# Collection, Collation, and Comparison of 3D Coronal CME Reconstructions

###### Abstract

Predicting the impacts of coronal mass ejections (CMEs) is a major focus of
current space weather forecasting efforts. Typically, CME properties are
reconstructed from stereoscopic coronal images and then used to forward model
a CME’s interplanetary evolution. Knowing the uncertainty in the coronal
reconstructions is then a critical factor in determining the uncertainty of
any predictions. A growing number of catalogs of coronal CME reconstructions
exist, but no extensive comparison between these catalogs has yet been
performed. Here we develop a Living List of Attributes Measured in Any Coronal
Reconstruction (LLAMACoRe), an online collection of individual catalogs, which
we intend to continually update. In this first version, we use results from 24
different catalogs with 3D reconstructions using STEREO observations between
2007–2014. We have collated the individual catalogs, determining which
reconstructions correspond to the same events. LLAMACoRe contains 2954
reconstructions for 1862 CMEs. Of these, 511 CMEs contain multiple
reconstructions from different catalogs. Using the best-constrained values for
each CME, we find that the combined catalog reproduces the generally known
solar cycle trends. We determine the typical difference we would expect
between two independent reconstructions of the same event and find values of
4.0∘ in the latitude, 8.0∘ in the longitude, 24.0∘ in the tilt, 9.3∘ in the
angular width, 0.1 in the shape parameter $\kappa$, 115 km/s in the velocity,
and 2.5$\times$1015 g in the mass. These remain the most probable values over
the solar cycle, though we find more extreme outliers in the deviation toward
solar maximum.

Space Weather

Heliophysics Science Division, NASA Goddard Space Flight Center, Greenbelt,
MD, USA Dept. of Physics, The Catholic University of America, Washington, DC,
USA Predictive Science Inc., San Diego, CA, USA

Christina<EMAIL_ADDRESS>
LLAMACoRe combines 24 different catalogs of 3D CME reconstructions and
identifies which entries correspond to the same event

We determine the most likely uncertainties in the reconstructed latitude,
longitude, tilt, angular width, shape, velocity, and mass

Most events are well-described by the most likely uncertainty but we do find a
few cases with extremely-differing reconstructions

Coronal mass ejections (CMEs) are large explosions from the solar atmosphere
that propagate out through space. Knowing where they are going is important
for predicting space weather. We have satellites staring at the Sun from
different directions, which we can use to reconstruct the 3D location and
orientation of a CME. A few groups have routinely reconstructed nearly every
CME over long time spans, other groups reconstruct tens of events for smaller,
focused studies. Here we collect as many reconstructions as we can find for
CMEs that occurred between 2007–2014. We match the reconstructions between
catalogs, producing a new catalog, LLAMACoRe, that contains 1862 CMEs with at
least one reconstruction. 511 CMEs have more than one reconstruction. We
compare the behavior of this set to previously known trends and find good
general agreement. For the cases with multiple reconstructions, we can
quantify how much uncertainty we expect in the reconstructed values, which
should be a useful metric for forecasters. We find that the most probable
uncertainty in each parameter tends to remain relatively constant over time.
When the Sun becomes more active and releases more energetic CMEs there are
more outlier cases, but most cases remain at lower values.

##  1 Introduction

Coronal mass ejections (CMEs) are known to be major drivers of space weather
effects at Earth as well as elsewhere in the solar system and, as such, they
are routinely monitored via remote-sensing imagery and included in forecasting
models by space weather agencies [<]e.g.,¿Piz11, Sha17. When a CME is ejected
from the Sun, its eruptive signatures are usually observed on the solar disk
and/or off the limb in extreme ultraviolet or X-ray data [<]e.g.,¿Hud01, and
its evolution through the corona is often followed in white-light coronagraph
images [<]e.g.,¿Vou13. While some models employed in heliophysics research are
able to simulate the eruption and evolution of CMEs from the lower solar
atmosphere outwards [<]using photospheric data as a boundary condition;
e.g.,¿Jin17, Kay22, Lyn21, Tor18, many research models and all operational
ones avail themselves of coronagraph data to derive input parameters to inject
CMEs into the computational domain at some altitude in the outer corona
[<]often set at 21.5 $R_{\odot}$ or 0.1 au; e.g.,¿Dum21, Mah22, Mos18, Ods23.
Even more so, in the former class of models white-light coronal imagery is
frequently employed to validate simulation results [<]e.g.,¿Kay17AR, Lug07,
Lyn16, Man08.

One of the major issues of observing CMEs in the corona remotely is that the
line-of-sight integrated electron density measured by a white-light camera
manifests itself as a 2D image. This means that most of the information on the
3D structure and trajectory of a CME is not retrieved, and the corresponding
geometric and kinematic properties can only be analyzed based on their
projection onto a plane. A major turning point in remote-sensing observations
of CMEs was reached in 2006 with the launch of the twin Solar Terrestrial
Relations Observatory [<]STEREO;¿Kai08 spacecraft away from the Sun–Earth
line, which enabled stereoscopic views of the solar corona alongside the near-
Earth perspective afforded by the Solar and Heliospheric Observatory
[<]SOHO;¿Dom95 mission, launched in 1995 and located at the Sun–Earth Lagrange
L1 point. Taking advantage of observations of the same CME from three
viewpoints, the heliophysics research community has developed a wealth of
models and techniques to recover the 3D structure of CMEs in the corona using
multi-point, remote-sensing measurements. These methods include triangulation
[<]e.g.,¿Inh06, Lie11, Mie08, Mor10 and fitting [<]e.g.,¿Isa16, Mil13, The09,
Zha22 techniques that, while similar in practical implementation, differ in
their basic assumptions about the CME morphology—i.e., fitting techniques
require a pre-determined geometry whereas triangulation techniques focus on
identifiable features in the data without requiring such models. Examples of
different available reconstruction methods were reviewed and applied to a few
events observed in coronagraph data from two or three viewpoints by Mie10 and
Fen13.

Due to their practicality of use, coronagraph fitting tools have been
extensively included in CME analyses, e.g., to investigate the coronal
evolution of case study events [<]e.g.,¿Dum21wde, Vou11, to derive input
parameters for heliospheric modeling [<]e.g.,¿May15, Pal19, and even to
interpret simulation data [<]e.g.,¿Ben23, Ver23. The basic principle behind
white-light fitting techniques is that a parameterized CME 3D “shape” is
projected onto nearly-simultaneous images (usually from two or three
coronagraphs) and manually adjusted until it visually matches observations to
the best of the user’s abilities. Among the simplest CME geometries is the so-
called Ice Cream Cone [<]ICC;¿xue05b model, which consists of a conical base
culminating in a semi-spherical front and is fully defined by four properties:
latitude, longitude, and height of the apex, as well as the width of the cone.
A conical geometry is implemented, e.g., by the Stereo CME Analysis Tool
[<]StereoCAT;¿DONKI, which uses imagery from two viewpoints and a cone’s
projection onto them to retrieve 3D CME parameters. A similar morphology is
implemented in the “teardrop” 3D lemniscate employed by the NOAA Space Weather
Prediction Center CME Analysis Tool [<]SWPC-CAT;¿Mil13. More complex
geometries include the croissant-shaped Graduated Cylindrical Shell
[<]GCS;¿The11 model, which to the ICC parameters of latitude, longitude, and
height of the CME apex adds the tilt of the axis, the distance between the two
legs, and the aspect ratio of the parameterized shell. Further modifications
to the GCS morphology include the Flux Rope in 3D [<]FRi3D;¿Isa16 model, which
adds three additional distortion parameters (skew, pancaking, and flattening)
to reproduce a larger variety of CME characteristics in the corona. By fitting
the same CME at two or more times using any of these models, it is possible to
derive the speed of the apex (or nose). Since the pre-determined CME
geometries for coronagraph fitting consist of “hollow” 3D shells, it is not
possible to determine CME mass using such models alone, but they can be
combined with other analysis methods [<]e.g.,¿Col09, Plu19, Sav13 to
ultimately yield a mass estimate.

Despite the ease of use and widespread availability of coronagraph fitting
techniques in the research and operational communities, there are still
several issues to be considered. First of all, many CMEs observed in the
corona deviate significantly from the generally well-behaved geometries
assumed by the models, making reconstructions particularly difficult
[<]e.g.,¿Col15, Nie22. Additionally, alongside the intrinsic uncertainties
attached to each model, it is important to mind the inherent subjectivity of
the user performing the fitting [<]e.g.,¿Sin22, Ver23. In fact, it has been
shown that, even when the exact same background solar wind conditions are
used, coronagraphic reconstructions of CMEs performed by different users, or
even by the same individual employing different CME geometries, can lead to
significantly different CME arrival time predictions [<]e.g.,¿Pal22enlil. To
mitigate the innate uncertainties and errors associated with a single set of
coronal reconstructions performed by a single user, the research community has
often modeled CME propagation in the heliosphere either by running ensemble
simulations [<]e.g.,¿Dum18, Kay18, Lee13, May15AT or by considering “clouds”
of synthetic observers around the target of interest [<]e.g.,¿Asv21, Mah23,
Pal23, Sco19.

Given the premises outlined above, it follows that having a “super-database”
of coronal reconstructions available to the community would be beneficial not
only to determine the characteristic uncertainties more precisely via large
statistics, but also for forecast-oriented researchers to test and validate
their models. To this end, we have perused the existing literature and
collected published catalogs of CME coronal reconstructions to the best of our
abilities, making sure to group together fits performed on the same event. We
have then analyzed the full “meta-catalog” in search of possible trends,
including explorations of characteristic errors with respect to various CME
properties and how such uncertainties vary with differing numbers of available
reconstructions. The manuscript is organized as follows. In Section 2, we
present the individual catalogs and specify how reconstructions have been
connected to single CME events. In Section 3, we analyze and discuss the
general CME properties (including distributions, variations, and correlations)
emerging from the collected reconstructions. In Section 4, we focus on the
events that appear in two or more catalogs and determine the characteristic
uncertainties and their variation with number of reconstructions. We discuss
our results in Section 5 and present our conclusions in Section 6.

##  2 Data Collection

We have created the largest-to-date collection of 3D coronal CME
reconstructions by combining the results of existing catalogs. This Living
List of Attributes Measured in Any Coronal Reconstruction, or LLAMACoRe, is
intended to be a continually evolving database, which we host online at
osprei.space/llamacore. This work presents the first iteration of LLAMACoRE.
In this first version, we focus on the time period 2007–2014, which is when
both STEREO spacecraft were active and the largest number of 3D CME
reconstructions have been performed using white-light data from the C2 and C3
cameras part of the Large Angle and Spectrometric Coronagraph [<]LASCO;¿Bru95
on board SOHO and/or the COR1 and COR2 telescopes part of the Sun Earth
Connection Coronal and Heliospheric Investigation [<]SECCHI;¿How08 on board
STEREO. We emphasize that no new reconstructions were performed for this work,
we have simply compiled existing results and identified matching cases between
catalogs. Before analyzing the combined results we present the individual
catalogs incorporated into LLAMACoRe.

###  2.1 Individual Catalogs

We use 21 different source catalogs to build LLAMACoRe. Each of these catalogs
uses imagery from at least two satellites to reconstruct the 3D direction of a
CME at at least one point during its coronal propagation. At a minimum, each
catalog contains the reconstructed latitude and longitude for each of their
events and contains at least five events. These must be a reconstructed
position of the CME apex in the corona and not simply the initial direction of
the source region of the CME. We also do not include catalogs that only
include the position angle as this only provides a projected location on the
plane of sky, not a distinct location in 3D space. A few sources reconstruct
CMEs multiple times using different techniques (e.g., both GCS and ICC). Some
sources use the positional information from a different source but add in new,
additional values, such as the velocity or mass. We have refined these 21
sources into 24 different catalogs, splitting the sources with multiple
techniques and combining those with repeated results.

Table 1 lists these catalogs by name, either by their given name, in the case
of large established sets, or by their author’s last name. It also includes
the appropriate reference, the time range covered by the catalog, the number
of events (nEvents), the number of events that appear in at least one other
catalog (nMulti), and the parameters that the catalog includes. For the
parameters, we collect the latitude (lat), longitude (lon), orientation or
tilt (tilt), angular width (AW), GCS kappa ($\kappa$), velocity (v), and mass
(M). We note that the AW is the half width, the common version typically
reported in the literature, and there is some subtlety in comparing the AW
from different reconstruction techniques, which we largely ignore. The
$\kappa$ parameter is the least intuitive of these parameters, and it is
essentially an aspect ratio. For a constant AW, increasing $\kappa$ will make
the CME “thicker” or “fatter” in the edge-on direction. Its mathematical
definition can be found in The06. Some catalogs may have additional features,
such as CME source regions or associations with in situ events, but we do not
include these within LLAMACoRE at this time. If a catalog includes multiple
coronal reconstructions for the same event at different distances/times then
we only include the outermost one, which should incorporate the most coronal
evolution of the CME and correspond to its properties as the CME begins its
interplanetary journey. The following list briefly describes each of our 24
catalogs.

  1. 1.

AFFECTScat [Bosman . (2012)] – The Advanced Forecast For Ensuring
Communications Through Space (AFFECTS) catalog began as a Ph.D. thesis project
and continued with project support from the 7th Framework Programme of the
European Union. AFFECTs focused on the STEREO SECCHI/COR2 field of view and
identified 1071 CMEs. A subset of these was then fit with the SWPC-CAT model.

  2. 2.

AFFECTSgcs [Bosman . (2012)] – Analogous to the AFFECTScat set but for a
subset of cases for which GCS reconstructions were performed. The KinCAT
catalog builds upon the AFFECTS GCS reconstructions, using the same position
and size, but adds in the velocity and mass. Our “AFFECTSgcs” catalog only
includes the CMEs that have AFFECTS GCS reconstruction but no additional
KinCAT data.

