a newly developed Interactive Data Language
(IDLTM) tool called EAGEL (Ecliptic cut Angles from GCS for ELEvoHI). EAGEL
allows the user to determine the propagation direction, $\phi$, and the half
width, $\lambda$, within the ecliptic plane, based on GCS reconstruction of a
CME. To perform GCS reconstruction, coronagraph images from at least two
vantage points (STEREO and/or LASCO) are required. EAGEL provides the routines
to download the required coronagraph images, combines all the functions to
perform GCS reconstruction, and produces a cut in the ecliptic plane. Standard
pre-processing of the images is implemented in EAGEL to make the CME features
clearly visible to the user, who can decide between using background-
subtracted, running-difference, and base-difference images. The user can then
perform GCS reconstruction using the IDL SolarSoft procedure rtsccguicloud.
The top row of Figure 1a shows the coronagraph images (from left to right:
STB/COR2, LASCO/C2, STA/COR2) for event $\\#$5\. The bottom row additionally
shows the GCS wire frame (green mesh). In its current version, ELEvoHI is a 2D
prediction model giving results only in the ecliptic plane. Therefore, EAGEL
calculates the ecliptic part of the GCS wire frame and selects the boundaries
of the ecliptic cut (see red and green line in Figure 1b). The boundaries are
defined to be the outermost points of each side of the ecliptic cut with
respect to the apex direction from GCS reconstruction. This gives $\lambda$
and $\phi$, where the latter is defined to be exactly in between the two
boundaries. A plot is shown to the user (Figure 1b) and, if needed, the
boundaries can be changed manually. Once the user approves the selection,
$\lambda$ and $\phi$ relative to Earth and to the two STEREO spacecraft are
stored and can be used by ELEvoHI.

In Table 2, we list the time (Date) of the STEREO coronagraph images used to
get $\lambda$ and $\phi$ for each event. EAGEL then selects the SoHO
coronagraph images closest in time to the quoted date. Each CME is fitted once
based on the three different viewpoints (STA, STB, LASCO). However, for event
$\\#$1 no LASCO data is available, so GCS reconstruction is based on STEREO
images only. For all the events, the times of the images are selected in such
a way that the CME front is clearly visible in the coronagraph images of all
the viewpoints. Furthermore, we try to fit the CME at times when the front is
already far out in both STA and STB COR2 images. Table 2 additionally contains
the GCS parameters (Lon, Lat, TA, AR, HA). Also the half width, $\lambda$, and
the CME ecliptic propagation angle, $\phi$, relative to Earth
($\phi_{\mathrm{Earth}}$) and relative to the two STEREO spacecraft
($\phi_{\mathrm{STA}}$ and $\phi_{\mathrm{STB}}$) obtained from EAGEL are
given. Lon is the longitude (here given in Stonyhurst coordinates) and Lat the
latitude of the apex of the idealized hollow croissant shaped model. The tilt
angle (TA) defines the tilt of the croissant, calculated with respect to the
solar equator. The half angle (HA) represents the angle between the center of
the footpoints and the aspect ratio (AR) is the ratio of the CME size in two
orthogonal directions.

When comparing $Lon$ (longitude from GCS reconstruction) and
$\phi_{\mathrm{Earth}}$ (longitude relative to Earth from the ecliptic cut),
it can be seen that the propagation direction obtained from the ecliptic cut
is quite comparable to (within 5° of) the propagation direction from the GCS
reconstruction. Only for events $\\#$6 and $\\#$10 we find differences of
about 30° and 10°, respectively. The reason can be found in the combination of
low/high latitude and large tilt angle. Therefore, the part within the
ecliptic plane does not correspond well to the main propagation direction
resulting from GCS reconstruction for these two CMEs.

