Date of Award
3-9-2009
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Engineering Physics
First Advisor
Ariel Acebal, PhD
Abstract
A small fraction of solar flares are accompanied by high energy (>10 MeV) protons. These events can cause degradation or failure of satellite systems and can be harmful to humans in space or in high altitude flight. For risk management purposes, the Air Force is interested in predicting these events. Several algorithms exist to do this operationally, but none predict when these events will occur with much accuracy. Here, we analyzed 3610 M1 and greater flares including 106 with proton events from the GOES sensors from 1 Jan 1986 to 31 Dec 2004 to produce new results, including a full scale comparison and optimization for all the algorithms. In every case, optimization leads to increased prediction ability. This research also produced a new algorithm based on the Garcia algorithm, which functions better than any other operational algorithm. This model, Garcia 2008, predicts with a skill score of .526, an improvement from .342. This new model is the best at prediction of all models measured.
AFIT Designator
AFIT-GAP-ENP-09-M09
DTIC Accession Number
ADA495840
Recommended Citation
Spaulding, Jonathan C., "Predicting Solar Protons: A Statistical Approach" (2009). Theses and Dissertations. 2442.
https://scholar.afit.edu/etd/2442
Included in
Statistical, Nonlinear, and Soft Matter Physics Commons, Structures and Materials Commons