  3. 3.

AFFKin [Bosman . (2012), Pluta . (2019)] – The overlap between the AFFECTS GCS
and the KinCAT catalogs, which we refer to as “AFFKin.” Reconstructions that
are included in AFFKin are not included in AFFECTSgcs or our “KinCAT” catalog.

  4. 4.

Braga [Braga . (2017)] – The CORSET3D method is applied to STEREO SECCHI/COR2
images. This method updates CORSET [Goussies . (2010)], a supervised computer
vision algorithm, to function in 3D using tie-pointing and triangulation.

  5. 5.

DONKI [Maddox . (2014)] – The Database Of Notifications, Knowledge,
Information (DONKI) is an online repository of space weather information
generated by the Community Coordinated Modeling Center (CCMC) at NASA Goddard
Space Flight Center. While the CCMC originally generated the DONKI content,
after 2020 the CCMC only hosts the repository, and the content is generated by
forecasters at the Moon to Mars Space Weather Analysis Office. Of interest to
LLAMACoRE, DONKI contains reconstructions (largely performed using the
StereoCAT and SWPC-CAT tools) for CMEs observed by the coronagraphs from both
STEREO satellites and LASCO. As the content is generated from continuous,
daily forecasts, this is the most complete catalog for the time span it
covers.

  6. 6.

Gopalswamy [Gopalswamy . (2014)] – This work reconstructs CMEs associated with
energetic particle events. The GCS technique is applied to simultaneous STEREO
and SOHO coronagraph images. We note that Gop14cat split their catalog into
smaller lists based on particle association and source location. Three of
their events appear multiple times within the smaller lists and we only
include these events once. For two of the three, the reconstructed CME
properties are the same across lists. The 2013-05-15T01:25 CME is listed once
with the coronal position matching its source region and once with a deflected
position. We assume the deflected position within LLAMACoRe.

  7. 7.

Gui [Gui . (2011)] – This work analyzes the coronal deflections of a small set
of CMEs. They use the GCS technique to reconstruct each CME’s position and
orientation at multiple coronal distances. For LLAMACoRe, we only include the
outermost reconstructions, which should incorporate most, if not all, of the
CME’s nonradial motion.

  8. 8.

Isavnin [Isavnin . (2013)] – This work compares the coronal reconstructions of
CMEs with their interplanetary orientations, as inferred from Grad–Shafranov
reconstructions. The CMEs are reconstructed in the corona using the GCS
technique with STEREO and LASCO images.

  9. 9.

Jang [Jang . (2016)] – This work uses the StereoCAT technique with STEREO
images to reconstruct front-sided (from Earth’s perspective) halo CMEs that
occurred between 2009 and 2013. A major focus of the project was comparing the
direction and speed calculated with 3D techniques as opposed to previous 2D
techniques. We only include the 3D parameters in LLAMACoRe as they should be
more accurate, in general.

  10. 10.

Kay [Kay Gopalswamy (2017)] – This work includes reconstructions for 45 Earth-
directed STEREO-era CMEs and the corresponding expected in situ profiles. The
CMEs were reconstructed from STEREO images using the GCS technique.

  11. 11.

KinCAT [Pluta . (2019)] – The Coronal Mass Ejection Kinematic Database
Catalogue (KinCAT) is a part of the Heliospheric Cataloguing, Analysis and
Techniques Service [<]HELCATS,¿HELCATS. HELCATS largely focuses on CMEs
observed in Heliospheric Imagers, but the KinCAT portion helps connect it with
the coronal reconstruction of CMEs. The reconstructed positions are mostly the
exact same as the AFFECTS catalog, but it includes a few additional cases, as
well as CME mass and velocity. The majority of the cases from Plu19 are
incorporated into our AFFKin catalog, we only label events as KinCAT if they
do not also appear in the AFFECTS catalog.

  12. 12.

Liewer [Liewer . (2011)] – This work compares new reconstructions made with
the tie-pointing and triangulation to previously made GCS reconstructions. The
work determines the reliability of a forecasted trajectory given the relative
locations of the CME and observing satellites. We only include the new
reconstructions from Lie11 as the GCS reconstructions were originally from
The09 and are already a part of the Rodriguez catalog.

  13. 13.

Majumdar [Majumdar . (2020)] – This work analyzes the coronal expansion and
propagation of a set of CMEs. The GCS technique is applied to STEREO images to
reconstruct 3D kinematic profiles.

  14. 14.

Martinic [Martinić . (2022)] – This work reconstructs isolated events to
compare with the in situ orientation. Events are reconstructed with the GCS
technique using STEREO and SOHO coronagraph images. They also reconstruct the
orientation of the CME by aligning an ellipse to just a SOHO image, but do not
include latitude/longitude measurements with these orientations so we do not
include them in LLAMACoRe. Their last event is from 2016 so it is also not
included in LLAMACoRe at this time.

  15. 15.

Rodriguez [Rodriguez . (2011)] – This work uses coronal reconstructions to
test whether one can predict the occurrence of corresponding in situ arrival.
The CMEs are reconstructed from STEREO images using the GCS technique. We note
that the first 26 of the 34 CMEs within this catalog have reconstructions that
were originally published in The09.

  16. 16.

Sachdeva [Sachdeva . (2017)] – This work performs an analysis of the drag and
Lorentz forces influencing the propagation of a CME. The GCS technique is used
with SOHO/LASCO and STEREO images to reconstruct the CME trajectory for
comparison with a force-based model.

  17. 17.

Shen [Shen . (2014)] – This work reconstructs a series of front-sided CMEs in
order to determine whether each event is likely to impact the Earth or not.
The GCS technique is applied to reconstruct the CMEs in the STEREO/COR2 field
of view.

  18. 18.

Shi [Shi . (2015)] – This work uses CME reconstructions to initiate a drag-
based model and determine the expected transit time for each event. Events
with unambiguous shock fronts are reconstructed by fitting the GCS model to
STEREO and SOHO observations.

  19. 19.

Temmer09 [Temmer . (2009)] – A geometric triangulation technique is used to
reconstruct CMEs from STEREO and SOHO images. They create profiles of the
velocity through the corona as seen from each viewpoint, then use them to
infer the source location. We take the average of the position reported for
the STEREO-A and STEREO-B views and use their deprojected velocity.

  20. 20.

Temmer21 [Temmer . (2021)] – This catalog combines STEREO GCS reconstructions
from several other catalogs, in addition to providing several new
measurements. It includes reconstructions from both the AFFECTS and Majumdar
catalogs, so we remove any overlapping cases to make sure the results are not
duplicated within LLAMACoRe.

  21. 21.

Wood [Wood . (2017)] – This work connects coronal CME observations with in
situ observations of magnetic clouds. The CMEs are reconstructed from STEREO
coronagraph imagery using the technique from Woo09, which is conceptually
similar to visually aligning the GCS shape, but employs a different set of
equations defining the shape.

  22. 22.

Zhong [Zhong . (2021)] – This work uses the GCS technique to reconstruct
Earth-impacting CMEs from the corresponding STEREO coronagraph observations.
Their fits include several reconstructions from the Shen catalog, which we do
not include as duplicates within LLAMACoRe.

  23. 23.

ZhuangGCS [Zhuang . (2017)] – This work uses coronal reconstructions of CMEs
as an input to the Integrated CME-Arrival forecasting (iCAF) system. There are
both GCS and ICC reconstructions, which we separate into two different
LLAMACoRE catalogs for comparison. This is the GCS component of the Zhuang
catalog.

  24. 24.

ZhuangICC [Zhuang . (2017)] – This is the ICC component of the Zhuang catalog.

Table 1: List of the individual catalogs within LLAMACoRe | Name | Reference | Time Range | nEvents | nMulti | Parameters
---|---|---|---|---|---|---
1 | AFFECTScat |  Bos12 | May 2007 – Dec 2011 | 196 | 196 | Lat, Lon, AW, v
2 | AFFECTSgcs |  Bos12 | Jan 2007 – Dec 2011 | 139 | 120 | Lat, Lon, Tilt, AW, k
3 | AFFKIN | combines 2 and 10 | May 2007 – Dec 2011 | 102 | 97 | Lat, Lon, Tilt, AW, k, v, M
4 | Braga |  Bra17 | Dec 2008 – Nov 2011 | 17 | 17 | Lat, Lon, v
5 | DONKI |  DONKI | Apr 2010 – Dec 2014 | 1611 | 380 | Lat, Lon, AW, v
6 | Gopalswamy |  Gop14cat | Feb 2010 – Jan 2014 | 74 | 65 | Lat, Lon, v
7 | Gui |  Gui11 | Nov 2007 – Dec 2008 | 10 | 10 | Lat, Lon, Tilt, AW, k, v
8 | Isavnin |  Isa13 | Jun 2008 – Dec 2010 | 15 | 11 | Lat, Lon, Tilt
9 | Jang |  Jan16 | Oct 2009 – Dec 2013 | 306 | 282 | Lat, Lon, AW, v
10 | Kay |  Kay1745 | Nov 2007 – Jun 2014 | 45 | 44 | Lat, Lon, Tilt, AW, k, v, M
11 | KinCAT |  Plu19 | Dec 2007 – Oct 2013 | 20 | 20 | Lat, Lon, Tilt, AW, k, v, M
12 | Liewer |  Lie11 | Aug 2007 – Apr 2008 | 7 | 6 | Lat, Lon, v
13 | Majumdar |  Maj20 | May 2007 – Apr 2014 | 59 | 45 | Lat, Lon, Tilt, AW, k, v
14 | Martinic |  Mar22 | Dec 2008 – Aug 2014 | 21 | 19 | Lat, Lon, Tilt, AW, k
15 | Rodriguez |  Rod11 | Nov 2007 – Dec 2008 | 34 | 23 | Lat, Lon
16 | Sachdeva |  Sac17 | Mar 2010 – Dec 2013 | 38 | 37 | Lat, Lon, Tilt, AW, k, v
17 | Shen |  She14b | Dec 2009 – May 2012 | 39 | 39 | Lat, Lon, AW, v
18 | Shi |  Shi15 | Dec 2008 – Oct 2012 | 21 | 21 | Lat, Lon, AW, v
19 | Temmer09 |  Tem09 | May 2007 – Mar 2008 | 10 | 9 | Lat, Lon, Tilt, AW, k, v, M
20 | Temmer21 |  Tem21b | Apr 2010 – Aug 2014 | 22 | 22 | Lat, Lon, Tilt, AW, k, v, M
21 | Wood |  Woo17 | Dec 2008 – Jul 2012 | 28 | 18 | Lat, Lon, Tilt, AW, v
22 | Zhong |  Zho21 | Jul 2008 – Dec 2012 | 71 | 52 | Lat, Lon, Tilt, AW, v
23 | ZhuangGCS |  Zhu17 | Feb 2010 – Apr 2012 | 31 | 31 | Lat, Lon, AW, v
24 | ZhuangICC |  Zhu17 | Feb 2010 – Apr 2012 | 38 | 38 | Lat, Lon, AW, v

We note that LLAMACoRE presents the CME longitude exclusively in Stonyhurst
coordinates. The source catalogs use a mix of Stonyhurst and Carrington
coordinates for the reconstructions. For those in Carrington coordinates, we
determine the corresponding Carrington longitude of Earth at the given time of
the reconstruction and use this to convert the CME longitude into Stonyhurst
coordinates. In other words, all LLAMACoRe positions are reported within a
coordinate system such that 0∘ latitude and longitude corresponds to an Earth-
directed CME.

###  2.2 Connecting Reconstructions to Single Events

We start assembling LLAMACoRe by defining a master list of all CMEs that
occurred between 2007–2014. AFFECTS and DONKI are by far the largest catalogs
so we first combine their CMEs into a list of unique CMEs. Each CME is
identified by a unique timestamp corresponding to some reconstruction time
during its coronal propagation, taken from one of the source catalogs. There
is only a small window of overlap between these two catalogs, between April
2010 and December 2011, so we use all the AFFECTS events (and their associated
times) before April 2010 and all the DONKI events after December 2011

We must stitch together the two catalogs in the overlapping time range. We
start by adding the full list of all AFFECTS CMEs over this time to the master
list. We begin with AFFECTS as the DONKI catalog appears to be less thorough
during the earliest days of their routine measurements. We take each DONKI
event between April 2010 and December 2011 and determine if there is a close
time match in the AFFECTS catalog. For any single event, the reconstruction
time is probably not the exact same between different catalogs, potentially
varying by a few hours depending on which images were used. If the distance of
reconstruction is given for both we can check if the time differences make
sense (i.e. the farther reconstruction should correspond to a later time).

We then compare the reconstructed geometry to see if we can validate the
plausible match. We primarily compare the latitude and longitude as we have
those parameters for every reconstruction. We expect there to be some
uncertainty in these values, but we check whether the reconstructions fall in
the same general area. If the positions fall in completely different
hemispheres it is fairly unlikely, though not completely impossible, that they
are reconstructions of the same event. If the times and positions seem
reasonable then we associate the DONKI reconstruction with the timestamp ID of
the corresponding AFFECTS event. For the majority of reconstructions, this is
a clear decision to associate or not, but there are a few events where the
decision is somewhat subjective. In general, we tend to make the conservative
decision and not associate events unless we are certain.

