{"id":{"repo_id":"njit","oai_identifier":"oai:digitalcommons.njit.edu:dissertations-1909"},"canonical_url":"https://search.dev.ndltd.org/etd/njit/oai:digitalcommons.njit.edu:dissertations-1909","repository":{"repo_id":"njit","name":"NJIT","base_url":"https://digitalcommons.njit.edu/do/oai/"},"display":{"title":"Automatic prediction of solar flares and super geomagnetic storms","abstract":"Space weather is the response of our space environment to the constantly changing Sun. As the new technology advances, mankind has become more and more dependent on space system, satellite-based services. A geomagnetic storm, a disturbance in Earth's magnetosphere, may produce many harmful effects on Earth. Solar flares and Coronal Mass Ejections (CMEs) are believed to be the major causes of geomagnetic storms. Thus, establishing a real time forecasting method for them is very important in space weather study. The topics covered in this dissertation are: the relationship between magnetic gradient and magnetic shear of solar active regions; the relationship between solar flare index and magnetic features of solar active regions; based on these relationships a statistical ordinal logistic regression model is developed to predict the probability of solar flare occurrences in the next 24 hours; and finally the relationship between magnetic structures of CME source regions and geomagnetic storms, in particular, the super storms when the index decreases below -200 nT is studied and proved to be able to predict those super storms. The results are briefly summarized as follows: (1) There is a significant correlation between magnetic gradient and magnetic shear of active region. Furthermore, compared with magnetic shear, magnetic gradient might be a better proxy to locate where a large flare occurs. It appears to be more accurate in identification of sources of X-class flares than M-class flares; (2) Flare index, defined by weighting the SXR flares, is proved to have positive correlation with three magnetic features of active region; (3) A statistical ordinal logistic regression model is proposed for solar flare prediction. The results are much better than those data published in the NASA/SDAC service, and comparable to the data provided by the NOAA/SEC complicated expert system. To our knowledge, this is the first time that logistic regression model has been applied in solar physics to predict flare occurrences; (4) The magnetic orientation angle [theta], determined from a potential field model, is proved to be able to predict the probability of super geomagnetic storms (Dst <= -200nT). The results show that those active regions associated with |[theta]| < 90 are more likely to cause a super geomagnetic storm.","abstract_html":"Space weather is the response of our space environment to the constantly changing Sun. As the new technology advances, mankind has become more and more dependent on space system, satellite-based services. A geomagnetic storm, a disturbance in Earth&#x27;s magnetosphere, may produce many harmful effects on Earth. Solar flares and Coronal Mass Ejections (CMEs) are believed to be the major causes of geomagnetic storms. Thus, establishing a real time forecasting method for them is very important in space weather study. The topics covered in this dissertation are: the relationship between magnetic gradient and magnetic shear of solar active regions; the relationship between solar flare index and magnetic features of solar active regions; based on these relationships a statistical ordinal logistic regression model is developed to predict the probability of solar flare occurrences in the next 24 hours; and finally the relationship between magnetic structures of CME source regions and geomagnetic storms, in particular, the super storms when the index decreases below -200 nT is studied and proved to be able to predict those super storms. The results are briefly summarized as follows: (1) There is a significant correlation between magnetic gradient and magnetic shear of active region. Furthermore, compared with magnetic shear, magnetic gradient might be a better proxy to locate where a large flare occurs. It appears to be more accurate in identification of sources of X-class flares than M-class flares; (2) Flare index, defined by weighting the SXR flares, is proved to have positive correlation with three magnetic features of active region; (3) A statistical ordinal logistic regression model is proposed for solar flare prediction. The results are much better than those data published in the NASA/SDAC service, and comparable to the data provided by the NOAA/SEC complicated expert system. To our knowledge, this is the first time that logistic regression model has been applied in solar physics to predict flare occurrences; (4) The magnetic orientation angle [theta], determined from a potential field model, is proved to be able to predict the probability of super geomagnetic storms (Dst &lt;= -200nT). The results show that those active regions associated with |[theta]| &lt; 90 are more likely to cause a super geomagnetic storm.","abstract_has_math":false,"creators":["Song, Hui"],"institution":null,"degree_name":"Doctor of Philosophy in Applied Physics - (Ph.D.)","degree_level":null,"degree_discipline":"Federated Physics Department","degree_department":null,"school":null,"contributors":["Haimin Wang","Dale E. Gary","Andrew Gerrard"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008-01-27T08:00:00Z","date_published":"2008-01-27T08:00:00Z","updated_at":"2026-07-24T03:23:09Z","subjects":["Solar magnetic field","Solar flares","Space weather","Coronal mass ejection","Gepmagnetic storm","Forestry","Other Physics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.njit.edu/dissertations/854","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Haimin Wang","Dale E. Gary","Andrew Gerrard"]},{"key":"dc:creator","label":"Author","values":["Song, Hui"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Federated Physics Department"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Applied Physics - (Ph.D.