EAGER: Improving our Understanding of Supercell Storms through Data Science

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This study seeks to apply novel data science techniques (such as tree-based classification models and deep learning) to four-dimensional (4D) weather radar observations of thunderstorm dynamics to enable identification of storms capable of producing tornadoes up to an hour prior to tornadogenesis. Real-time severe storm prediction is a challenging task that currently requires a human forecaster with a thorough understanding of the dynamics and current state of the atmosphere. This study will develop and apply data science techniques to four-dimensional radar data from severe storms throughout the continental U.S. with the goal of identifying critical spatiotemporal relationships that can improve the understanding and prediction of tornadoes. The long-term goal will be to develop techniques to fundamentally improve our understanding of severe storms in general (including hail, wind, and tornadoes) by analyzing the new knowledge identified by the data science models.This study seeks to advance the scientific knowledge of tornadogenesis by identifying novel precursors to tornadoes in two unique 4D weather radar datasets. Data science has the potential to advance knowledge by processing and objectively evaluating a large amount of data in a relatively short period of time. This provides a mechanism by which large, complicated meteorological datasets can be assessed for their predictive capability or alternative applications without the need for time consuming subjective evaluation. The methods developed will enable others to evaluate existing Earth system data to a spatiotemporal extent that is not possible with established approaches. The application of data science techniques to a novel domain will require the development of new techniques focusing on spatiotemporal 4D weather radar data.