We take the master list of times and add in any DONKI events that were not
already included during the overlapping time period. We then repeat the
process for each other catalog, associating each event with a corresponding
existing case or adding the CME if we cannot find a suitable match. At the end
of the process, we have a master list with a unique timestamp ID for every CME
and every reconstruction in every catalog is associated with one of these IDs.
We initiated our master list with the full list of all AFFECTS CMEs, which
contains the times of all CMEs over its range, not just those with AFFECTS GCS
or CAT reconstructions. We now remove any of those times that do not have a
corresponding reconstruction, either from AFFECTS itself or some other
catalog, so that our list only contains entries with reconstructions. This
leaves us with a master list of 1862 CMEs with at least one reconstruction and
a set of 2954 reconstructions across all the catalogs. We find that for 511
CMEs we have more than one reconstruction, which we will refer to as “multi-
cat” events. The full list of all reconstructions for all CMEs is included in
the supplementary material of this manuscript. This corresponds to the version
of LLAMACoRe used in this work, updates will be made to the online version as
additional reconstructions become available. We also include a text file with
one best set of parameters per CME, as well as their uncertainties (when
available). The details of deriving these best parameters are presented in
Section 3 and the corresponding uncertainties in Section 4.

Figure 1 shows the spread of the events within each catalog. The individual
catalogs are listed vertically and the horizontal axis shows time. Each dot
represents the time of the individual reconstructions within a catalog. The
maroon dots correspond to CMEs with only a single reconstructions and the blue
dots are the multi-cat events. This figure shows the range of catalogs
included within LLAMACoRE, which varies from large catalogs covering nearly
all events over a long time frame to smaller catalogs with only a few case
studies.

Figure 1: Temporal span of the individual catalogs used within LLAMACoRe. Each
dot represents a reconstructed CME, the maroon points correspond to events
that only appear in a single catalog whereas the blue dots are multi-catalog
events.

The supporting information includes a table with the number of overlapping
cases between each pair of catalogs. For each catalog, it also shows the total
number of reconstructions (nRecon) and the number of reconstructions that have
no overlap with any other catalog. AFFECTS and DONKI are the most extensive
catalogs but have minimal temporal overlap between the two, so we expect them
to have a high number of no-overlap cases. In the other catalogs, we double-
check the no-overlap events to ensure we have not missed any plausible
connections. This corresponds to a total of 98 no-overlap events. All of these
do not readily match any other entry with the closest event to each having
either a difference of $>$12 hours in time and/or a completely different
spatial location. In some cases, the time will be “close” to a multi-cat time
in the master list, but that catalog has an additional entry that is a better
fit both time-wise and geometry-wise. In other cases, there are multiple
reconstructions at the same time with differing geometries, and coronagraph
observations confirm there are simultaneously multiple distinct CMEs.

One point we want to acknowledge is that, in addition to the uncertainty in
human interpretation of reconstructions, we are likely subject to human errors
such as typographical errors when transcribing lists. In developing LLAMACoRE
we have attempted to directly copy and paste as much as possible, rather than
transcribing by hand, to avoid introducing any additional errors. We suspect
that the source catalogs contain some transcription errors, such as
adding/skipping a number or forgetting a negative sign, as we have found
questionable spread within some events. We first use a Zhuang result as an
example, not implying that the Zhaung catalog is any more or less accurate
than any other, but it is easy to find examples since they provide both GCS
and ICC reconstructions that they themselves have associated with the same
event. For example, the 2011-06-21T03:08:15 CME has a GCS longitude of
$-20^{\circ}$ but an ICC longitude $80^{\circ}$. We expect there to be some
variation between different techniques, but these are essentially
perpendicular directions. As another example, the 2012-03-12T02:42:00 CME has
reconstructed latitudes of $35^{\circ}$, $-37^{\circ}$, and $-53^{\circ}$ in
the DONKI, Jang, and Majumdar catalogs, which is the largest spread in
latitude for a single event within LLAMACoRe. We have confirmed that DONKI
lists the latitude as positive, but recognize that if they happened to have
forgotten a negative sign it would make much better agreement between the
different reconstructions. In any similar situations, we have verified that we
are correctly reproducing the published results from each catalog, but we
cannot confirm whether the large spreads are real features of using different
reconstruction techniques or less scientifically relevant human errors. We
note that, at some level, all the results presented here are a combination of
these errors. We also point out that such errors could explain some of the no-
overlap events. We will revise LLAMACoRe if we receive confirmation of human
errors that should be corrected.

##  3 General CME Properties

After collecting all the events for the meta-catalog, we present an analysis
of the reconstructions within this first version of LLAMACoRE. We show a
summary of the general CME properties within the set in this section and an
analysis of the uncertainty and spread in the reconstructed CME properties
using the multi-cat events in Section 4.

###  3.1 Distribution of Properties

Since we have multiple reconstructions of many of the CMEs, we can combine
them into what is hopefully the most accurate reconstruction of a CME’s
properties. We have no relative judgment of the individual catalogs or reasons
to favor one over the other. Assuming all catalogs are of equal quality, then
combining them may average out some of the uncertainties due to the subjective
nature of CME reconstructions. For each CME, we determine the median value of
the reconstructed properties using whatever information is available from the
combined catalogs. For the single-cat cases, this is the single
reconstruction. For the multi-cat cases, this is the median for each
parameter, and a different number of catalogs may be available for different
parameters. For example, all catalogs include latitude and longitude, but very
few have masses, so the median properties may include multi-cat measurements
of the position but a single-cat measurement of the mass. We note that
circular medians were used for the longitude and tilt. We use the median
instead of the mean as it is less sensitive to outliers. We will refer to this
set of median properties as the “best-constrained” values throughout this
work.

Figure 2 shows histograms of the best-constrained properties of the LLAMACoRe
CMEs. These histograms include each CME once, rather than simultaneously
showing all of the reconstructions available for every CME and weighting the
distributions by the popular CMEs. If the best-constrained values are the most
accurate characterization of CMEs between 2007 and 2014 then it is useful to
explore the distribution of observed CME properties. We use the same color
scheme as in Figure 1 with maroon representing the full sample and blue
representing only the multi-cat cases. The top row shows the number of
reconstructions for each CME (nRecon), the latitude (lat), longitude (lon),
and tilt from left to right. The bottom row shows the angular width (AW), GCS
aspect ratio ($\kappa$), velocity (v), and mass (M). Note the difference in
scale on the y-axis as there are very differing numbers of CMEs for each
parameter.

Figure 2: Histograms of the best-constrained CME properties. The maroon
histograms show the full catalog, including single-source cases, whereas the
blue only shows the median values for the multi-cat cases.

The top left panel shows the large number of single-cat cases compared to
multi-cat cases, largely due to the continuous record of nearly all events in
DONKI starting in April 2010. We note a slight difference between the two
populations depicted in the histograms. This is likely due to a bias toward
which events tend to be more popular and have multiple reconstructions versus
those that receive less attention and have been reconstructed only once. This
bias is clearly reflected in the histograms. For example, the multiple
reconstruction events tend to have smaller absolute longitudes and, to a
lesser extent, smaller absolute latitudes. We can also see that the multi-cat
events are disproportionately missing more of the smaller AW and slower CMEs.
These trends make sense as the smaller catalogs tend to be biased towards
Earth-directed and larger events that are easier to distinguish from the
background corona when performing the reconstruction.

Table 2 shows statistics for the reconstructed CME properties, providing
estimates of the most probable CME values and their ranges. We include
measures for both the full set of reconstructions (labeled “All”) and the
multi-cat set (labeled “Multi”). We show the number of events (n), the median
(Med) of the best-constrained values, and the interquartile range (IQR). The
IQR is the range between the 25th and 75th percentiles and shows the spread of
the middle half of the distribution. For the longitude and tilt, which are
circular distributions, we sort the values and then shift them one by one
across the circular boundary (e.g. moving a longitude of $-170^{\circ}$ to
$190^{\circ}$) to determine where the distribution extends over the shortest
range. We then determine the median and IQR of this distribution.

Table 2: Number of events, medians (Meds) and interquartile range (IQR) of the full set of CMEs and the multi-cat CMEs. Property | nAll | MedAll | IQRAll | nMulti | MedMulti | IQRMulti
---|---|---|---|---|---|---
Lat (∘) | 1862 | 2.8 | 44.6 | 511 | 2.6 | 38.5
Lon (∘) | 1862 | -5.0 | 161.0 | 511 | 1.6 | 112.7
Tilt (∘) | 394 | 0 | 55.0 | 320 | -0.5 | 58.2
AW (∘) | 1833 | 29.0 | 17.0 | 508 | 33.3 | 18.3
$\kappa$ | 341 | 0.34 | 0.17 | 300 | 0.35 | 0.18
v (km/s) | 1822 | 509 | 345 | 507 | 619 | 428
M (1015 g) | 157 | 4.6 | 5.2 | 151 | 4.6 | 5.6

These numbers confirm the slight bias in the multi-cat distribution, as the
median speed and AW are slightly larger than those of the full distribution,
highlighting the trend for more people to fit more energetic or dynamic CMEs.
If we want to analyze how “Earth-directed” the longitude is, it is best to
look at the median value of the absolute longitude (not shown in the Table),
rather than the median of the circular range (shown in the Table). For the
median absolute longitude, we find $57.0^{\circ}$ for the multi-cat events as
opposed to $80.5^{\circ}$ for the full set, hinting more strongly at the
popularity of Earth-directed events in the multi-cat set.

In general, the median properties of the full set are not the most useful as
there is a wide variance in them throughout the solar cycle, as evidenced by
the IQR for both the full set and the multi-cat set. We do find a median CME
velocity of 509 km/s, which is slightly higher than the generally quoted
${\sim}400$–450 km/s [<]e.g.,¿Com17, Yur05, but again this is likely simply a
bias of which CMEs tend to be more suitable for 3D reconstructions.
Reconstructions are more likely to be performed for brighter CMEs that are
easy to identify in coronagraph images, which tend to be in turn more
energetic—the CME kinetic energy depending on mass and speed [<]e.g.,¿Vou10.
In fact, the median CME velocity for the multi-cat cases is 619 km/s, proving
again that case studies tend to focus on faster and generally more energetic
CMEs.

###  3.2 Temporal Variations

To investigate the solar cycle dependence we look at the spread in CME
properties as a function of time. Again, we are still considering the
properties of individual CMEs, whether that is a single reconstruction or the
median for a multi-cat case. Figure 3 shows a heat map where we have
translated both time and CME properties onto a uniform grid and tallied the
number of events that fall within each grid cell. Darker cells represent more
counts and white indicates no instance of that property value at that time. To
facilitate comparison to the solar cycle, the top row of Fig. 3 shows the
smoothed sunspot number (SSN) using data retrieved from sidc.

Figure 3: Heat map showing the range of reconstruction CME properties as a
function of time. The grid cell color indicates the number of events with that
given property value at a given time. The top row shows the smoothed sunspot
number.

We see a general increase in the number of reconstructed CMEs over our
considered time range, which is a combination of several factors. First, there
is the solar cycle dependence with a greater number of events occurring as the
Sun approaches solar maximum, which occurred in April 2014 for Solar Cycle 24.
Second, there is the feasibility of performing multi-viewpoint
reconstructions. At the launch of STEREO, there was minimal angular separation
between the two satellites. They began slowly separating, at some point
reaching an optimal separation for stereoscopic reconstruction of Earth-
directed CMEs, then continuing onward. By 2011 they were located around
$\pm$90∘ from Earth and by the end of 2014 they reached the far side of the
Sun and communication was lost with STEREO-B, making stereoscopic
reconstructions more difficult.

We see some trend in the reconstructed latitude as a function of the solar
cycle. Toward solar minimum, the CMEs tend to be located closer to the
equator, but they are spread over a larger range of latitudes as activity
increases. This results as a combination of the source regions of CME and
their coronal deflection [<]e.g.,¿Cre04, Gop09. The most energetic CMEs tend
to erupt from active regions, which, at the start of the rise phase of a new
solar cycle (about 2010 here), tend to first appear toward higher latitudes,
then tend toward emerging at lower latitudes as the cycle progresses.
Deflection, however, tends to cause CMEs to move away from coronal holes and
toward the heliospheric current sheet (HCS), following the gradients in the
background magnetic energy [<]e.g.,¿Cre06, Kay15. At solar minimum, the HCS
has low inclination, and we see more CMEs near the equator than at high
latitudes. As the solar cycle progresses, the active regions move to lower
latitudes but the HCS becomes more inclined, and we see a wider range of
reconstructed latitudes. We see low latitude reconstructions throughout the
end of Solar Cycle 23, the minimum between cycles, and still in much of the
rise phase of Solar Cycle 24. As maximum approaches, the reconstructions
exhibit a wider range of latitudes.

We do not note any strong trends in the longitude as a function of time but
one would not expect there to be a preferred longitude for CMEs. We also
cannot identify any trend in tilt, $\kappa$, or $M$ over time, but this is
strongly affected by the limited number of events with reconstructions of
these parameters. We see larger speeds and AWs as solar maximum approaches.
There is a trend toward slightly higher values for the highest density grid
cells (the core of the distribution), but we also encounter a larger number of
outlier events with “extreme” CME speeds as solar activity increases. Overall,
we note nothing fundamentally new in these results but we can confirm that the
combined LLAMACoRe catalog reproduces the generally expected behavior of CMEs
[<]e.g.¿Gop20, Lam19, Yas04.

###  3.3 Correlation Between Parameters

Our final analysis of the best-constrained properties, before looking at the
scatter within the multi-cat cases, is looking for any relations between
reconstructed CME properties. The literature contains many examples attempting
to relate parameters such as angular width or mass to velocity [<]e.g.,¿Plu19,
Vrs05, Vrs08, Vou02, Wel18. Such relations, especially those relating a
parameter that is difficult to measure with one that is not (such as relating
mass to velocity) could be extremely useful for any space weather predictions
that require forward-modeling a CME starting in the outer corona.

Figure 4: Correlations between some of the best-constrained properties for the
multi-cat cases. The panels are colored by the Pearson r-value and the black
dashed line shows the linear regression between those two properties.