Table 2: GCS parameter obtained from fitting the hollow croissant shape to the STEREO and SoHO coronagraph images and EAGEL results. Date: time set in EAGEL to perform the reconstruction. Lon: longitude (Stonyhurst coordinates), Lat: latitude, TA: tilt angle, AR: aspect ratio, HA: half angle from GCS. The remaining values are based on the ecliptic cut from EAGEL: $\lambda$: half width of the CME, $\phi_{\mathrm{Earth}}$, $\phi_{\mathrm{STA}}$, $\phi_{\mathrm{STB}}$: propagation direction with respect to Earth, STA, STB, respectively. |  |  GCS parameter  | EAGEL results
---|---|---|---
Nr. |  Date  | Lon | Lat | TA | AR |  HA  | $\lambda$ | $\phi_{\mathrm{Earth}}$ | $\phi_{\mathrm{STA}}$ | $\phi_{\mathrm{STB}}$
|  | [°] | [°] | [°] |  |  [°]  | [°] | [°] | [°] | [°]
1 |  2010-02-03 15:54  | 355 | -17 | -1 | 0.33 |  30  | 36 | -4 | 67 | 68
2 |  2010-03-19 17:39  | 23 | -12 | -7 | 0.29 |  19  | 30 | 22 | 44 | 93
3 |  2010-04-03 12:39  | 7 | -19 | 15 | 0.39 |  30  | 38 | 9 | 58 | 81
4 |  2010-04-08 06:39  | 1 | -10 | -20 | 0.28 |  30  | 31 | -2 | 70 | 69
5 |  2010-05-23 20:39  | 6 | 2 | -15 | 0.48 |  18  | 35 | -6 | 65 | 76
6 |  2010-10-26 14:39  | 18 | -35 | -28 | 0.51 |  30  | 18 | -11 | 95 | 69
7 |  2011-01-30 21:24  | 351 | -18 | -20 | 0.33 |  12  | 24 | -11 | 97 | 82
8 |  2011-02-15 04:08  | 10 | -10 | 27 | 0.87 |  29  | 49 | 10 | 77 | 104
9 |  2011-09-06 23:39  | 29 | 20 | -90 | 0.49 |  30  | 26 | 29 | 74 | 124
10 |  2012-01-23 04:39  | 19 | 41 | 64 | 0.77 |  55  | 37 | 9 | 99 | 123
11 |  2012-06-14 14:54  | 360 | -28 | 11 | 0.90 |  30  | 53 | 1 | 116 | 117
12 |  2012-07-12 17:54  | 8 | -12 | 68 | 0.46 |  30  | 26 | 14 | 106 | 129

###  3.2 WSA/HUX model

In the following paragraph, we summarize the main characteristics of the
numerical framework used here for modelling the physical conditions in the
evolving ambient solar wind flow. For this study, we make use of the framework
shown in Reiss . (2019, 2020), but the components of this framework were
developed by YM. Wang Sheeley (1995), Arge . (2003), Riley Lionello (2011),
and Owens Riley (2017). Specifically, we use magnetic maps of the photospheric
field from Global Oscillation Network Group (GONG) provided by the National
Solar Observatory (NSO) as input to magnetic models of the solar corona. Using
the Potential Field Source Surface model (PFSS; Altschuler Newkirk, 1969;
Schatten ., 1969) and the Schatten current sheet model (SCS; Schatten, 1971)
we compute the global coronal magnetic field topology. While the PFSS model
attempts to find the potential magnetic field solution in the corona with an
outer boundary condition that the field is radial at the source surface at 2.5
R⊙, the SCS model in the region between 2.5 and 5 R⊙ accounts for the
latitudinal invariance of the radial magnetic field as observed by Ulysses
(YM. Wang Sheeley, 1995). From the global magnetic field topology, we
calculate the solar wind conditions near the Sun using the established Wang-
Sheeley-Arge (WSA) model. To map the solar wind solutions from near the Sun to
Earth, we use the Heliospheric Upwind eXtrapolation model (HUX). This model
simplifies the fluid momentum equation as much as possible, by neglecting the
pressure gradient and the gravitation term in the fluid momentum equations as
proposed by Riley Lionello (2011). The HUX model solutions match the dynamical
evolution explored by global heliospheric MHD codes fairly well while having
low processor requirements. An example of the ambient solar wind, modeled by
WSA/HUX combination, is shown in Figure 2.

###  3.3 ELEvoHI ensemble modeling

ELEvoHI uses HI time-elongation profiles of CME fronts and assumes an
elliptical shape for those fronts to derive their interplanetary kinematics
(detailed information about the underlying Ellipse Conversion method can be
found in Rollett ., 2016). The tracking of each CME was done manually using
ecliptic time-elongation maps (j-maps; Sheeley ., 1999; Davies ., 2009),
generated by extracting ecliptic data from STA and STB HI images. Transients,
like CMEs, appear as a bright feature in the j-maps. To extract the time-
elongation profiles, we use the SATPLOT tool implemented in IDLTM SolarSoft.
It allows any user to measure the elongation, which is defined as the angle
between the Sun - observer (STA or STB) line and the CME front. ELEvoHI
converts the resulting time-elongation profiles to time-distance profiles,
assuming an elliptic frontal shape using the ELEvoHI built-in procedure
ELlipse Conversion (ELCon; Rollett ., 2016).