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Solar magnetic field","Solar flares","Space weather","Coronal mass ejection","Gepmagnetic storm","Forestry","Other Physics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.njit.edu/dissertations/854"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Space weather is the response of our space environment to the constantly changing Sun. As the new technology advances, mankind has become more and more dependent on space system, satellite-based services. A geomagnetic storm, a disturbance in Earth's magnetosphere, may produce many harmful effects on Earth. Solar flares and Coronal Mass Ejections (CMEs) are believed to be the major causes of geomagnetic storms. Thus, establishing a real time forecasting method for them is very important in space weather study. The topics covered in this dissertation are: the relationship between magnetic gradient and magnetic shear of solar active regions; the relationship between solar flare index and magnetic features of solar active regions; based on these relationships a statistical ordinal logistic regression model is developed to predict the probability of solar flare occurrences in the next 24 hours; and finally the relationship between magnetic structures of CME source regions and geomagnetic storms, in particular, the super storms when the index decreases below -200 nT is studied and proved to be able to predict those super storms. The results are briefly summarized as follows: (1) There is a significant correlation between magnetic gradient and magnetic shear of active region. Furthermore, compared with magnetic shear, magnetic gradient might be a better proxy to locate where a large flare occurs. It appears to be more accurate in identification of sources of X-class flares than M-class flares; (2) Flare index, defined by weighting the SXR flares, is proved to have positive correlation with three magnetic features of active region; (3) A statistical ordinal logistic regression model is proposed for solar flare prediction. The results are much better than those data published in the NASA/SDAC service, and comparable to the data provided by the NOAA/SEC complicated expert system. To our knowledge, this is the first time that logistic regression model has been applied in solar physics to predict flare occurrences; (4) The magnetic orientation angle [theta], determined from a potential field model, is proved to be able to predict the probability of super geomagnetic storms (Dst <= -200nT). The results show that those active regions associated with |[theta]| < 90 are more likely to cause a super geomagnetic storm."]},{"key":"dc:title","label":"Title","values":["Automatic prediction of solar flares and super geomagnetic storms"]}]}],"canonical_facts":{"dc:contributor":["Haimin Wang","Dale E. Gary","Andrew Gerrard"],"dc:creator":["Song, Hui"],"dc:description.abstract":["Space weather is the response of our space environment to the constantly changing Sun. As the new technology advances, mankind has become more and more dependent on space system, satellite-based services. A geomagnetic storm, a disturbance in Earth's magnetosphere, may produce many harmful effects on Earth. Solar flares and Coronal Mass Ejections (CMEs) are believed to be the major causes of geomagnetic storms. Thus, establishing a real time forecasting method for them is very important in space weather study. The topics covered in this dissertation are: the relationship between magnetic gradient and magnetic shear of solar active regions; the relationship between solar flare index and magnetic features of solar active regions; based on these relationships a statistical ordinal logistic regression model is developed to predict the probability of solar flare occurrences in the next 24 hours; and finally the relationship between magnetic structures of CME source regions and geomagnetic storms, in particular, the super storms when the index decreases below -200 nT is studied and proved to be able to predict those super storms. The results are briefly summarized as follows: (1) There is a significant correlation between magnetic gradient and magnetic shear of active region. Furthermore, compared with magnetic shear, magnetic gradient might be a better proxy to locate where a large flare occurs. It appears to be more accurate in identification of sources of X-class flares than M-class flares; (2) Flare index, defined by weighting the SXR flares, is proved to have positive correlation with three magnetic features of active region; (3) A statistical ordinal logistic regression model is proposed for solar flare prediction. The results are much better than those data published in the NASA/SDAC service, and comparable to the data provided by the NOAA/SEC complicated expert system. To our knowledge, this is the first time that logistic regression model has been applied in solar physics to predict flare occurrences; (4) The magnetic orientation angle [theta], determined from a potential field model, is proved to be able to predict the probability of super geomagnetic storms (Dst <= -200nT). The results show that those active regions associated with |[theta]| < 90 are more likely to cause a super geomagnetic storm."],"dc:identifier":["https://digitalcommons.njit.edu/dissertations/854"],"dc:subject":["Solar magnetic field","Solar flares","Space weather","Coronal mass ejection","Gepmagnetic storm","Forestry","Other Physics"],"dc:title":["Automatic prediction of solar flares and super geomagnetic storms"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Federated Physics Department"],"thesis:degree_name":["Doctor of Philosophy in Applied Physics - (Ph.D.)"]},"updated_at":"2026-07-24T03:23:09Z"}