Figure 4 shows correlations between AW, $\kappa$, v, and M for the multi-cat
observations. We use the multi-cat median values, and only specifically the
values with multiple reconstructions, as we hope they are more accurate than
the individual reconstructions, which should lead to stronger correlations.
For example, while there are 151 events in the multi-cat sample with a
measured mass, which are considered multi-cat because multiple catalogs
include at least their latitude and longitude, only 25 of those had the mass
reconstructed in two or more sources. We have multiple reconstructions for the
AW for 485 events, of $\kappa$ for 67 events, and of v for 507 events. The
figure contains a panel with a scatter plot for each pair of parameters. The
color of the dots indicates the Pearson correlation coefficient (r-value) for
that pair of parameters. The dashed black line shows the linear regression for
each set.

In general, a higher correlation coefficient represents a greater likelihood
of a relation between the two parameters, but the value that is “significant”
depends on the size of the samples being compared. The correlation coefficient
and sample size can be used to compute the probability that the correlation
could accidentally appear from random uncorrelated distributions. Using a
somewhat arbitrary cutoff of a less than 1% chance of being an artificial
correlation, we find significant correlations between all pairs except for
$\kappa$ and M. We list the linear regressions for the significant
correlations below.

$\displaystyle\mathrm{AW}$ $\displaystyle=$ $\displaystyle
0.0176\;\mathrm{v}+23.8$ (1) $\displaystyle\kappa$ $\displaystyle=$
$\displaystyle 0.000200\;\mathrm{v}+0.226$ (2) $\displaystyle\kappa$
$\displaystyle=$ $\displaystyle 0.00495\;\mathrm{AW}+0.210$ (3)
$\displaystyle\mathrm{M}$ $\displaystyle=$ $\displaystyle
0.010\;\mathrm{v}+0.016$ (4) $\displaystyle\mathrm{M}$ $\displaystyle=$
$\displaystyle 0.284\;\mathrm{AW}-3.76$ (5)

where v is always in km/s, AW in ∘, and M is in 1015 g. Note that we have
inverted the equation relating AW and $\kappa$ from the figure to give what we
believe is the more useful form. These regressions are certainly biased based
on the multi-cat cases tending to be larger, faster cases. They imply an AW of
22.6∘ and an M of 1.6$\times$1014 g for a zero-velocity CME. There are plenty
of observations of CMEs smaller than the AW limit, so we suggest caution in
blindly applying these relations but we provide them as a useful
characterization given the reconstructions we currently have.

We do not include the latitude, longitude, and tilt in the pairings shown in
Figure 4. To first order, we do not expect these parameters to scale with the
CME intensity (e.g., if CMEs from the west tended to be faster). One could
make an argument, however, that solar cycle variations might simultaneously
affect the intensity and the latitude and tilt through the variations in the
source region, deflection, and rotation. We checked for such variations but
found no significant correlations. We also note that we have repeated this
analysis using the full set of CMEs but the correlations are much weaker than
what we find for using exclusively the multi-cat events. We propose that the
multi-cat regressions are the optimal versions to use, but have not tested
anything against additional observed events.

##  4 Variation Within Multi-Cat Events

We now analyze the scatter between the reconstructions within each multi-cat
event. We have already determined the median properties for each case so now
we will determine the spread of the reconstructions about those median values.
In an ideal scenario, we would determine the quartile values, but the vast
majority of our cases only have two or three reconstructions, so we cannot
accurately infer that level of detail. Instead, we determine the median
absolute deviation (MAD). We determine the absolute difference between each
reconstruction and the median value, then take the median of these
differences, which is the MAD. This metric is the most appropriate given our
sample sizes and the potential for outlier reconstructions. The uncertainties
in the supplementary table of the best CME parameters correspond to the MAD.
We then take the median value of the set of MADs for all the full multi-cat
CMEs, which we refer to as the medMAD. We also determine the IQR of this set
of MADs (IQRMAD) and the maximum value (maxMAD). Table 3 lists these values
for each property, along with the number of events with multiple
reconstructions of that property, nMAD.

Table 3: Statistics for the spread within the multi-cat cases. We show the
number of events with multiple reconstructions of that property, the median
MAD over all of the CMEs, the IQR, and the maximum MAD within our sample. We
also show the expected deviation between multiple reconstructions of the same
event, both in physical units and as a percentage (where appropriate). We also
include the mean absolute error in the reconstructed values of synthetic CMEs
from Ver23, the average error in the reconstructed values of real CMEs from
Plu19, and the uncertainties derived from the sensitivity analysis of the GCS
model by The09

.  Property nMAD medMAD IQRMAD maxMAD $\epsilon$ $\epsilon$ (%) V23 P19 T09
Lat (∘) 511 2.0 2.6 21.2 4.0 — 1.5 5 1.8 Lon (∘) 511 4.0 5.8 50.1 8.0 — 3.4 5
4.3 Tilt (∘) 85 12.0 15.7 48.0 24.0 — 2.6 30 22 AW (∘) 485 4.7 5.8 23.0 9.3 27
10.6 10 10 $\kappa$ 67 0.05 0.06 0.28 0.1 29 0.05 0.025 0.06 v (km/s) 448 58
95 837 115 19 — — — M (1015 g) 25 1.3 2.8 6.5 2.5 38 — — —

These values should be the best-to-date characterization of the uncertainty in
CME reconstructions, but the precise statistical definitions are slightly
complicated for several reasons. First, we do not have “true” values so we can
calculate uncertainties but not actual errors. Even calculating uncertainties
requires a reference value from which we can calculate the deviation for each
reconstruction. We use the median value as the reference point, which helps
account for outliers, but makes mathematical interpretation of the uncertainty
difficult as the median is not determined from common formulae such as a sum.
If we consider a simple example with just two reconstructions, the median
value will be halfway in between the two reconstructed values. Each
reconstruction will have the same absolute deviation from the median value, so
the MAD is the same as the individual deviations in this two-reconstruction
example. The difference between the two reconstructions is twice the MAD.

A similar, general analysis cannot be performed for a generic set of three or
more events. The absolute distances between the median and individual
reconstructions will be grouped around the MAD, but we cannot say the exact
distance between individual reconstructions as it depends on their exact
distribution. We suggest that the “typical” deviation between two
reconstructions of the same event is roughly twice the MAD, but this is an
order of magnitude estimate and not a precise calculation. We call this
expected deviation $\epsilon$ and propose it is the best estimate of the
uncertainty between reconstructed values. In other words, if we have an
existing reconstruction and a second, independent reconstruction is performed,
we expect the new values to differ by about $\epsilon$. Again this says
nothing about the relation of either reconstruction to the “true” CME
properties, just their relationship to one another. Table 3 lists $\epsilon$
for each reconstructed property, both in physical units and as a percentage.
The percentages were calculated using the median properties of each event,
then reduced to a single value by taking the median of the individual
percentages over all of the events.

We compare our uncertainties with the errors found in Ver23, Plu19, and The09,
which we will refer to as V23, P19, and T09, respectively. V23 determined the
errors in GCS reconstructions using synthetic white-light events, which have
known values and for which a perfect reconstruction is possible. This yields
true errors for the V23 statistics, rather than just uncertainties. The final
column of Table 3 lists the mean absolute error (MAE) from V23. We note that
we use their values corresponding to all satellite configurations. If we treat
the LLAMACoRe medians as true values our medMAD numbers are analogous to the
V23 MAE, though there is some subtlety in using a median within LLAMACoRe and
a mean within V23. P19 estimated uncertainties in GCS reconstructions based on
two independent fits of 15 sample real events, differing from V23 in that the
“true” CME parameters, in this case, are not known. T09 developed an automated
fitting procedure for the GCS model and determined the magnitude of variation
that produced a 10% decrease in their merit function. While this is not the
exact same as the variation between visual fits between different observers,
it is an interesting comparison. For the T09 errors we show the mean values
and for AW and $\kappa$ we show the average of their positive and negative
values. We note that all these numbers tend to display similar magnitudes to
what is seen in LLAMACoRe with the exception of the tilt, which is
significantly lower in V23. This is not surprising, since the axis orientation
of a CME is more straightforward to determine for a synthetic, croissant-like
structure than for a real, more irregular event.

We see nearly identical metrics for the latitude, longitude, and $\kappa$
between LLAMACoRe and V23. This supports using these values as accurate
measures of the uncertainty and potentially the error from the true value. It
also builds confidence in our ability to reconstruct these properties for real
events since the uncertainties are comparable to the best-case scenario of
idealized events. In contrast, we find much larger uncertainty in the tilt but
smaller uncertainty in the AW for LLAMACoRe as compared to V23. This is likely
a result of comparing the GCS shape to non-idealized, real CMEs as opposed to
perfectly symmetrical synthetic CMEs. The difference in the tilt uncertainty
follows logically as the synthetic CMEs tend to look more like a distinct,
isolated tube with an apparent inclination, whereas real CMEs look more like
amorphous blobs that can be hard to distinguish from the background. In terms
of the AW, the full extent of the GCS wireframe is a combination of both kappa
and AW, with the AW setting the angle between the legs and $\kappa$
determining the width of the cross section. V23 found very large ranges in AW
when only a single viewpoint is used, particularly if the event is propagating
along the line-of-sight direction. We suspect that for real events most
observers tend to minimally vary kappa from the default settings and primarily
adjust the AW until the wireframe matches the white-light extent. This is just
one potential interpretation, however. We also note that the LLAMACoRe AWs
include both toroidal GCS-like shapes as well as more symmetric ICC-like
shapes, so it is somewhat surprising to see smaller variations in the
LLAMACoRe AWs than in V23.

###  4.1 Range in Deviations

While it is useful to reduce the results of the entire catalog into a few
instructive numbers describing the uncertainty, this allows for only a surface
understanding and no insights into any trends in uncertainty. To start
unpacking these numbers, Figure 5 shows histograms of the MADs for each CME
within the multi-cat set. The top left panel shows the number of
reconstructions, the same as the top left panel of Figure 2, but now we only
include the multi-cat cases and show the vertical range on a logarithmic
scale. The rest of the panels use a linear y-axis and within them the dashed
light blue line represents the median of the distribution and the dotted blue
lines the 25th and 75th percentiles.

Figure 5: Histograms of the MAD for each property within the multi-cat cases.
The vertical lines show the median of the MADs (solid line) and the 25th and
75th percentiles of the distributions (dotted lines).

All of the parameters have histograms peaking toward low MADs. The latitude,
longitude, and velocity all have sharp peaks at low values with only a few
outliers at slightly higher values. This suggests that for most events the
reconstructions are close to the median value and largely consistent with one
another, and we can feel relatively confident about our ability to reconstruct
these values. However, there are instances where we see fairly large
disagreements within a single event. For example, the 2011-02-26T17:08:15 CME
appears in both AFFECTScat and AFFECTSgcs but no other catalogs. We use it as
an example as the reconstructions were performed by the same AFFECTS team and
they associated these reconstructions with the same event. The reconstructed
longitudes are 146.2∘ and $-139.1^{\circ}$ for the CAT and GCS versions,
respectively, which is a separation of 74.7∘ and a MAD of 37.4∘. This is a
back-sided event, which tends to make reconstruction more difficult. STEREO,
however, was located at roughly $\pm$90∘ from Earth at this time, which should
be favorable for the quality of the reconstruction. We cannot say why these
reconstructions differ so much, though we do note that a missing negative sign
would bring them to much better agreement. Only 7 events in our sample have a
MAD greater than 30∘ in the longitude, suggesting that while these extremely
uncertain cases do exist, they are fairly rare.

The AW and tilt distributions are not as sharply peaked, suggesting that these
parameters are slightly more difficult to measure precisely than the latitude,
longitude, and velocity. We do not see a noticeable separate population of
outliers for either property, but this is largely due to there being a finite
range of plausible or possible values. In theory, the AW can vary between 0∘
and 180∘ since it is a half-width and the maximum value would correspond to a
physically unrealistic complete loop. More often, maximum values tend to be
around 90∘, which would correspond to a large CME covering a full hemisphere.
Looking at the distributions in Figure 2, the LLAMACoRe AW distribution peaks
around 30∘ with only a small fraction of cases above 50∘. Only 24 of the
events have an AW MAD above 15∘ because the most common CMEs are not big
enough to exhibit that large of a spread in the reconstructions. The MADs for
the tilt are similarly limited but by the range of possibilities rather than
the range of commonly observed values. There is only a 180∘ range in tilts,
and it is circular, so the maximum two values can differ by is 90∘. For two
reconstructions for a single event, a difference of 90∘ corresponds to a MAD
of 45∘, which is the maximum value we observe. This means we have cases,
albeit relatively few, where the reconstructed tilts are differing by as much
as physically possible. Most cases are better than this but there are times
when the reconstructed tilt is completely unreliable.

The distribution of $\kappa$ shows a defined peak toward lower MADs but also a
significant secondary population of higher values. We note the small number of
events with multiple reconstructions of $\kappa$, the secondary peak of MADs
greater than 0.2 corresponds to only 5 events. The histogram in Figure 2 shows
that most CMEs are reconstructed with $\kappa$ between 0.2 and 0.5. For these
higher MAD cases, one catalog (always either AFFECTS or KINCAT) uses a
$\kappa$ in the range of 0.6-0.9 for the reconstruction. As with most of the
other parameters, there is typically a reasonable consensus on the
reconstructed $\kappa$, but for a small percentage of events, we find a
drastic disagreement.

The bottom right panel of Figure 5 shows the MAD in the mass. Measuring the
mass is difficult due to a variety of reasons, including assumptions about
where the mass exists relative to the plane of the sky and potential
contamination from background objects such as coronal streamers. For a more
thorough discussion of these issues we refer the reader to Plu19. We have very
few cases with multiple reconstructions of the mass so the distribution is not
at all continuous. From the limited information, it appears that the mass
reconstructions MADs have similar behavior to the AW with a peak at low values
but a relatively broad distribution. This cannot be confirmed, however,
without additional data.