ELEvoHI accounts for the effect of the drag force exerted by the ambient solar
wind, which is incorporated in the model. The drag force is an essential
factor influencing the dynamic evolution of CMEs in the heliosphere. Within
ELEvoHI, the time-distance track is fitted using a drag-based equation based
on the drag-based model (DBM) given in Vršnak . (2013). The user has to define
the start- and end point for the DBM fit (usually around $30-100$ R⊙) in the
time-distance profile. In order to account for the de-/acceleration of the CME
due to drag, an estimate of the ambient solar wind speed is needed. Here we
make use of the WSA/HUX model (see Section 3.2). It provides the ambient solar
wind conditions for a full Carrington rotation (see Figure 2). We only
consider the part of the full map according to the start- and end-point
selected by the user, and the CME propagation direction and half width from
EAGEL. From this area surrounded by the white box in Figure 2, we take the
median of the solar wind speed and define the uncertainties to be $\pm$100 km
s-1, based on a study by Reiss . (2020). They considered nine years (mid 2006
to mid 2015) and report a mean absolute error of the WSA solar wind speed
prediction with respect to the in situ speed of 91 km s-1. The obtained
ambient solar wind speed with its uncertainty is split into steps of 25 km
s-1, which gives nine different input speeds to ELEvoHI. For each of the nine
input speeds DBM fitting is performed. ELEvoHI then selects the combination of
drag parameter and ambient solar wind speed that best fits the time-distance
profile for each ensemble member (for a detailed description see Rollett .,
2016). The selected drag parameter and solar wind speed is assumed to be valid
for the full CME front during the propagation in the heliosphere.

Since ELEvoHI is a 2D model, we are only interested in the propagation of a
CME in the ecliptic plane. $\phi$ and $\lambda$, in this plane, are provided
by EAGEL (see Section 3.1). The inverse ellipse aspect ratio, $f$, defines the
shape of the assumed CME front in the ecliptic plane, where $f=1$ represents a
circular front, while $f<1$ corresponds to an elliptical CME front (with the
semi-major axis perpendicular to the propagation direction)

Figure 2: Ambient solar wind speed provided by the WSA/HUX model for event
$\\#$5\. The white box defines the area that is used to calculate an estimate
of the ambient solar wind speed for the ensemble member of ELEvoHI
corresponding to the minimum propagation direction ($\phi_{\mathrm{STA}}=56$°)
with the maximum half width ($\lambda=50$°). The black box indicates the total
area based on all the ensemble members of ELEvoHI for this event. The
longitude of 0° corresponds to the longitude of Earth.

To run ELEvoHI in ensemble mode, we vary $\phi$, $\lambda$, and $f$. A details
description can be found in Amerstorfer . (2018) and the code is available
online (see Section 6). $\phi$ and $\lambda$ vary over a range of $\pm 10$°
from their values obtained from EAGEL, with a step size of $2$° and $5$°,
respectively. This range is defined based on a study by Mierla . (2010), who
report an uncertainty in the parameters when different users manually perform
GCS reconstruction. Note that the propagation direction and the half width
obtained from EAGEL are rounded to even numbers and to whole tens,
respectively. For $f$ we set a fixed range from $0.7-1.0$ ($0.1$ step size).
Thus we get a total of 220 ensemble members for one ELEvoHI event (i.e. 11
values of $\phi$, 5 values of $\lambda$ and 4 values of $f$). For each
ensemble member we select a different sector from the ambient solar wind
provided by the WSA/HUX model combination according to the propagation
direction, half width, start- and end-point. In Figure 2, the WSA/HUX model
results for event $\\#5$ are shown. The white box indicates the area from
which the ambient solar wind speed for one individual run of ELEvoHI is
computed. Shown is the area for the minimum propagation direction,
$\phi_{\mathrm{STA}}$ of 56° with a $\lambda$ of 50°. For each ensemble member
the area surrounded by the white box is slightly different according to $\phi$
and $\lambda$. The black box plotted indicates the total area based on all
ELEvoHI ensemble members for this event.

Running ELEvoHI in ensemble mode enables us to calculate a mean and a median
predicted CME arrival time and also to define an uncertainty. In addition, we
can give a probability for whether a CME is likely to hit Earth or not. When
all of the 220 ensemble members predict an arrival at Earth, we assume the
predicted likelihood of an Earth hit to be 100%.

