Date of Award
Master of Science (MS)
This research combines recent technological advances (i.e. Docker software containers, and cloud computing) in conjunction with the WRF model to create a portable NWP educational module. Incorporating the educational module into a senior level numerical methods course at the University of North Dakota allowed for analysis of the economic, policy, and privacy issues associated with using new software/tools. It also allowed for documentation of the benefits of tool usage within the classroom, both positive and negative. Use of these new tools also allowed for greater computational power to be accessed within the university computer labs. While the university labs were fully capable of running this module, Docker was unable to be installed due to security concerns. Through usage of Amazon Web Services cloud computing platform, the security issues associated with Docker were mitigated. Compliance of policy with the University of North Dakota was no issue, due to their policy not restricting the usage of cloud computing within the educational environment. The economic impact that cloud computing has on the university was found to be minor and easily reduced with proper AWS instance monitoring and use of the AWS Educate program. Use of these tools within the educational environment allowed for students to be exposed to hands on usage of NWP models and emerging tools. While these tools are associated with an increase in cost for the university, that cost is minimal in comparison to the workforce preparedness that can be generated from tool exposure. Due to the benefit associated with exposure to these tools, the recommendation of this work is to use the module within a cloud computing environment, such as AWS, to help better equip students for their careers following graduation.
See Jr, Timothy Wayne, "Implementation Of Computational Resources Focusing On Forecast Model Access For Universities" (2018). Theses and Dissertations. 2340.