###  4.2 Trends in the MAD Values

We look to see if there are any trends in the MAD with the number of
reconstructions performed for an event. Figure 6 includes each CME once as a
blue dot and shows the MAD versus the number of reconstructions (nRecon) for
that property. We note that there is significant overlap in the dots for the
low numbers of nRecon. For each nRecon, we determine the mean and median of
the MADs with that number of reconstructions, and show these values in yellow
(mean) and pink (median). For the most part, we see little to no dependence of
any MAD on the number of reconstructions, the pink and yellow lines are
essentially horizontal for most parameters.

We tend to see larger outliers in the MAD for the low number of
reconstructions. The fewer the number of events the more significant the
effect a single reconstruction has on the median value and therefore the MAD.
Small sample size effects also explain the slight increase in the overall
mean/median (yellow/pink lines) toward higher nRecon, particularly for the
tilt and the AW. There are only two events that have six reconstructions of
the tilt (2008-12-12T08:22:20 and 2010-04-03T11:08:15) and both of them have a
fairly wide range in their reconstructed values. We propose that these small
sample effects would disappear if we had more events with a large number of
independent reconstructions, but cannot confirm this without additional
measurements. In general, we find no evidence to support any trend in the most
probable MAD versus the number of reconstructions, but the possibility of a
large MAD does increase when there are only a few reconstructions.

Figure 6: MAD of properties for each multi-cat case versus the number of
reconstructions. The yellow shows the median and the pink shows the median
within each set of events with the same number of reconstructions.

We next investigate how the MAD values change over time, seeking any
variations that could be attributed to the solar cycle or satellite
separation. Figure 7 shows a heat map of the MAD over time, analogous to
Figure 3 but for the uncertainties and with the top panel showing the number
of reconstructions per event as opposed to the sunspot number.

Figure 7: Heat map showing the distribution in the MAD of properties as a
function of time.

We see that the most-populated grid cells stay at about the same MAD values
over time. More CMEs occur toward solar maximum, but their most probable
uncertainty is the same as the rest of the solar cycle. Toward solar maximum,
we do see a larger spread in the range of measured MADs with more cases at
high values. This may simply result from the fact that we have a much larger
sample of events at this time, or it may be an inherent property of the solar
maximum events themselves. At solar maximum, we expect there to be a larger
number of very energetic events with larger size and faster speed. If our
uncertainties in reconstructions are generally percentage errors, then we
would expect larger uncertainties for the more energetic events. Additionally,
near maximum, the higher CME occurrence rate may lead to overlapping
signatures in the projected white light events. This will make it difficult to
accurately separate which feature belongs to the CME of interest versus other
events or the background coronal structures. We cannot distinguish between
these effects options with Figure 7 alone, but we can compare the MAD with the
median CME properties for each event to gain some insights.

Figure 8: Correlations between the MAD of properties versus the median value
of properties. Each panel is colored according to the Pearson r-value for that
pair of standard deviation and median values.

Figure 8 shows the scatter of the MADs versus each CME property. Each dot
represents a single event and we use the median values for the CME properties.
With this analysis, we seek any systematic causes leading to higher
uncertainty in the CME reconstructions. Some trends we might expect, such as
larger uncertainty in the speed when the speed is higher, but it could also
unveil some unexpected patterns. Within Figure 8 the individual panels are
colored by the correlation coefficient (Pearson r value) corresponding to that
MAD and CME parameter.

We compare with the absolute value of the latitude and longitude as we expect
there could be a trend with the location relative to the Sun–Earth line but
probably no east–west or north–south asymmetry in the uncertainty. Similarly,
we might expect variation with high or low inclination, but not whether the
tilt is in the clockwise or counterclockwise direction. We have confirmed that
such asymmetries with the sign of the position and orientation do not exist,
but do not include a figure showing such. Looking at Figure 8, we actually see
no significant correlations with the position and orientation at all. This
suggests that, at least while the STEREO probes were separated from Earth, we
should be able to reconstruct the position and orientation of CMEs from all
source locations with equal uncertainty.

We see some moderate correlations for parameters we might expect to scale with
the CME energetics. The MADs for both the velocity and mass increase as the
actual velocity and mass increase. There is significant scatter about the
general trend, leading only to moderate correlations. We only see a moderate
correlation between the AW median and MAD, and interestingly we find a
moderate correlation between $\kappa$ and the MAD for the AW. This may hint at
a difficulty in matching the wireframe reconstruction shape to the
observations when it becomes too fat, but we cannot confirm this at this time.

Figure 8 shows an unexpected moderate trend between the CME mass and the MAD
for the tilt with higher masses being less uncertain. We only have 25 events
in the multi-cat set that have a reconstructed mass so this correlation may
just be an artifact of the small sample size. More massive CMEs should have
more prominent white light signatures, which should make it easier to
reconstruct the CME. This should reduce the uncertainty, but we have no
suggestion as to why it would only improve the uncertainty in the tilt but no
other parameters. We do note that the MADs for latitude, longitude, $\kappa$,
and v tend to be smaller for the subset of the multi-cat set that does have a
reconstructed mass than they are for the full set (comparison of the vertical
extend of the points within the right-most column panels to any other column).
This likely indicates that the cases for which mass reconstructions have been
performed tend to be easier to analyze in general, which is why the mass
reconstruction could be performed and the rest of the uncertainties are
generally low for these events.

Overall, we see very little in terms of correlations between the CME
parameters and the uncertainties beyond a slight suggestion of the velocity
and mass having an uncertainty proportional to their magnitude. We do not show
it but we have also compared the uncertainties to one another and looked for
trends there (e.g., tend to have high uncertainty in tilt when we have high
uncertainty in longitude) but found nothing noteworthy. We cannot find any
systematic variations that could help identify when we expect reconstructions
to be more accurate, it seems to be a nonlinear process combining the effects
of many different factors.

##  5 Discussion

In combining the existing catalogs into LLAMACoRe we hope to create the most
accurate collection of reconstructed CME parameters over the given time span.
We certainly feel it is the most thorough collection, given that it includes
more events than any of the individual catalogs. We have no way of validating
the accuracy of any reconstruction since there is no method to directly
measure the “true” values of CME properties, everything requires a model
making some sort of assumption or approximation. Beyond that, real CMEs are
not the rigid, symmetric, idealized structures we use in reconstructions, but
rather tenuous structures, so using a single number to describe a property,
such as the latitude, may accurately represent the average position but does
away with any of the details needed to more-realistically describe the
structure.

In comparing independent reconstructions of the same event we expect slight
variations depending on how each particular person fitted a CME. Most events
are complicated and it can be difficult to disentangle what portion of the
white-light imagery actually corresponds to the CME of interest versus other
CMEs or background solar structures such as streamers. If two observers pick
slightly different white-light features to fit then we expect there to be a
small amount of variation in the reconstructed parameters. If one observer,
however, accidentally includes an incorrect white light feature, such as a
streamer region, then we expect a much larger variation in the
reconstructions. Additionally, if a CME is propagating fast enough, a CME-
driven shock may be apparent in the white light images. While reconstructing
the shock is also of scientific interest, it is critical to reconstruct it
separately from the CME. Distinguishing between these features can be
difficult, particularly if one has less experience and is just beginning to
perform routine reconstructions.

Visual comparison can show that some reconstructions are clearly better than
others, but there are wide ranges of “good” parameter space where the
reconstruction looks reasonable for the white-light signature and cannot be
further constrained through visual comparison alone. Ideally, all
reconstructions would fall within this good region and by combining multiple
reconstructions we could average out some of the personal preferences within
the individual reconstructions and create a new set of best-constrained CME
properties. We expect that the vast majority of the reconstructions are
reasonable, but that there are probably a few bad cases, either due to
mistakes in the reconstruction process or unintentionally introduced human
errors. We have no true values to allow for a numerical comparison, nor the
time to individually reproduce and compare each of the nearly 3000
reconstructions that are a part of LLAMACoRe. In general, we believe that the
individual catalogs are mostly accurate and the derived LLAMACoRe values
should be of high quality. We will continually update the online version of
LLAMACoRe, updating any reconstructions if errors are identified and adding
new cases as they become available.

While we believe the best-constrained properties to be reasonably accurate, we
note that the tilt is probably the most suspect. It may be worth examining the
individual reconstructions for an event before blindly using our best-
constrained value. In the cases where the individual reconstructions are all
in general agreement, say within 45∘, then our reported median value does
reflect the consensus. However, we know there are cases where the individual
reconstructions span the full range of possible tilts. If half the
reconstructions suggest high inclination and half suggest low inclination, the
best-constrained value will be a mid inclination. Mathematically, this is in
the middle of the individual reconstructions, but the spread for this case is
so large we really cannot declare anything with any certainty. This is really
only an issue for the tilt and not any other reconstructed parameter. The tilt
is known to be difficult to reconstruct, but it is a critical parameter for
making space weather predictions. Forecasting the southward magnetic field is
one of the most important factors in determining the severity of a near-Earth
CME impact. In fact, many works have focused on connecting CME axial
orientations estimated at the Sun and subsequently at 1 au [<]e.g.,¿Mar23,
Pal22sanchita, Pal17, finding often striking discrepancies between the two
tilts [<]e.g.,¿Pal18, Xie21, Yur07. Even more so, it has been proposed that
(at least some) CMEs propagate through interplanetary space as significantly
kinked structures, and the axis orientation encountered at Earth (or any other
point in the heliosphere) only reflects the local conditions of the portion of
the cloud that is sampled by a spacecraft [Bothmer Mrotzek (2017)]. If we do
not know the orientation of a CME, how can we know the orientation of its
internal magnetic field? We suggest that our inability to accurately
reconstruct the orientation of CMEs will be a major limiting factor in space
weather predictions in the coming decades and the community should focus on
addressing this knowledge gap.

We have investigated the presence (or lack thereof) of any trends in the
magnitude of the uncertainties with respect to the satellite locations. We
considered a 2D scatter plot (not shown) with each point colored by the MAD,
with the x-axis showing the absolute longitudinal distance between the CME
direction and the longitude of STEREO-A, and the y-axis showing the same for
STEREO-B. No strong patterns were present. We may see a slight hint of higher
uncertainties in the latitude when the CME is moving directly toward one
satellite and directly away from the other, but this is likely not
statistically significant. Overall, we find that any effects of the relative
viewing angles are overwhelmed by the other factors involved in determining
the uncertainties.

##  6 Conclusion

We have presented LLAMACoRe, a new meta-catalog that combines existing
catalogs into the most extensive collection of 3D CME reconstructions over the
prime STEREO era between 2007–2014. LLAMACoRe contains 2954 reconstructions
for 1862 different CMEs. Each reconstruction includes, at a minimum, the
coronal latitude and longitude of that event. Many reconstructions also
include the tilt, angular width, GCS shape parameter (or aspect ratio),
velocity, and mass of the CME.

LLAMACoRe contains 511 events with multiple reconstructions, for which we
determine the median properties. We use the median properties, in addition to
the single-reconstruction events, and analyze the variation in CME properties
over time. These results reproduce the known solar cycle variations in CME
behavior [<]e.g.,¿Yas04, Lam19, Gop20. We see a higher rate of CME occurrence
toward solar maximum and a larger number of highly energetic events. We derive
new relations between the CME mass, angular width, shape, and velocity, which
could be useful for estimating input values for CME forecasting models.

We compare the range in the reconstructions for each event and use their
variance to better establish the uncertainty in our reconstruction techniques.
We have no measure of the true CME values, so we report the uncertainty as the
typical difference we would expect between two independent reconstructions of
the same event. We find uncertainties of 4.0∘ in the latitude, 8.0∘ in the
longitude, 24.0∘ in the tilt, 9.3∘ in the angular width, 0.1 in the shape
parameter $\kappa$, 115 km/s in the velocity, and 2.5$\times$1015 g in the
mass. Some of these uncertainties can also be expressed as percentages: 27%
for the angular width, 29% for $\kappa$, 19% for the velocity, and 38% for the
mass.

We look for any trends in the uncertainty to see if there are instances in
which the community is particularly good or bad in performing consistent
reconstructions. Unfortunately, we see very little evidence of any useful
relations. We do find more outliers with large uncertainty toward solar
maximum. This likely results from a combination of more energetic CMEs and the
reconstructions being more difficult to perform as the CME occurrence rate
increases and cases begin overlapping in the projected fields of view. The
most probable uncertainties, however, tend to remain around the same values
over the solar cycle.

We have made our best attempt to incorporate any existing catalogs into
LLAMACoRe, but recognize we may have potentially missed some invite any
authors to contact us and we will update the online version. We will keep
watch over the literature and automatically add any results from new
publications with 5 or more reconstructed CMEs. We are happy to include
smaller lists if they are sent directly to the LLAMACoRE team, but it is
beyond our capabilities to automatically keep track of every publication with
a single reconstruction.

We also invite collaboration from the community to help further develop
LLAMACoRe as a living repository for any measurements that have been made for
observed CMEs. We initially limited the focus to coronal geometric/kinematic
reconstructions between 2007–2014 to get the project running, but would like
LLAMACoRe to grow in whatever direction the community sees fit. This includes
expanding the time range and incorporating additional information such as
source regions, associated flare/dimming properties, reconstructions based on
heliospheric imaging, and in situ associations/properties.