##  4 Results

Table 3: List of predicted median arrival times (Date) and the standard deviation (SD) based on STA and STB observations, respectively. STA - STB gives the difference between the predicted median arrival times. $v$ is the predicted median arrival speed with the standard deviation and $v_{\mathrm{STA-STB}}$ is the difference in arrival speed between STA and STB predictions Nr. | Date STA |  SDSTA | Date STB |  SDSTB | STA - STB | $v_{\mathrm{STA}}$ | $v_{\mathrm{STB}}$ | $v_{\mathrm{STA-STB}}$
---|---|---|---|---|---|---|---|---
|  | [h] |  | [h] | [h] |  [km s-1] |  [km s-1] |  [km s-1]
1 |  2010-02-07 11:24 | 1.5 |  2010-02-07 20:24 | 2.1 | -9.0 | $455\pm 17$ | $395\pm 11$ | 60
2 |  2010-03-24 07:17 | 9.1 |  2010-03-24 16:40 | 4.1 | -9.5 | $401\pm 32$ | $351\pm 11$ | 50
3 |  2010-04-05 13:23 | 2.5 |  2010-04-05 16:06 | 0.4 | -2.7 | $649\pm 37$ | $625\pm 5$ | 24
4 |  2010-04-11 16:07 | 0.6 |  2010-04-12 00:12 | 5.1 | -8.1 | $443\pm 6$ | $391\pm 33$ | 52
5 |  2010-05-27 17:36 | 1.9 |  2010-05-28 02:26 | 1.2 | -8.8 | $455\pm 9$ | $407\pm 9$ | 48
6 |  2010-10-30 11:24 | 1.4 |  2010-10-30 04:43 | 7.1 | 6.7 | $432\pm 7$ | $476\pm 45$ | -44
7 |  2011-02-04 01:08 | 2.4 |  2011-02-03 22:24 | 7.3 | 4.5 | $387\pm 9$ | $446\pm 34$ | -59
8 |  2011-02-18 06:22 | 2.8 |  2011-02-18 10:34 | 6.1 | -4.3 | $478\pm 18$ | $407\pm 50$ | 71
9 |  2011-09-10 18:55 | 14.9 |  2011-09-10 09:48 | 5.4 | 9.1 | $396\pm 46$ | $430\pm 18$ | -34
10 |  2012-01-24 17:49 | 4.0 |  2012-01-24 13:29 | 3.6 | 4.3 | $793\pm 103$ | $982\pm 150$ | -189
11 |  2012-06-16 15:47 | 3.8 |  2012-06-16 07:53 | 5.2 | 7.9 | $712\pm 72$ | $749\pm 143$ | -37
12 |  2012-07-14 22:16 | 4.9 |  2012-07-14 18:53 | 3.7 | 3.5 | $658\pm 80$ | $579\pm 28$ | 89

We perform ELEvoHI ensemble modeling for 12 CMEs between February 2010 to July
2012 (see Table 1) and compare the predicted arrival times based on STA and
STB HI observations with each other. The CMEs propagated close to the ecliptic
plane and showed clear in situ signatures at L1. A prerequisite for the chosen
CMEs was that the CMEs could be tracked unambiguously in both STA and STB HI
j-maps.

In Table 3, we list the predicted ensemble median arrival times and speeds
with their standard deviation for each CME under study. It further contains
the difference between the predictions from the two vantage points. We find
that the predicted arrival times for STA and STB can deviate by up to 9.5 hrs
while the mean difference is 6.5 hrs. The mean difference in the arrival speed
is 63 km s-1, with an exceptionally large discrepancy of 189 km s-1 for event
$\\#10$.

The largest arrival time differences are found for events $\\#2$ and $\\#9$.
The arrival probability, based on the number of ensemble members that are
predicted to hit Earth, is 79% for event $\\#2$ and only 56% for event $\\#9$.
According to their relatively large angle of propagation with respect to the
Sun-Earth line, the CMEs $\\#2$ ($\phi_{\mathrm{Earth}}=22$°, $HA=30$°) and
$\\#9$ ($\phi_{\mathrm{Earth}}=30$°, $HA=30$°) are considered as ”flank hits”.
In such cases, ELEvoHI tends to predict the CME arrival time to be later than
detected in-situ. The reason may be found in the assumed circular CME front
for $f=1.0$. For future versions of ELEvoHI, we will consider different
approaches to tackle such extreme delays for flank encounters e.g. by changing
the values for $f$ (from 0.4 to 0.7). Braga . (2020) found a value of $f\sim
0.6$ for most of the CMEs in their study.

