Spatiotemporal Methodologies and Analytics for Extreme Weather Study – Using Dust Storm Event as an Example

Authors: Manzhu Yu*, George Mason University
Topics: Geographic Information Science and Systems
Keywords: spatiotemporal data framework, dust storm, feature tracking, 4D, simulation
Session Type: Paper
Day: 4/14/2018
Start / End Time: 8:00 AM / 9:40 AM
Room: Napoleon B1, Sheraton 3rd Floor
Presentation File: No File Uploaded

Dust storm represents a serious hazard to health, property, and the environment in arid and semi-arid areas. To mitigate the hazardous impact of dust storms, it is crucial to detect an upcoming dust event and predict its evolution to inform the early warning and decision-making process. Various dust models have been developed in the past decades to predict dust storms to provide valuable information for early warning. A series of challenges associated with dust modeling and the computational analysis of dust simulation was determined via a critical review of existing approaches in the literature. This research highlights the problems associated with dust model uncertainty, challenges of automatically identifying dust features, tracking the evolution pattern of dust events, and challenges in spatiotemporal data framework. The result of this research is a ready-to-use application, visualizing dust events over space and time in a graphic user interface. These algorithms provide insight on how dust processes transport in the vertical dimension, helping meteorologists better understand the dust process and forecasters to test hypotheses and enhance dust prediction capabilities. In addition, policymakers can use the derived information to mitigate the impact on the population that are particularly vulnerable to airborne and respiratory diseases, and better determine whether a disease outbreak is the causal effect of transported dust. With the intuitive derived information, the public can better prepare and protect their health, avoid traffic accidents and shelter personal assets.

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