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Dissertation Defense Announcement for Jie Lian

The Information Technology Doctoral program invites the university community to a dissertation defense for Jie Lian on July 26, 2017 at 11:00 am in Room 459 in the 7800 York Road building.


Committee Chair: Michael McGuire, 410-704-2337, mmcguire@towson.edu


Recent research has shown an increase in the number of extreme tornado outbreaks per year. The characterization of the spatio-temporal pattern of tornado events is therefore a critical task in the analysis of meteorological data. Currently, there are a large number of available meteorological datasets that can be used for such analysis. However, much of this data is distributed across multiple websites and are not accessible in a central location. This poses a significant challenge for a scientist who is interested in exploring meteorological patterns associated with tornado events.

Change detection has also proven to be a very useful approach to mine tornado data. There has been a lot of research on finding the spatial change of a particular tornado event from NEXRAD reflectivity data. However, most tornado events happen over a period of time. Therefore, detecting the spatio-temporal change of tornado events is another challenge for meteorologists.

This dissertation proposes to address these challenges by using cloud-based technology for integrating, storing, exploring, analyzing, and visualizing meteorological data associated with tornado outbreaks. The system employs a novel NoSQL database schema and web services architecture for data integration and provides a user friendly interface that allows scientists to explore the spatio-temporal pattern of tornado events. Furthermore, scientists can use this interface to analyze the relationship between different meteorological variables and properties of tornado outbreaks using a number of spatio-temporal statistical and data mining methods. The efficacy of the system is demonstrated on a use case centered on the analysis of climatic indicators of large spatio-temporally clustered tornado outbreaks.

In addition, three methods are proposed to detect spatio-temporal change. One of the methods is a quadtree-based method, which can generate homogeneous regions and identify the homogeneous regions change over time. The other is a graph-based method, which can identify high intensity storm cells as clusters and track the cluster change during a period of time. The third method is a hybrid method of the first method and the second method. Then, experiments were performed by using a real-world NEXRAD dataset to demonstrate and compare the performance of these three methods by comparing the distance between the analyzed dynamic regions and the tornado events touch down locations. And the results of which are validated by finding interesting tornado event patterns that are explained by known tornado phenomena.

The results of the dissertation suggest that the cloud-base system can be used for meteorologists to explore tornado events and analyze tornado event patterns, and the proposed spatio-temporal change detection methods can be used to characterize the NEXRAD radar signatures associated with tornado touch down locations.