Conference on Intelligent Data Understanding 2010 (CIDU)

UNDERSTANDING SEVERE WEATHER PROCESSES THROUGH
SPATIOTEMPORAL RELATIONAL RANDOM FORESTS

AMY MCGOVERN, TIMOTHY SUPINIE, DAVID JOHN GAGNE II, NATHANIEL TROUTMAN,
MATTHEW COLLIER, RODGER A. BROWN, JEFFREY BASARA, AND JOHN K. WILLIAMS

Abstract. Major severe weather events can cause a significant loss of life and property. We
seek to revolutionize our understanding of and ability to predict such events through the mining
of severe weather data. Because weather is inherently a spatiotemporal phenomenon, mining
such data requires a model capable of representing and reasoning about complex spatiotemporal
dynamics, including temporally and spatially varying attributes and relationships. We introduce
an augmented version of the Spatiotemporal Relational Random Forest, which is a Random Forest
that learns with spatiotemporally varying relational data. Our algorithm maintains the strength
and performance of Random Forests but extends their applicability, including the estimation of
variable importance, to complex spatiotemporal relational domains. We apply the augmented
Spatiotemporal Relational Random Forest to three severe weather data sets. These are: predicting
atmospheric turbulence across the continental United States, examining the formation of tornadoes
near strong frontal boundaries, and understanding the translation of drought across the southern
plains of the United States. The results on such a wide variety of real-world domains demonstrate
the extensive applicability of the Spatiotemporal Relational Random Forest. Our long-term goal
is to significantly improve the ability to predict and warn about severe weather events.