Multispectral Study Of Major Solar Flares Toward Flares Predictions

• Main Author: Roslan, Muhamad Akram Zaki
• Format: Thesis
• Language: English
• Published: 2018
• Subjects: QC1 Physics (General)
• Online Access: http://eprints.usm.my/49577/1/MUHAMAD%20AKRAM%20ZAKI%20BIN%20ROSLAN%20-%20MULTISPECTRAL%20STUDY%20OF%20MAJOR%20SOLAR.pdf; http://eprints.usm.my/49577/
• Institution: Universiti Sains Malaysia

Major solar flares (X-class) are the most dangerous solar activities for the Earth in terms of space weather. It is due to a magnetic eruption and sudden release of intense magnetic energy at active regions (AR). The main objective of this research is to identify AR characteristics (McIntosh and Mt Wilson class) associated with major solar flares from NGDC data and to formulate criteria for its early prediction. These criteria are enhanced by qualitative multispectral images. Multispectral images such as a continuum, magnetogram, and EIT 195Å images from SOHO were used in this research. This study took into account the evolution of AR, properties of AR (Zurich, Penumbra, Compactness and Mt Wilson class), the morphology of magnetic flux rope (MFR), and formation of magnetic sigmoidal shape. Considering data availability and consistency, we chose five AR, namely AR 10486, AR 9393, AR 9415, AR 10720 and AR 9077 were chosen. Each AR generated series of major flares, resulting in a total of 21 flares studied. Images were analysed and interpreted by using ImageJ and Helio Viewer to measure solar flares features. The appearance of magnetic sigmoidal shape can be seen in EIT 195Å images while MFR can be seen in magnetogram images. The prediction criteria state that major flares occur at AR that has Zurich D, E or F, Penumbra class K, Compactness class I and C, and Mt Wilson class BGD. A negative prediction showed AR that will not produce major solar flares with 99.5% accuracy and 98% sensitivity. This prediction method can give early warning for major solar flares occurrences within 1-4 days. By including MFR and sigmoidal shape images, the accuracy of the prediction was improved.