Document Type
Dissertation
Date of Award
Fall 1-27-2008
Degree Name
Doctor of Philosophy in Applied Physics - (Ph.D.)
Department
Federated Physics Department
First Advisor
Haimin Wang
Second Advisor
Dale E. Gary
Third Advisor
Andrew Gerrard
Fourth Advisor
Wenda Cao
Fifth Advisor
Li Guo
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 θ, 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 |θ| < 90° are more likely to cause a super geomagnetic storm.
Recommended Citation
Song, Hui, "Automatic prediction of solar flares and super geomagnetic storms" (2008). Dissertations. 854.
https://digitalcommons.njit.edu/dissertations/854