Event $\\#$11 occurred on June 14, 2012 and was studied e.g. by Kubicka .
(2016) who report two preceding CMEs. However, the WSA/HUX model does not
provide the ambient solar wind conditions with preceding CMEs included and is
therefore most probably not suitable for interaction events. The events
$\\#1$, $\\#4$, and $\\#5$ also show large differences in the predicted
arrival times based on STA and STB observations. However, these differences
are most certainly related to large variance in the modeled ambient solar wind
speeds that are used as input to ELEvoHI (see Section 4.2 and Figure 5 and 6).

Figure 3: Two snapshots of the CME propagation for one ensemble member based
on STA (red) and STB (blue) observations for event $\\#4$. The ambient solar
wind is computed using the WSA/HUX model combination. The elliptical CME
fronts from one ensemble member based on STA and STB observations are shown in
red and blue, respectively. The thick curved lines in red and blue show the
intercept of the idealized elliptical front of the CME and the tangent (gray
lines) for each time step over the course of the simulation for STA and STB,
respectively. Link to the movie
(https://figshare.com/articles/media/20110130_AB_tangent_movie/13077608).
Figure 4: Ambient solar wind speed at the tangent points for event $\\#4$.
Plotted are time series of the elongation angles of the tangent points as seen
from STA (top panel) and STB (bottom panel) colour-coded according to the
speed of the ambient solar wind at that tangent point.

###  4.1 Tracking different parts of the CME front

It is important to keep in mind that different parts of the CME front are
tracked in STA and STB HI images. This leads to different input conditions to
the ELEvo propagation model for STA and STB. ELEvoHI is designed to take HI
tracks for the same CME from different viewpoints. Ideally, predictions should
give the same CME speed and direction in both cases. One problem is, however,
that the CME is not behaving as a single coherent entity, but is instead
moving with different speeds at different longitudes (Owens ., 2017), which is
not incorporated within ELCon nor in any other HI conversion method (e.g. SSE,
FP, and HM).

Figure 3 presents two snapshots of a movie for event $\\#4$, with the ambient
solar wind provided by WSA/HUX model combination and the positions of various
spacecraft and planets. The elliptical CME fronts from one ensemble member
based on STA and STB observations are shown in red and blue, respectively. The
gray lines from the two STEREO spacecraft to the elliptical CME fronts are
plotted. These tangents correspond to the elongations of the leading edge of
the CME at these times. At the end of these lines, we add a point, which is
the ’tangent point’ at each time step. Over the course of the simulation,
these points trace out curved lines, in red and blue for STA and STB,
respectively. From Figure 3, it is obvious that, in the near-Sun part of the
HI FOV, the observed leading edge is close to the apex of the idealized CME
front for both STEREO spacecraft. As the CME propagates, the tangent point,
i.e. the part of the CME with the greatest elongation seen by STA and STB
progressively moves out to the flanks of the ellipse. Based on the
observations of these tangent points, the prediction for the whole front is
conducted. Hence, the apex of the CME is, if at all, only observed for a short
period of time. In order to get an estimate of the CME Earth arrival we have
to assume a designated shape of the CME front, which is in our case, an
ellipse. As shown by Owens . (2017) this assumption might not be valid since
the CME interacts with the ambient solar wind.

###  4.2 Effect of the ambient solar wind

When considering different points along the idealized elliptical CME front, it
is noteworthy that the ambient solar wind speeds at these points would likely
be different. Furthermore, the part of the CME front corresponding to the
greatest elongation as seen by STA and STB (i.e. the points corresponding to
the tangent to the CME front) would propagate in different ambient solar wind
conditions. In Figure 4, the modeled time-elongation profiles of the tangent
points seen from STA (top panel) and STB (bottom panel) for event $\\#$4 are
shown.