We propose that the current version of LLAMACoRe has several uses for the
community. First, the derived uncertainties should be considered when
reporting any CME reconstructions in other works. While our numbers are
similar to previously reported uncertainties, they have been derived from the
largest number of cases thus far. Second, LLAMACoRe is the most thorough
collection of stereoscopic coronal CME reconstructions between 2007–2014. This
makes it an excellent starting point for any event case studies, collecting
the information from the literature into a single location. This will quickly
show whether or not there is consensus on a specific reconstruction. Well-
behaved, consensus events would be the most suitable for initially testing new
models whereas complicated, highly uncertain events could be more interesting
for in-depth observational analyses. When LLAMACoRe grows to include
information about source regions and in situ counterparts the amount of
information readily at hand will become even more useful.

Another factor is that we have developed a set of over 500 events with
relatively well-constrained parameters derived from multiple reconstructions.
We propose that using the best-constrained parameters as inputs could be a
systematic method for benchmarking interplanetary propagation models,
particularly once the CMEs’ in situ counterparts have been added to LLAMACoRe.
The community has also embraced more machine learning and artificial
intelligence approaches over the previous decade [<]e.g.,¿Cam19, but
appropriately applying these techniques requires larger data sets than often
available. The full data set (over 1800 cases), and to a lesser extent the
multi-cat set, begin approaching what is reasonable for some advanced
techniques. With such a large set there are certainly aspects to explore that
the authors have not yet thought of and we invite the community to make use of
LLAMACoRe however they see fit.

##  7 Open Research

The present version of LLAMACoRe and future updates to the meta-catalog can be
accessed at osprei.space/llamacore. The full data set and the best-constrained
properties for each CME can be found in the supplementary information and has
been archived at Zenodo at https://doi.org/10.5281/zenodo.10462800. Smoothed
sunspot data were taken from the World Data Center SILSO, Royal Observatory of
Belgium, Brussels, available at sidc.be/SILSO/datafiles.

###### Acknowledgements.

First and foremost we acknowledge the authors and teams responsible for
compiling the individual catalogs that comprise LLAMACoRe. None of this work
would have been possible without these existing sources. The AFFECTS
STEREO/SECCHI/COR2 CME catalog is generated and maintained at the Institute
for Astrophysics of the University of Goettingen, supported by the German
Space Agency DLR and the European Union in collaboration with the U.S. Naval
Research Laboratory, Washington. We acknowledge the Community Coordinated
Modeling Center (CCMC) at Goddard Space Flight Center for the use of the DONKI
catalog. The HELCATS KINCAT catalog is generated and maintained at the
Institute for Astrophysics of the University of Goettingen, supported by the
German Space Agency DLR and the European Union. The remaining catalogs were
pulled from the online published papers and we acknowledge the work of those
authors and the journals that host the content.
C. Kay is supported by the National Aeronautics and Space Administration under
Grant 80NSSC19K0909 issued through the Heliophysics Early Career Investigators
Program. E. Palmerio acknowledges support from NASA’s Heliophysics Guest
Investigators-Open program (grant no. 80NSSC23K0447).

