COMPARISON BETWEEN 2D AND 3D PARAMETERS OF 306 FRONT-SIDE HALO CMEs FROM 2009 TO 2013

Jang, Su-Jin; Moon, Yong‐Jae; Kim, Rok‐Soon; Lee, Harim; Cho, K.-S.

doi:10.3847/0004-637x/821/2/95

Cited by 27 publications

(29 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gopalswamy et al () could show from observations that

v_{r} \approx \frac{1}{2} v_{\exp}

, giving at last the very convenient conclusion that v r ≈ v disk for very fast ( v r ≈1,000 km/s) and wide ( ω ≈45°) CMEs. This relationship was also found in a more recent and larger statistical study (Jang et al, ). Thus, it will suffice to estimate v disk , which may be done in the frameseries mode in StereoCAT (LaSota, ).…”

Section: Methodssupporting

confidence: 83%

Modeling the Multiple CME Interaction Event on 6–9 September 2017 with WSA‐ENLIL+Cone

et al. 2019

View full text Add to dashboard Cite

A series of coronal mass ejections (CMEs) erupted from the same active region between 4–6 September 2017. Later, on 6–9 September, two interplanetary (IP) shocks reached L1, creating a complex and geoeffective plasma structure. To understand the processes leading up to the formation of the two shocks, we model the CMEs with the Wang‐Sheeley‐Arge (WSA)‐ENLIL+Cone model. The first two CMEs merged already in the solar corona driving the first IP shock. In IP space, another fast CME presumably interacted with the flank of the preceding CMEs and caused the second shock detected in situ. By introducing a customized density enhancement factor (dcld) in the WSA‐ENLIL+Cone model based on coronagraph image observations, the predicted arrival time of the first IP shock was drastically improved. When the dcld factor was tested on a well‐defined single CME event from 12 July 2012 the shock arrival time saw similar improvement. These results suggest that the proposed approach may be an alternative to improve the forecast for fast and simple CMEs. Further, the slowly decelerating kilometric type II radio burst confirms that the properties of the background solar wind have been preconditioned by the passage of the first IP shock. This likely caused the last CME to experience insignificant deceleration and led to the early arrival of the second IP shock. This result emphasizes the need to take preconditioning of the IP medium into account when making forecasts of CMEs erupting in quick succession.

show abstract

“…Gopalswamy et al () could show from observations that

v_{r} \approx \frac{1}{2} v_{\exp}

Section: Methodssupporting

confidence: 83%

Modeling the Multiple CME Interaction Event on 6–9 September 2017 with WSA‐ENLIL+Cone

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Although for Sample 3 the projected 2D CME launch speed (v 0 ) were used, compared to the Sample 1 where 3D speeds were employed and the differences in those speeds can be up to 20% (e.g. Jang et al 2016), we note that this doesn't introduce a big difference in samples since the other uncertainties related to v 0 may be the far larger (e.g. estimation of v 0 very close to the Sun).…”

Section: Dbemv3 Evaluation With Richardson and Cane (2010) Icme Event...mentioning

confidence: 99%

Probabilistic Drag-Based Ensemble Model (DBEM) Evaluation for Heliospheric Propagation of CMEs

Čalogović,

Dumbović,

Sudar

et al. 2021

Preprint

View full text Add to dashboard Cite

The Drag-based Model (DBM) is a 2D analytical model for heliospheric propagation of Coronal Mass Ejections (CMEs) in ecliptic plane predicting the CME arrival time and speed at Earth or any other given target in the solar system. It is based on the equation of motion and depends on initial CME parameters, background solar wind speed, w and the drag parameter γ. A very short computational time of DBM (< 0.01s) allowed us to develop the Drag-Based Ensemble Model (DBEM) that takes into account the variability of model input parameters by making an ensemble of n different input parameters to calculate the distribution and significance of the DBM results. Thus the DBEM is able to calculate the most likely CME arrival times and speeds, quantify the prediction uncertainties and determine the confidence intervals. A new DBEMv3 version is described in detail and evaluated for the first time determing the DBEMv3 performance and errors by using various CME-ICME lists as well as it is compared with previous DBEM versions. The analysis to find the optimal drag parameter γ and ambient solar wind speed w showed that somewhat higher values (γ ≈ 0.3 × 10 −7 km −1 , w ≈ 425 km s −1 ) for both of these DBEM input parameters should be used for the evaluation compared to the previously employed ones. Based on the evaluation performed for 146 CME-ICME pairs, the DBEMv3 performance with mean error (ME) of -11.3 h, mean absolute error (MAE) of 17.3 h was obtained. There is a clear bias towards the negative prediction errors where the fast CMEs are predicted to arrive too

show abstract

“…Observations from large‐angle viewpoints minimize the projection effects. If observations from multiple longitudinally separated points are available, they allow getting more reliable parameters (e.g., Jang et al, ; H. Lee et al, ) through, for example, three‐dimensional (3‐D) forward modeling (Thernisien et al, ) or triangulation (e.g., Mierla et al, ; Rouillard et al, ). For example, the graduated cylindrical shell (GCS) model describes the CME as a donut‐shaped section attached to two conical legs (croissant‐shaped; J. Chen et al, ; Thernisien et al, ), see example of the fitting in Figure .…”

Section: Intrinsic Parameters Of Cmesmentioning

confidence: 99%

Forecasting the Structure and Orientation of Earthbound Coronal Mass Ejections

et al. 2019

View full text Add to dashboard Cite

Coronal Mass Ejections (CMEs) are the key drivers of strong to extreme space weather storms at the Earth that can have drastic consequences for technological systems in space and on ground. The ability of a CME to drive geomagnetic disturbances depends crucially on the magnetic structure of the embedded flux rope, which is thus essential to predict. The current capabilities in forecasting in advance (at least half a day before) the geoeffectiveness of a given CME is however severely hampered by the lack of remote-sensing measurements of the magnetic field in the corona and adequate tools to predict how CMEs deform, rotate, and deflect during their travel through the coronal and interplanetary space as they interact with the ambient solar wind and other CMEs. These problems can lead not only to overestimation or underestimation of the severity of a storm, but also to forecasting "misses" and "false alarms" that are particularly difficult for the end-users. In this paper, we discuss the current status and future challenges and prospects related to forecasting of the magnetic structure and orientation of CMEs. We focus both on observational-and modeling-based (first principle and semiempirical) approaches and discuss the space-and ground-based observations that would be the most optimal for making accurate space weather predictions. We also cover the gaps in our current understanding related to the formation and eruption of the CME flux rope and physical processes that govern its evolution in the variable ambient solar wind background that complicate the forecasting.

show abstract

COMPARISON BETWEEN 2D AND 3D PARAMETERS OF 306 FRONT-SIDE HALO CMEs FROM 2009 TO 2013

Cited by 27 publications

References 37 publications

Modeling the Multiple CME Interaction Event on 6–9 September 2017 with WSA‐ENLIL+Cone

Modeling the Multiple CME Interaction Event on 6–9 September 2017 with WSA‐ENLIL+Cone

Probabilistic Drag-Based Ensemble Model (DBEM) Evaluation for Heliospheric Propagation of CMEs

Forecasting the Structure and Orientation of Earthbound Coronal Mass Ejections

Contact Info

Product

Resources

About