These profiles are obtained from one modeled ensemble member of the ELEvoHI
prediction, separately for STA and STB (see Figure 3), and are therefore
available from 2010-04-08 11:00 until 2010-04-14 10:00. As long as the CME
front could be tracked in HI images (until about 2010-04-10 01:00), the
plotted profiles are consistent with the measured HI time-elongation profiles,
obtained using the SATPLOT tool. The colors represent the speed of the ambient
solar wind at the corresponding points. Due to the propagation of the modeled
CME in the heliosphere, the elongation of the tangent point ranges from
roughly 8° to about 92° and the speed of the ambient solar wind at these
points ranges from 330 km s-1 to 500 km s-1, with a maximum speed of 530 km
s-1 at about 66°, for STA (top panel in Figure 4). The range of the elongation
is similar for STB (6° to 91°) but the ambient solar wind speed ranges only
from $\approx$450 km s-1 to $\approx$365 km s-1.

Figure 5: Ambient solar wind speed provided by the WSA/HUX model combination
for all 12 events under study. The black boxes define the areas that are used
to estimate how structured the ambient solar wind is for each CME. Longitude
of 0° corresponds to the longitude of Earth. Figure 6: Standard deviation of
the ambient solar wind vs. the arrival time difference between STA and STB
predictions. The Pearson correlation coefficient for all events under study
(black) is calculated. In blue we present the Pearson correlation coefficient
and a linear fit when excluding the outliers (indicated by the red boxes),
i.e. flank hits (events $\\#2$ and $\\#9$) and the CME-CME interaction event
(event $\\#11$).

In the previous paragraph, we considered the ambient solar wind speed at the
tangent point for one ensemble member. Additionally, we examine the
distribution of the ambient solar wind speed considered for all ensemble
members (see black boxes in Figures 2 and 5) that are used as input to ELEvoHI
for a single CME. From the areas framed by the black boxes, we calculate the
standard deviation and correlate those to the absolute values of the
difference between STA and STB arrival time predictions for each event (see
Figure 6). This gives us the possibility to check the influence of the ambient
solar wind on the arrival time differences. We obtain a Pearson correlation
coefficient of $cc=0.52$ for all events under study. However, when excluding
events $\\#2$ and $\\#9$, which are considered as ”flank hits”, and excluding
event $\\#11$ (CME-CME interaction event), the Pearson correlation coefficient
increases to $cc=0.92$. This indicates that a more structured ambient solar
wind (i.e. a larger standard deviation) leads to a larger differences between
STA and STB arrival time prediction.

###  4.3 Comparison to in situ arrivals

Figure 7: Frequency distributions derived from all ensemble members for the
arrival time prediction (top panel) and the arrival speed prediction (bottom
panel) based on HI data from STB (blue) and STA (orange), respectively. In the
top panel, positive values correspond to a late arrival time prediction while
negative values indicate an early arrival prediction. Positive/negative values
in the bottom panel indicate an over-/underestimated arrival speed prediction.
The black horizontal bars show the median values of the distributions of all
the ensemble members for STB and STA.

Figure 7 shows the distributions of the arrival time and arrival speed
differences with respect to the in situ arrivals for all ensemble members for
each CME. Blue and orange correspond to STB and STA ensemble predictions,
respectively. The black horizontal lines indicate the median values of each
distribution. When comparing the median predicted arrival times to the in situ
arrivals, we obtain a mean absolute error (MAE) over all events of
$7.5~{}\pm~{}9.5$ hrs and a root mean square error (RMSE) of $\approx 10.4$
hrs. A mean error (ME) of $\approx 4$ hrs indicates, in this setup, that
ELEvoHI tends to predict the arrivals too late. The highest arrival time
discrepancy is found for event $\\#$9 where the prediction based on STA is 31
hrs too late. When comparing the median predicted arrival speeds to the in
situ speeds we get a MAE of 87 $\pm$ 111 km s-1, a RMSE of $\approx$123 km s-1
and a ME of $\approx$52 km s-1. The highest speed difference is found for the
STB prediction of event $\\#$10, overestimating the arrival speed by 369 km
s-1.

Interestingly, event $\\#$10 gives an accurate predicted arrival time, even
though the predicted arrival speed is highly overestimated. When performing
GCS reconstruction we obtain a high latitude and a large tilt angle for this
CME meaning that the 3D propagation direction differs from that in the
ecliptic plane (see Table 2). As already mentioned, event $\\#$11 is a CME-CME
interaction event which explains the large discrepancy especially for the
predicted arrival speed. The reason might be found in an extremely low drag
due to preconditioning in the interplanetary space (Rollett ., 2014; YD. Liu
., 2014; Temmer Nitta, 2015).