## References

  * Asvestari . (2021) Asv21Asvestari, E., Pomoell, J., Kilpua, E., Good, S., Chatzistergos, T., Temmer, M.Magdalenic, J.  202108\.  Modelling a multi-spacecraft coronal mass ejection encounter with EUHFORIA Modelling a multi-spacecraft coronal mass ejection encounter with EUHFORIA.  Astronomy & Astrophysics652A27.  10.1051/0004-6361/202140315
  * Ben-Nun . (2023) Ben23Ben-Nun, M., Török, T., Palmerio, E., Downs, C., Titov, VS., Linton, MG.Lionello, R.  202311\.  Deflection of Coronal Mass Ejections in Unipolar Ambient Magnetic Fields Deflection of Coronal Mass Ejections in Unipolar Ambient Magnetic Fields.  The Astrophysical Journal957274.  10.3847/1538-4357/acfe6c
  * Bosman . (2012) Bos12Bosman, E., Bothmer, V., Nisticò, G., Vourlidas, A., Howard, RA. Davies, JA.  201211\.  Three-Dimensional Properties of Coronal Mass Ejections from STEREO/SECCHI Observations Three-Dimensional Properties of Coronal Mass Ejections from STEREO/SECCHI Observations.  Solar Physics2811167-185.  10.1007/s11207-012-0123-5
  * Bothmer Mrotzek (2017) Bot17Bothmer, V. Mrotzek, N.  201711\.  Comparison of CME and ICME Structures Derived from Remote-Sensing and In Situ Observations Comparison of CME and ICME Structures Derived from Remote-Sensing and In Situ Observations.  Solar Physics29211157.  10.1007/s11207-017-1171-7
  * Braga . (2017) Bra17Braga, CR., Dal Lago, A., Echer, E., Stenborg, G. Rodrigues Souza de Mendonça, R.  201706\.  Pseudo-automatic Determination of Coronal Mass Ejections’ Kinematics in 3D Pseudo-automatic Determination of Coronal Mass Ejections’ Kinematics in 3D.  The Astrophysical Journal8422134.  10.3847/1538-4357/aa755f
  * Brueckner . (1995) Bru95Brueckner, GE., Howard, RA., Koomen, MJ., Korendyke, CM., Michels, DJ., Moses, JD.Eyles, CJ.  199512\.  The Large Angle Spectroscopic Coronagraph (LASCO) The Large Angle Spectroscopic Coronagraph (LASCO).  Solar Physics1621-2357-402.  10.1007/BF00733434
  * Camporeale (2019) Cam19Camporeale, E.  201908\.  The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting.  Space Weather1781166-1207.  10.1029/2018SW002061
  * Colaninno Vourlidas (2009) Col09Colaninno, RC. Vourlidas, A.  200906\.  First Determination of the True Mass of Coronal Mass Ejections: A Novel Approach to Using the Two STEREO Viewpoints First Determination of the True Mass of Coronal Mass Ejections: A Novel Approach to Using the Two STEREO Viewpoints.  The Astrophysical Journal698852-858.  10.1088/0004-637X/698/1/852
  * Colaninno Vourlidas (2015) Col15Colaninno, RC. Vourlidas, A.  201512\.  Using Multiple-viewpoint Observations to Determine the Interaction of Three Coronal Mass Ejections Observed on 2012 March 5 Using Multiple-viewpoint Observations to Determine the Interaction of Three Coronal Mass Ejections Observed on 2012 March 5.  The Astrophysical Journal815170.  10.1088/0004-637X/815/1/70
  * Compagnino . (2017) Com17Compagnino, A., Romano, P. Zuccarello, F.  201701\.  A Statistical Study of CME Properties and of the Correlation Between Flares and CMEs over Solar Cycles 23 and 24 A Statistical Study of CME Properties and of the Correlation Between Flares and CMEs over Solar Cycles 23 and 24.  Solar Physics29215.  10.1007/s11207-016-1029-4
  * Cremades Bothmer (2004) Cre04Cremades, H. Bothmer, V.  200407\.  On the three-dimensional configuration of coronal mass ejections On the three-dimensional configuration of coronal mass ejections.  Astronomy & Astrophysics422307-322.  10.1051/0004-6361:20035776
  * Cremades . (2006) Cre06Cremades, H., Bothmer, V. Tripathi, D.  200601\.  Properties of structured coronal mass ejections in solar cycle 23 Properties of structured coronal mass ejections in solar cycle 23.  Advances in Space Research38461-465.  10.1016/j.asr.2005.01.095
  * Domingo . (1995) Dom95Domingo, V., Fleck, B. Poland, AI.  199512\.  The SOHO Mission: an Overview The SOHO Mission: an Overview.  Solar Physics1621-21-37.  10.1007/BF00733425
  * Dumbović . (2018) Dum18Dumbović, M., Čalogović, J., Vršnak, B., Temmer, M., Mays, ML., Veronig, A. Piantschitsch, I.  201802\.  The Drag-based Ensemble Model (DBEM) for Coronal Mass Ejection Propagation The Drag-based Ensemble Model (DBEM) for Coronal Mass Ejection Propagation.  The Astrophysical Journal854180.  10.3847/1538-4357/aaaa66
  * Dumbović, Čalogović . (2021) Dum21Dumbović, M., Čalogović, J., Martinić, K., Vršnak, B., Sudar, D., Temmer, M. Veronig, A.  202105\.  Drag-Based Model (DBM) Tools for Forecast of Coronal Mass Ejection Arrival Time and Speed Drag-Based Model (DBM) Tools for Forecast of Coronal Mass Ejection Arrival Time and Speed.  Frontiers in Astronomy and Space Sciences858.  10.3389/fspas.2021.639986
  * Dumbović, Veronig . (2021) Dum21wdeDumbović, M., Veronig, AM., Podladchikova, T., Thalmann, JK., Chikunova, G., Dissauer, K.Samara, E.  202108\.  2019 International Women’s Day event. Two-step solar flare with multiple eruptive signatures and low Earth impact 2019 International Women’s Day event. Two-step solar flare with multiple eruptive signatures and low Earth impact.  Astronomy & Astrophysics652A159.  10.1051/0004-6361/202140752
  * Feng . (2013) Fen13Feng, L., Inhester, B. Mierla, M.  201301\.  Comparisons of CME Morphological Characteristics Derived from Five 3D Reconstruction Methods Comparisons of CME Morphological Characteristics Derived from Five 3D Reconstruction Methods.  Solar Physics2821221-238.  10.1007/s11207-012-0143-1
  * Gopalswamy . (2020) Gop20Gopalswamy, N., Akiyama, S. Yashiro, S.  202007\.  The State of the Heliosphere Revealed by Limb-halo Coronal Mass Ejections in Solar Cycles 23 and 24 The State of the Heliosphere Revealed by Limb-halo Coronal Mass Ejections in Solar Cycles 23 and 24.  The Astrophysical Journal Letters8971L1.  10.3847/2041-8213/ab9b7b
  * Gopalswamy . (2009) Gop09Gopalswamy, N., Mäkelä, P., Xie, H., Akiyama, S. Yashiro, S.  200903\.  CME interactions with coronal holes and their interplanetary consequences CME interactions with coronal holes and their interplanetary consequences.  Journal of Geophysical Research (Space Physics)114A00A22.  10.1029/2008JA013686
  * Gopalswamy . (2014) Gop14catGopalswamy, N., Xie, H., Akiyama, S., Mäkelä, PA. Yashiro, S.  201412\.  Major solar eruptions and high-energy particle events during solar cycle 24 Major solar eruptions and high-energy particle events during solar cycle 24.  Earth, Planets and Space66104.  10.1186/1880-5981-66-104
  * Goussies . (2010) Gou10Goussies, N., Stenborg, G., Vourlidas, A. Howard, R.  201004\.  Tracking of Coronal White-Light Events by Texture Tracking of Coronal White-Light Events by Texture.  Solar Physics2622481-494.  10.1007/s11207-009-9495-6
  * Gui . (2011) Gui11Gui, B., Shen, C., Wang, Y., Ye, P., Liu, J., Wang, S. Zhao, X.  201107\.  Quantitative Analysis of CME Deflections in the Corona Quantitative Analysis of CME Deflections in the Corona.  Solar Physics271111-139.  10.1007/s11207-011-9791-9
  * Harrison . (2016) HELCATSHarrison, R., Davies, J., Perry, C., Moestl, C., Rouillard, A., Bothmer, V.Gallagher, P.  201604\.  HELCATS - Heliospheric Cataloguing, Analysis and Techniques Service HELCATS - Heliospheric Cataloguing, Analysis and Techniques Service.  EGU General Assembly Conference Abstracts EGU General Assembly Conference Abstracts ( EPSC2016-10220).
  * Howard . (2008) How08Howard, RA., Moses, JD., Vourlidas, A., Newmark, JS., Socker, DG., Plunkett, SP.Carter, T.  200804\.  Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI).  Space Science Reviews1361-467-115.  10.1007/s11214-008-9341-4
  * Hudson Cliver (2001) Hud01Hudson, HS. Cliver, EW.  200111\.  Observing coronal mass ejections without coronagraphs Observing coronal mass ejections without coronagraphs.  Journal of Geophysical Research106A1125199-25214.  10.1029/2000JA904026
  * Inhester (2006) Inh06Inhester, B.  200612\.  Stereoscopy basics for the STEREO mission Stereoscopy basics for the STEREO mission.  arXiv e-printsastro-ph/0612649.  10.48550/arXiv.astro-ph/0612649
  * Isavnin (2016) Isa16Isavnin, A.  201612\.  FRiED: A Novel Three-dimensional Model of Coronal Mass Ejections FRiED: A Novel Three-dimensional Model of Coronal Mass Ejections.  The Astrophysical Journal833267.  10.3847/1538-4357/833/2/267
  * Isavnin . (2013) Isa13Isavnin, A., Vourlidas, A. Kilpua, EKJ.  201305\.  Three-Dimensional Evolution of Erupted Flux Ropes from the Sun (2 - 20 R ⊙) to 1 AU Three-Dimensional Evolution of Erupted Flux Ropes from the Sun (2 - 20 R ⊙) to 1 AU.  Solar Physics2841203-215.  10.1007/s11207-012-0214-3
  * Jang . (2016) Jan16Jang, S., Moon, YJ., Kim, RS., Lee, H. Cho, KS.  201604\.  Comparison between 2D and 3D Parameters of 306 Front-side Halo CMEs from 2009 to 2013 Comparison between 2D and 3D Parameters of 306 Front-side Halo CMEs from 2009 to 2013.  The Astrophysical Journal821295.  10.3847/0004-637X/821/2/95
  * Jin . (2017) Jin17Jin, M., Manchester, WB., van der Holst, B., Sokolov, I., Tóth, G., Vourlidas, A.Gombosi, TI.  201701\.  Chromosphere to 1 AU Simulation of the 2011 March 7th Event: A Comprehensive Study of Coronal Mass Ejection Propagation Chromosphere to 1 AU Simulation of the 2011 March 7th Event: A Comprehensive Study of Coronal Mass Ejection Propagation.  The Astrophysical Journal834172.  10.3847/1538-4357/834/2/172
  * Kaiser . (2008) Kai08Kaiser, ML., Kucera, TA., Davila, JM., St. Cyr, OC., Guhathakurta, M. Christian, E.  200804\.  The STEREO Mission: An Introduction The STEREO Mission: An Introduction.  Space Science Reviews1361-45-16.  10.1007/s11214-007-9277-0
  * Kay Gopalswamy (2017) Kay1745Kay, C. Gopalswamy, N.  201712\.  Using the Coronal Evolution to Successfully Forward Model CMEs’ In Situ Magnetic Profiles Using the Coronal Evolution to Successfully Forward Model CMEs’ In Situ Magnetic Profiles.  Journal of Geophysical Research (Space Physics)122A1111.  10.1002/2017JA024541
  * Kay Gopalswamy (2018) Kay18Kay, C. Gopalswamy, N.  2018Sep.  The Effects of Uncertainty in Initial CME Input Parameters on Deflection, Rotation, Bz, and Arrival Time Predictions The Effects of Uncertainty in Initial CME Input Parameters on Deflection, Rotation, Bz, and Arrival Time Predictions.  Journal of Geophysical Research (Space Physics)12397220-7240.  10.1029/2018JA025780
  * Kay . (2017) Kay17ARKay, C., Gopalswamy, N., Xie, H. Yashiro, S.  201706\.  Deflection and Rotation of CMEs from Active Region 11158 Deflection and Rotation of CMEs from Active Region 11158.  Solar Physics292678.  10.1007/s11207-017-1098-z
  * Kay . (2022) Kay22Kay, C., Mays, ML. Collado-Vega, YM.  202204\.  OSPREI: A Coupled Approach to Modeling CME-Driven Space Weather With Automatically Generated, User-Friendly Outputs OSPREI: A Coupled Approach to Modeling CME-Driven Space Weather With Automatically Generated, User-Friendly Outputs.  Space Weather204e02914.  10.1029/2021SW002914
  * Kay . (2015) Kay15Kay, C., Opher, M. Evans, RM.  201506\.  Global Trends of CME Deflections Based on CME and Solar Parameters Global Trends of CME Deflections Based on CME and Solar Parameters.  The Astrophysical Journal805168.  10.1088/0004-637X/805/2/168
  * Lamy . (2019) Lam19Lamy, PL., Floyd, O., Boclet, B., Wojak, J., Gilardy, H. Barlyaeva, T.  201908\.  Coronal Mass Ejections over Solar Cycles 23 and 24 Coronal Mass Ejections over Solar Cycles 23 and 24.  Space Science Reviews215539.  10.1007/s11214-019-0605-y
  * Lee . (2013) Lee13Lee, CO., Arge, CN., Odstrčil, D., Millward, G., Pizzo, V., Quinn, JM. Henney, CJ.  201307\.  Ensemble Modeling of CME Propagation Ensemble Modeling of CME Propagation.  Solar Physics2851-2349-368.  10.1007/s11207-012-9980-1
  * Liewer . (2011) Lie11Liewer, PC., Hall, JR., Howard, RA., De Jong, EM., Thompson, WT. Thernisien, A.  201106\.  Stereoscopic analysis of STEREO/SECCHI data for CME trajectory determination Stereoscopic analysis of STEREO/SECCHI data for CME trajectory determination.  Journal of Atmospheric and Solar-Terrestrial Physics73101173-1186.  10.1016/j.jastp.2010.09.004
  * Lugaz . (2007) Lug07Lugaz, N., Manchester, I., W. B., Roussev, II., Tóth, G. Gombosi, TI.  200704\.  Numerical Investigation of the Homologous Coronal Mass Ejection Events from Active Region 9236 Numerical Investigation of the Homologous Coronal Mass Ejection Events from Active Region 9236.  The Astrophysical Journal6591788-800.  10.1086/512005
  * Lynch . (2016) Lyn16Lynch, BJ., Masson, S., Li, Y., DeVore, CR., Luhmann, JG., Antiochos, SK. Fisher, GH.  201611\.  A model for stealth coronal mass ejections A model for stealth coronal mass ejections.  Journal of Geophysical Research (Space Physics)1211110677-10697.  10.1002/2016JA023432
  * Lynch . (2021) Lyn21Lynch, BJ., Palmerio, E., DeVore, CR., Kazachenko, MD., Dahlin, JT., Pomoell, J. Kilpua, EKJ.  202106\.  Modeling a Coronal Mass Ejection from an Extended Filament Channel. I. Eruption and Early Evolution Modeling a Coronal Mass Ejection from an Extended Filament Channel. I. Eruption and Early Evolution.  The Astrophysical Journal914139.  10.3847/1538-4357/abf9a9
  * Maddox . (2014) DONKIMaddox, MM., Wiegand, C., Mullinix, R., Mays, ML., Chulaki, A., Kuznetsova, MM.Zheng, Y.  201412\.  Innovative Tools and Systems Addressing Space Weather Needs Developed By the Community Coordinated Modeling Center (CCMC) Innovative Tools and Systems Addressing Space Weather Needs Developed By the Community Coordinated Modeling Center (CCMC).  AGU Fall Meeting Abstracts AGU Fall Meeting Abstracts ( 2014, SM31A-4183).
  * Maharana . (2022) Mah22Maharana, A., Isavnin, A., Scolini, C., Wijsen, N., Rodriguez, L., Mierla, M.Poedts, S.  202209\.  Implementation and validation of the FRi3D flux rope model in EUHFORIA Implementation and validation of the FRi3D flux rope model in EUHFORIA.  Advances in Space Research7061641-1662.  10.1016/j.asr.2022.05.056
  * Maharana . (2023) Mah23Maharana, A., Scolini, C., Schmieder, B. Poedts, S.  202307\.  Rotation and interaction of the CMEs of September 8 and 10, 2014, tested with EUHFORIA Rotation and interaction of the CMEs of September 8 and 10, 2014, tested with EUHFORIA.  Astronomy & Astrophysics675A136.  10.1051/0004-6361/202345902
  * Majumdar . (2020) Maj20Majumdar, S., Pant, V., Patel, R. Banerjee, D.  202008\.  Connecting 3D Evolution of Coronal Mass Ejections to Their Source Regions Connecting 3D Evolution of Coronal Mass Ejections to Their Source Regions.  The Astrophysical Journal89916.  10.3847/1538-4357/aba1f2
  * Manchester . (2008) Man08Manchester, I., Ward B., Vourlidas, A., Tóth, G., Lugaz, N., Roussev, II., Sokolov, IV.Opher, M.  200809\.  Three-dimensional MHD Simulation of the 2003 October 28 Coronal Mass Ejection: Comparison with LASCO Coronagraph Observations Three-dimensional MHD Simulation of the 2003 October 28 Coronal Mass Ejection: Comparison with LASCO Coronagraph Observations.  The Astrophysical Journal68421448-1460.  10.1086/590231
  * Martinić . (2022) Mar22Martinić, K., Dumbović, M., Temmer, M., Veronig, A. Vršnak, B.  202205\.  Determination of coronal mass ejection orientation and consequences for their propagation Determination of coronal mass ejection orientation and consequences for their propagation.  Astronomy & Astrophysics661A155.  10.1051/0004-6361/202243433
  * Martinić . (2023) Mar23Martinić, K., Dumbović, M., Čalogović, J., Vršnak, B., Al-Haddad, N. Temmer, M.  202311\.  Effects of coronal mass ejection orientation on its propagation in the heliosphere Effects of coronal mass ejection orientation on its propagation in the heliosphere.  Astronomy & Astrophysics679A97.  10.1051/0004-6361/202346858
  * Mays, Taktakishvili . (2015) May15ATMays, ML., Taktakishvili, A., Pulkkinen, A., MacNeice, PJ., Rastätter, L., Odstrcil, D.Kuznetsova, MM.  201506\.  Ensemble Modeling of CMEs Using the WSA-ENLIL+Cone Model Ensemble Modeling of CMEs Using the WSA-ENLIL+Cone Model.  Solar Physics2901775-1814.  10.1007/s11207-015-0692-1
  * Mays, Thompson . (2015) May15Mays, ML., Thompson, BJ., Jian, LK., Colaninno, RC., Odstrcil, D., Möstl, C.Zheng, Y.  201510\.  Propagation of the 7 January 2014 CME and Resulting Geomagnetic Non-event Propagation of the 7 January 2014 CME and Resulting Geomagnetic Non-event.  The Astrophysical Journal812145.  10.1088/0004-637X/812/2/145
