Correcting Projection Effects in CMEs Using GCS‐Based Large Statistics of Multi‐Viewpoint Observations
Abstract This study addresses the limitations of single‐viewpoint observations of Coronal Mass Ejections (CMEs) by presenting results from a 3D catalog of 360 CMEs during solar cycle 24, fitted using the Graduated Cylindrical Shell (GCS) model. The data set combines 326 previously analyzed CMEs and...
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| Format: | Article |
| Language: | English |
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Wiley
2024-02-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003805 |
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| author | Harshita Gandhi Ritesh Patel Vaibhav Pant Satabdwa Majumdar Sanchita Pal Dipankar Banerjee Huw Morgan |
| author_facet | Harshita Gandhi Ritesh Patel Vaibhav Pant Satabdwa Majumdar Sanchita Pal Dipankar Banerjee Huw Morgan |
| author_sort | Harshita Gandhi |
| collection | DOAJ |
| description | Abstract This study addresses the limitations of single‐viewpoint observations of Coronal Mass Ejections (CMEs) by presenting results from a 3D catalog of 360 CMEs during solar cycle 24, fitted using the Graduated Cylindrical Shell (GCS) model. The data set combines 326 previously analyzed CMEs and 34 newly examined events, categorized by their source regions into active region (AR) eruptions, active prominence (AP) eruptions, and prominence eruptions (PE). Estimates of errors are made using a bootstrapping approach. The findings highlight that the average 3D speed of CMEs is ∼1.3 times greater than the 2D speed. PE CMEs tend to be slow, with an average speed of 432 km s−1. AR and AP speeds are higher, at 723 and 813 km s−1, respectively, with the latter having fewer slow CMEs. The distinctive behavior of AP CMEs is attributed to factors like overlying magnetic field distribution or geometric complexities leading to less accurate GCS fits. A linear fit of projected speed to width gives a gradient of ∼2 km s−1 deg−1, which increases to 5 km s−1 deg−1 when the GCS‐fitted ‘true’ parameters are used. Notably, AR CMEs exhibit a high gradient of 7 km s−1 deg−1, while AP CMEs show a gradient of 4 km s−1 deg−1. PE CMEs, however, lack a significant speed‐width relationship. We show that fitting multi‐viewpoint CME images to a geometrical model such as GCS is important to study the statistical properties of CMEs, and can lead to a deeper insight into CME behavior that is essential for improving future space weather forecasting. |
| format | Article |
| id | doaj-art-c73ebd5a02ee4f4ab8605b8611647a0c |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-c73ebd5a02ee4f4ab8605b8611647a0c2025-01-14T16:30:41ZengWileySpace Weather1542-73902024-02-01222n/an/a10.1029/2023SW003805Correcting Projection Effects in CMEs Using GCS‐Based Large Statistics of Multi‐Viewpoint ObservationsHarshita Gandhi0Ritesh Patel1Vaibhav Pant2Satabdwa Majumdar3Sanchita Pal4Dipankar Banerjee5Huw Morgan6Department of Physics Aberystwyth University Aberystwyth UKSouthwest Research Institute Boulder CO USAAryabhatta Research Institute of Observational Sciences Nainital IndiaAustrian Space Weather Office GeoSphere Austria Graz AustriaHeliophysics Science Division NASA Goddard Space Flight Center Greenbelt MD USAAryabhatta Research Institute of Observational Sciences Nainital IndiaDepartment of Physics Aberystwyth University Aberystwyth UKAbstract This study addresses the limitations of single‐viewpoint observations of Coronal Mass Ejections (CMEs) by presenting results from a 3D catalog of 360 CMEs during solar cycle 24, fitted using the Graduated Cylindrical Shell (GCS) model. The data set combines 326 previously analyzed CMEs and 34 newly examined events, categorized by their source regions into active region (AR) eruptions, active prominence (AP) eruptions, and prominence eruptions (PE). Estimates of errors are made using a bootstrapping approach. The findings highlight that the average 3D speed of CMEs is ∼1.3 times greater than the 2D speed. PE CMEs tend to be slow, with an average speed of 432 km s−1. AR and AP speeds are higher, at 723 and 813 km s−1, respectively, with the latter having fewer slow CMEs. The distinctive behavior of AP CMEs is attributed to factors like overlying magnetic field distribution or geometric complexities leading to less accurate GCS fits. A linear fit of projected speed to width gives a gradient of ∼2 km s−1 deg−1, which increases to 5 km s−1 deg−1 when the GCS‐fitted ‘true’ parameters are used. Notably, AR CMEs exhibit a high gradient of 7 km s−1 deg−1, while AP CMEs show a gradient of 4 km s−1 deg−1. PE CMEs, however, lack a significant speed‐width relationship. We show that fitting multi‐viewpoint CME images to a geometrical model such as GCS is important to study the statistical properties of CMEs, and can lead to a deeper insight into CME behavior that is essential for improving future space weather forecasting.https://doi.org/10.1029/2023SW003805coronal mass ejectionforward modelingkinematicsmulti‐viewpointbootstrapstereoscopy |
| spellingShingle | Harshita Gandhi Ritesh Patel Vaibhav Pant Satabdwa Majumdar Sanchita Pal Dipankar Banerjee Huw Morgan Correcting Projection Effects in CMEs Using GCS‐Based Large Statistics of Multi‐Viewpoint Observations Space Weather coronal mass ejection forward modeling kinematics multi‐viewpoint bootstrap stereoscopy |
| title | Correcting Projection Effects in CMEs Using GCS‐Based Large Statistics of Multi‐Viewpoint Observations |
| title_full | Correcting Projection Effects in CMEs Using GCS‐Based Large Statistics of Multi‐Viewpoint Observations |
| title_fullStr | Correcting Projection Effects in CMEs Using GCS‐Based Large Statistics of Multi‐Viewpoint Observations |
| title_full_unstemmed | Correcting Projection Effects in CMEs Using GCS‐Based Large Statistics of Multi‐Viewpoint Observations |
| title_short | Correcting Projection Effects in CMEs Using GCS‐Based Large Statistics of Multi‐Viewpoint Observations |
| title_sort | correcting projection effects in cmes using gcs based large statistics of multi viewpoint observations |
| topic | coronal mass ejection forward modeling kinematics multi‐viewpoint bootstrap stereoscopy |
| url | https://doi.org/10.1029/2023SW003805 |
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