##  5 Discussion and Conclusions

We present the ELEvoHI ensemble modeling results for 12 CMEs, occurring
between February 2010 and July 2012, that were observed by both STEREO
spacecraft. This study mainly focuses on the difference of the modeled arrival
time and arrival speed when using STA and STB HI observations, separately. We
find on average a difference of 6.5 hrs between arrival time predictions from
the two spacecraft but the largest difference is about 9.5 hrs for event
$\\#9$. For the arrival speed we find a mean difference between STA and STB
predictions of 63 km s-1 with a maximum difference of 189 km s-1 for event
$\\#10$.

ELEvoHI tends to predict the arrival time later than observed for CMEs that
are considered as ’flank hits’ (event $\\#2$ and event $\\#9$). For such
events the propagation direction with respect to Earth is larger than 20°, and
not all of the ensemble members predict an Earth impact. The reason for the
late arrival prediction may be found in the assumed circular shape (for
$f=1.0$) and the highly curved flanks.

We provide two CME arrival time and arrival speed predictions, from STA and
STB observation, for the same CME to examine the reasons for the discrepancy
between these two predictions. We find, that the CME front propagates in
different ambient solar wind conditions when observed in STA and STB HI
images. However the kinematics of the CME front obtained e.g. by STA data is
used for modeling of the whole CME front, including the Earth-directed part.
The same applies for predictions based on STB data, which is the reason for
the differences in the predicted arrival times based on STA and STB
observations.

We further see, that an ambient solar wind exhibiting a high variance within
the area used for ELEvoHI model predictions leads to larger discrepancies
between STA and STB model predictions. We obtain a Pearson correlation
coefficient ($cc=0.92$), when excluding flank hits (events $\\#2$ and event
$\\#9$) and the CME-CME interaction event (event $\\#11$). Furthermore, we
assume that in such cases the CME front is more likely to deform from an
idealized elliptical shape due to interaction with the ambient solar wind
(Riley Crooker, 2004; Owens ., 2017).

The current CME forecasting abilities in the community are summarized in Riley
. (2018). They analyzed CME forecasts that have been submitted to the
Community Coordinated Modeling Center (CCMC) scoreboard from 2013 to mid-2018.
The CCMC scoreboard is a platform provided to scientists to compare their
forecasts with each other in real-time. Riley . (2018) found that the CME
shock arrival times for all models combined are predicted on average within
$\pm 10$ hrs but with standard deviations of sometimes more than 20 hrs. The
best model performance was found for the WSA-ENLIL+Cone model (Odstrcil .,
2004), run by the UK Met Office, having a bias of 1 hour, a MAE of 13 hrs and
a standard deviation of 15 hrs. The results of this study are similar to the
findings of Riley . (2018) when comparing the modeled arrival times to the
actual arrivals of CMEs, as determined from in situ measurements. Here, we
only perform hindcasts of CME arrivals. For the 24 arrival predictions (12
based on STA and 12 based on STB observations), we obtain a MAE of
$7.5~{}\pm~{}9.5$ hrs, a RMSE of $\approx 10.4$ hrs and a ME of $\approx 4$
hrs for the arrival time. For the arrival speed, we get a MAE of 87 $\pm$ 111
km s-1, a RMSE of $\approx$123 km s-1 and a ME of $\approx$52 km s-1.

As already mentioned, event $\\#$11 is a CME-CME interaction event studied
e.g. by Kubicka . (2016). This CME was closely preceded by two other CMEs that
erupted one and two days before this event and that altered the conditions in
the heliosphere. The arrival time prediction for this CME is about 11 hrs too
early, while the arrival speed is greatly overestimated (by 260 km s-1) using
the ambient solar wind solutions provided by the WSA/HUX model. However, this
model does not consider preceding CMEs and is likely not valid in such cases.
An additional approach to infer the ambient solar wind conditions in the low
heliosphere is shown in Barnard . (2019). In this study the authors
established a statistical relationship between the solar wind speed in the low
heliosphere and the variability in HI images. A recent study by Amerstorfer .
(2021) focuses on different input parameters to ELEvoHI including three
possible methods to infer the ambient solar wind conditions needed by the
model. First, the ambient solar wind speed is obtained from in-situ
measurements at 1 AU. Second, the solar wind speed is based on an statistical
approach using 14 years of OMNI data. Third, an estimate of the ambient solar
wind speed is obtained from the WSA-HUX model combination. In their study,
Amerstorfer . (2021) concluded that the third approach provides the best
results.