  * Mierla . (2008) Mie08Mierla, M., Davila, J., Thompson, W., Inhester, B., Srivastava, N., Kramar, M.Howard, RA.  200811\.  A Quick Method for Estimating the Propagation Direction of Coronal Mass Ejections Using STEREO-COR1 Images A Quick Method for Estimating the Propagation Direction of Coronal Mass Ejections Using STEREO-COR1 Images.  Solar Physics2522385-396.  10.1007/s11207-008-9267-8
  * Mierla . (2010) Mie10Mierla, M., Inhester, B., Antunes, A., Boursier, Y., Byrne, JP., Colaninno, R.Zhukov, AN.  201001\.  On the 3-D reconstruction of Coronal Mass Ejections using coronagraph data On the 3-D reconstruction of Coronal Mass Ejections using coronagraph data.  Annales Geophysicae28203-215.  10.5194/angeo-28-203-2010
  * Millward . (2013) Mil13Millward, G., Biesecker, D., Pizzo, V. de Koning, CA.  201302\.  An operational software tool for the analysis of coronagraph images: Determining CME parameters for input into the WSA-Enlil heliospheric model An operational software tool for the analysis of coronagraph images: Determining CME parameters for input into the WSA-Enlil heliospheric model.  Space Weather11257-68.  10.1002/swe.20024
  * Moran . (2010) Mor10Moran, TG., Davila, JM. Thompson, WT.  201003\.  Three-Dimensional Polarimetric Coronal Mass Ejection Localization Tested Through Triangulation Three-Dimensional Polarimetric Coronal Mass Ejection Localization Tested Through Triangulation.  The Astrophysical Journal7121453-458.  10.1088/0004-637X/712/1/453
  * Möstl . (2018) Mos18Möstl, C., Amerstorfer, T., Palmerio, E., Isavnin, A., Farrugia, CJ., Lowder, C.Boakes, PD.  201803\.  Forward Modeling of Coronal Mass Ejection Flux Ropes in the Inner Heliosphere with 3DCORE Forward Modeling of Coronal Mass Ejection Flux Ropes in the Inner Heliosphere with 3DCORE.  Space Weather163216-229.  10.1002/2017SW001735
  * Nieves-Chinchilla . (2022) Nie22Nieves-Chinchilla, T., Alzate, N., Cremades, H., Rodriguez-Garcia, L., Dos Santos, LFG., Narock, A.Kasper, JC.  202205\.  Direct First Parker Solar Probe Observation of the Interaction of Two Successive Interplanetary Coronal Mass Ejections in 2020 November Direct First Parker Solar Probe Observation of the Interaction of Two Successive Interplanetary Coronal Mass Ejections in 2020 November.  The Astrophysical Journal930188.  10.3847/1538-4357/ac590b
  * Odstrcil (2023) Ods23Odstrcil, D.  202308\.  Heliospheric 3-D MHD ENLIL simulations of multi-CME and multi-spacecraft events Heliospheric 3-D MHD ENLIL simulations of multi-CME and multi-spacecraft events.  Frontiers in Astronomy and Space Sciences101226992.  10.3389/fspas.2023.1226992
  * Pal . (2022) Pal22sanchitaPal, S., Nandy, D. Kilpua, EKJ.  202209\.  Magnetic cloud prediction model for forecasting space weather relevant properties of Earth-directed coronal mass ejections Magnetic cloud prediction model for forecasting space weather relevant properties of Earth-directed coronal mass ejections.  Astronomy & Astrophysics665A110.  10.1051/0004-6361/202243513
  * Palmerio . (2017) Pal17Palmerio, E., Kilpua, EKJ., James, AW., Green, LM., Pomoell, J., Isavnin, A. Valori, G.  201702\.  Determining the Intrinsic CME Flux Rope Type Using Remote-sensing Solar Disk Observations Determining the Intrinsic CME Flux Rope Type Using Remote-sensing Solar Disk Observations.  Solar Physics29239.  10.1007/s11207-017-1063-x
  * Palmerio . (2018) Pal18Palmerio, E., Kilpua, EKJ., Möstl, C., Bothmer, V., James, AW., Green, LM.Harrison, RA.  201805\.  Coronal Magnetic Structure of Earthbound CMEs and In Situ Comparison Coronal Magnetic Structure of Earthbound CMEs and In Situ Comparison.  Space Weather165442-460.  10.1002/2017SW001767
  * Palmerio . (2022) Pal22enlilPalmerio, E., Lee, CO., Mays, ML., Luhmann, JG., Lario, D., Sánchez-Cano, B.Mitrofanov, IG.  202205\.  CMEs and SEPs During November-December 2020: A Challenge for Real-Time Space Weather Forecasting CMEs and SEPs During November-December 2020: A Challenge for Real-Time Space Weather Forecasting.  Space Weather205e2021SW002993.  10.1029/2021SW002993
  * Palmerio . (2023) Pal23Palmerio, E., Maharana, A., Lynch, BJ., Scolini, C., Good, SW., Pomoell, J.Kilpua, EKJ.  202311\.  Modeling a Coronal Mass Ejection from an Extended Filament Channel. II. Interplanetary Propagation to 1 au Modeling a Coronal Mass Ejection from an Extended Filament Channel. II. Interplanetary Propagation to 1 au.  The Astrophysical Journal958191.  10.3847/1538-4357/ad0229
  * Palmerio . (2019) Pal19Palmerio, E., Scolini, C., Barnes, D., Magdalenić, J., West, MJ., Zhukov, AN.Poedts, S.  201906\.  Multipoint Study of Successive Coronal Mass Ejections Driving Moderate Disturbances at 1 au Multipoint Study of Successive Coronal Mass Ejections Driving Moderate Disturbances at 1 au.  The Astrophysical Journal878137.  10.3847/1538-4357/ab1850
  * Pizzo . (2011) Piz11Pizzo, V., Millward, G., Parsons, A., Biesecker, D., Hill, S. Odstrcil, D.  201103\.  Wang-Sheeley-Arge-Enlil Cone Model Transitions to Operations Wang-Sheeley-Arge-Enlil Cone Model Transitions to Operations.  Space Weather9303004.  10.1029/2011SW000663
  * Pluta . (2019) Plu19Pluta, A., Mrotzek, N., Vourlidas, A., Bothmer, V. Savani, N.  201903\.  Combined geometrical modelling and white-light mass determination of coronal mass ejections Combined geometrical modelling and white-light mass determination of coronal mass ejections.  Astronomy & Astrophysics623A139.  10.1051/0004-6361/201833829
  * Rodriguez . (2011) Rod11Rodriguez, L., Mierla, M., Zhukov, AN., West, M. Kilpua, E.  201106\.  Linking Remote-Sensing and In Situ Observations of Coronal Mass Ejections Using STEREO Linking Remote-Sensing and In Situ Observations of Coronal Mass Ejections Using STEREO.  Solar Physics270561-573.  10.1007/s11207-011-9784-8
  * Sachdeva . (2017) Sac17Sachdeva, N., Subramanian, P., Vourlidas, A. Bothmer, V.  201709\.  CME Dynamics Using STEREO and LASCO Observations: The Relative Importance of Lorentz Forces and Solar Wind Drag CME Dynamics Using STEREO and LASCO Observations: The Relative Importance of Lorentz Forces and Solar Wind Drag.  Solar Physics2929118.  10.1007/s11207-017-1137-9
  * Savani . (2013) Sav13Savani, NP., Vourlidas, A., Pulkkinen, A., Nieves-Chinchilla, T., Lavraud, B. Owens, MJ.  201305\.  Tracking the momentum flux of a CME and quantifying its influence on geomagnetically induced currents at Earth Tracking the momentum flux of a CME and quantifying its influence on geomagnetically induced currents at Earth.  Space Weather115245-261.  10.1002/swe.20038
  * Scolini . (2019) Sco19Scolini, C., Rodriguez, L., Mierla, M., Pomoell, J. Poedts, S.  201906\.  Observation-based modelling of magnetised coronal mass ejections with EUHFORIA Observation-based modelling of magnetised coronal mass ejections with EUHFORIA.  Astronomy & Astrophysyics626A122.  10.1051/0004-6361/201935053
  * Sharpe Murray (2017) Sha17Sharpe, MA. Murray, SA.  201710\.  Verification of Space Weather Forecasts Issued by the Met Office Space Weather Operations Centre Verification of Space Weather Forecasts Issued by the Met Office Space Weather Operations Centre.  Space Weather15101383-1395.  10.1002/2017SW001683
  * Shen . (2014) She14bShen, C., Wang, Y., Pan, Z., Miao, B., Ye, P. Wang, S.  201407\.  Full-halo coronal mass ejections: Arrival at the Earth Full-halo coronal mass ejections: Arrival at the Earth.  Journal of Geophysical Research (Space Physics)11975107-5116.  10.1002/2014JA020001
  * Shi . (2015) Shi15Shi, T., Wang, Y., Wan, L., Cheng, X., Ding, M. Zhang, J.  201506\.  Predicting the Arrival Time of Coronal Mass Ejections with the Graduated Cylindrical Shell and Drag Force Model Predicting the Arrival Time of Coronal Mass Ejections with the Graduated Cylindrical Shell and Drag Force Model.  The Astrophysical Journal8062271.  10.1088/0004-637X/806/2/271
  * SILSO World Data Center (2023) sidcSILSO World Data Center.  2023\.  The International Sunspot Number The International Sunspot Number [Dataset].  International Sunspot Number Monthly Bulletin and online catalogue.
  * Singh . (2022) Sin22Singh, T., Kim, TK., Pogorelov, NV. Arge, CN.  202207\.  Ensemble Simulations of the 2012 July 12 Coronal Mass Ejection with the Constant-turn Flux Rope Model Ensemble Simulations of the 2012 July 12 Coronal Mass Ejection with the Constant-turn Flux Rope Model.  The Astrophysical Journal9332123.  10.3847/1538-4357/ac73f3
  * Temmer . (2021) Tem21bTemmer, M., Holzknecht, L., Dumbović, M., Vršnak, B., Sachdeva, N., Heinemann, SG.Hofmeister, SJ.  202101\.  Deriving CME Density From Remote Sensing Data and Comparison to In Situ Measurements Deriving CME Density From Remote Sensing Data and Comparison to In Situ Measurements.  Journal of Geophysical Research (Space Physics)1261e28380.  10.1029/2020JA028380
  * Temmer . (2009) Tem09Temmer, M., Preiss, S. Veronig, AM.  200905\.  CME Projection Effects Studied with STEREO/COR and SOHO/LASCO CME Projection Effects Studied with STEREO/COR and SOHO/LASCO.  Solar Physics2561-2183-199.  10.1007/s11207-009-9336-7
  * A. Thernisien (2011) The11Thernisien, A.  201106\.  Implementation of the Graduated Cylindrical Shell Model for the Three-dimensional Reconstruction of Coronal Mass Ejections Implementation of the Graduated Cylindrical Shell Model for the Three-dimensional Reconstruction of Coronal Mass Ejections.  The Astrophysical Journals19433.  10.1088/0067-0049/194/2/33
  * A. Thernisien . (2009) The09Thernisien, A., Vourlidas, A. Howard, RA.  200905\.  Forward Modeling of Coronal Mass Ejections Using STEREO/SECCHI Data Forward Modeling of Coronal Mass Ejections Using STEREO/SECCHI Data.  Solar Physics256111-130.  10.1007/s11207-009-9346-5
  * AFR. Thernisien . (2006) The06Thernisien, AFR., Howard, RA. Vourlidas, A.  200611\.  Modeling of Flux Rope Coronal Mass Ejections Modeling of Flux Rope Coronal Mass Ejections.  The Astrophysical Journal652763-773.  10.1086/508254
  * Török . (2018) Tor18Török, T., Downs, C., Linker, JA., Lionello, R., Titov, VS., Mikić, Z.Wijaya, J.  201803\.  Sun-to-Earth MHD Simulation of the 2000 July 14 “Bastille Day” Eruption Sun-to-Earth MHD Simulation of the 2000 July 14 “Bastille Day” Eruption.  The Astrophysical Journal856175.  10.3847/1538-4357/aab36d
  * Verbeke . (2023) Ver23Verbeke, C., Mays, ML., Kay, C., Riley, P., Palmerio, E., Dumbović, M.Hinterreiter, J.  202312\.  Quantifying errors in 3D CME parameters derived from synthetic data using white-light reconstruction techniques Quantifying errors in 3D CME parameters derived from synthetic data using white-light reconstruction techniques.  Advances in Space Research72125243-5262.  10.1016/j.asr.2022.08.056
  * Vourlidas . (2002) Vou02Vourlidas, A., Buzasi, D., Howard, RA. Esfandiari, E.  200212\.  Mass and energy properties of LASCO CMEs Mass and energy properties of LASCO CMEs.  A. Wilson (), Solar Variability: From Core to Outer Frontiers Solar variability: From core to outer frontiers ( 1, 91-94).
  * Vourlidas . (2011) Vou11Vourlidas, A., Colaninno, R., Nieves-Chinchilla, T. Stenborg, G.  201106\.  The First Observation of a Rapidly Rotating Coronal Mass Ejection in the Middle Corona The First Observation of a Rapidly Rotating Coronal Mass Ejection in the Middle Corona.  The Astrophysical Journal Letters733L23.  10.1088/2041-8205/733/2/L23
  * Vourlidas . (2010) Vou10Vourlidas, A., Howard, RA., Esfandiari, E., Patsourakos, S., Yashiro, S. Michalek, G.  201010\.  Comprehensive Analysis of Coronal Mass Ejection Mass and Energy Properties Over a Full Solar Cycle Comprehensive Analysis of Coronal Mass Ejection Mass and Energy Properties Over a Full Solar Cycle.  The Astrophysical Journal7221522-1538.  10.1088/0004-637X/722/2/1522
  * Vourlidas . (2013) Vou13Vourlidas, A., Lynch, BJ., Howard, RA. Li, Y.  201305\.  How Many CMEs Have Flux Ropes? Deciphering the Signatures of Shocks, Flux Ropes, and Prominences in Coronagraph Observations of CMEs How Many CMEs Have Flux Ropes? Deciphering the Signatures of Shocks, Flux Ropes, and Prominences in Coronagraph Observations of CMEs.  Solar Physics2841179-201.  10.1007/s11207-012-0084-8
  * Vršnak . (2005) Vrs05Vršnak, B., Sudar, D. Ruždjak, D.  200506\.  The CME-flare relationship: Are there really two types of CMEs? The CME-flare relationship: Are there really two types of CMEs?  Astronomy & Astrophysics43531149-1157.  10.1051/0004-6361:20042166
  * Vršnak . (2008) Vrs08Vršnak, B., Vrbanec, D. Čalogović, J.  200811\.  Dynamics of coronal mass ejections. The mass-scaling of the aerodynamic drag Dynamics of coronal mass ejections. The mass-scaling of the aerodynamic drag.  Astronomy & Astrophysics4902811-815.  10.1051/0004-6361:200810215
  * Welsch (2018) Wel18Welsch, BT.  201807\.  Flux Accretion and Coronal Mass Ejection Dynamics Flux Accretion and Coronal Mass Ejection Dynamics.  Solar Physics2937113.  10.1007/s11207-018-1329-y
  * Wood Howard (2009) Woo09Wood, BE. Howard, RA.  200909\.  An Empirical Reconstruction of the 2008 April 26 Coronal Mass Ejection An Empirical Reconstruction of the 2008 April 26 Coronal Mass Ejection.  The Astrophysical Journal702901-910.  10.1088/0004-637X/702/2/901
  * Wood . (2017) Woo17Wood, BE., Wu, CC., Lepping, RP., Nieves-Chinchilla, T., Howard, RA., Linton, MG. Socker, DG.  201704\.  A STEREO Survey of Magnetic Cloud Coronal Mass Ejections Observed at Earth in 2008-2012 A STEREO Survey of Magnetic Cloud Coronal Mass Ejections Observed at Earth in 2008-2012.  The Astrophysical Journals22929.  10.3847/1538-4365/229/2/29
  * Xie . (2021) Xie21Xie, H., Gopalswamy, N. Akiyama, S.  202111\.  The Structural Connection between Coronal Mass Ejection Flux Ropes near the Sun and at 1 au The Structural Connection between Coronal Mass Ejection Flux Ropes near the Sun and at 1 au.  The Astrophysical Journal922164.  10.3847/1538-4357/ac23cc
  * Xue . (2005) xue05bXue, XH., Wang, CB. Dou, XK.  200508\.  An ice-cream cone model for coronal mass ejections An ice-cream cone model for coronal mass ejections.  Journal of Geophysical Research1108103.  10.1029/2004JA010698
  * Yashiro . (2004) Yas04Yashiro, S., Gopalswamy, N., Michalek, G., St. Cyr, OC., Plunkett, SP., Rich, NB. Howard, RA.  200407\.  A catalog of white light coronal mass ejections observed by the SOHO spacecraft A catalog of white light coronal mass ejections observed by the SOHO spacecraft.  Journal of Geophysical Research (Space Physics)1097105.  10.1029/2003JA010282
  * Yurchyshyn . (2007) Yur07Yurchyshyn, V., Hu, Q., Lepping, RP., Lynch, BJ. Krall, J.  200701\.  Orientations of LASCO Halo CMEs and their connection to the flux rope structure of interplanetary CMEs Orientations of LASCO Halo CMEs and their connection to the flux rope structure of interplanetary CMEs.  Advances in Space Research40121821-1826.  10.1016/j.asr.2007.01.059
  * Yurchyshyn . (2005) Yur05Yurchyshyn, V., Yashiro, S., Abramenko, V., Wang, H. Gopalswamy, N.  200501\.  Statistical Distributions of Speeds of Coronal Mass Ejections Statistical Distributions of Speeds of Coronal Mass Ejections.  The Astrophysical Journal6191599-603.  10.1086/426129
  * Zhang (2022) Zha22Zhang, QM.  202204\.  Tracking the 3D evolution of a halo coronal mass ejection using the revised cone model Tracking the 3D evolution of a halo coronal mass ejection using the revised cone model.  Astronomy & Astrophysics660A144.  10.1051/0004-6361/202142942
  * Zhong . (2021) Zho21Zhong, Z., Shen, C., Mao, D., Chi, Y., Xu, M., Liu, J. Wang, Y.  202109\.  Three-Dimensional Parameters of the Earth-Impacting CMEs Based on the GCS Model Three-Dimensional Parameters of the Earth-Impacting CMEs Based on the GCS Model.  Universe710361.  10.3390/universe7100361
  * Zhuang . (2017) Zhu17Zhuang, B., Wang, Y., Shen, C., Liu, S., Wang, J., Pan, Z.Liu, R.  201708\.  The Significance of the Influence of the CME Deflection in Interplanetary Space on the CME Arrival at Earth The Significance of the Influence of the CME Deflection in Interplanetary Space on the CME Arrival at Earth.  The Astrophysical Journal8452117.  10.3847/1538-4357/aa7fc0