ELEvoHI provides ensemble predictions based on various inputs, namely
propagation direction, half width, inverse aspect ratio and ambient solar wind
speed. In the current version, ELEvoHI is not able to react to possible
deflections of a CME during its propagation. Furthermore, the elliptical CME
shape, once defined by the input parameters, does not change during
propagation. This has been shown to be invalid by, for example, Rollett .
(2014), who performed a case study by combining HI data with in situ data to
ascertain the kinematics of the 2012, March 7 CME. The authors demonstrated
evidence for an asymmetric evolution of the CME, which was caused by the
preconditioned ambient solar wind resulting in a different drag regime
influencing different parts of the CME.

CME deflection (e.g. Y. Wang ., 2004, 2014) and deformation (e.g. Barnard .,
2017; Kay Nieves-Chinchilla, 2020) are important factors when considering CME
propagation in the heliosphere. The authors found that the failure to take
these factors into account would likely lead to uncertainties in the arrival
time and arrival speed prediction. Barnard . (2017) additionally showed that
different tracks lead to quite different CME arrival time predictions. By
using HI observations with better solar wind modeling and varying CME frontal
shapes we should be able to improve our current arrival time predictions
(Barnard ., 2020).

A number of studies have taken advantage of stereoscopic HI observations, from
the two STEREO spacecraft, to glean information on CME propagation and
evolution (e.g. Y. Liu ., 2010; Lugaz, 2010; Davies ., 2013; Volpes Bothmer,
2015). Braga . (2020) studied 14 CMEs using the drag model and a modified
version of the ELCon model, to get the CME parameters (e.g. $\phi$, $\lambda$,
$f$) based on HI observations from both STEREO spacecraft simultaneously. For
the five events, that are also included in our study, we obtain similar
results as Braga . (2020). Please note that we run ELEvoHI in ensemble mode in
this study. We believe, that a stereoscopic view on CMEs incorporated in
ELEvoHI will improve the arrival time predictions substantially. Therefore, we
strongly support ESA’s L5 mission, equipped with a heliospheric imager
(Lavraud ., 2016; Kraft ., 2017), and an additional heliospheric imager at L1.
Fortunately, the upcoming Earth-orbiting PUNCH mission (launch planned in
2023) will also possess wide-angle white-light heliospheric imagers, as well
as a coronagraph, and will be able to provide additional observations of CMEs.
Based on information from these additional vantage points, more accurate CME
arrival predictions are likely to be achieved. Since ELEvoHI is ready to be
used in near real-time, future HI observations are essential for further CME
arrival predictions. STA, currently near L5, will have moved beyond L4 by
2027, so it will be necessary to have heliospheric imagers that are observing
the space between Sun and Earth after around 2030.

In a next step, we want to further develop ELEvoHI in such a way that it can
combine HI data from two vantage points in order to constrain the CME and
exclude ensemble runs that are not consistent with the observations. Also the
CME shape can be constrained by multiple HI observations and therefore, we aim
to make the CME front deformable during the propagation through the
heliosphere. Hence, the assumed elliptical CME front would be able to adjust
according to the ambient solar wind conditions. It was already shown in
previous studies (e.g. Scott ., 2019; Chi ., 2020) that ghost fronts in the HI
observations can be used to infer the structure of a CME. Using their
approach, we also aim to improve our model by verifying and constraining the
CME shape.

##  6 Data Sources

Data

STEREO/HI: https://www.ukssdc.ac.uk/solar/stereo/data.html
STEREO/COR2 and SoHO/LASCO: https://sdac.virtualsolar.org/cgi/
NSO/GONG: https://gong.nso.edu/data/magmap/
HELCATS: https://www.helcats-fp7.eu
ICMECAT: https://doi.org/10.6084/m9.figshare.6356420

Model

ELEvoHI is available at https://zenodo.org/record/3873420.
EAGEL is available at https://zenodo.org/record/4154458.

Results

The visualization of each prediction result, i.e. movies and figures, can be
downloaded from https://doi.org/10.6084/m9.figshare.12758312.

Software

IDLTM Version 8.4
Python 3.7.6
SATPLOT:
https://hesperia.gsfc.nasa.gov/ssw/stereo/secchi/idl/jpl/satplot/SATPLOT_User_Guide.pdf

###### Acknowledgements.

T.A., J.H., M.B., M.A.R., C.M., A.J.W., R.L.B, and U.V.A. thank the Austrian
Science Fund (FWF): P31265-N27, J4160-N27, P31659-N27, P31521-N27.

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