Sura Photo

Associate Professor of Meteorology
Department of Earth, Ocean and Atmospheric Science

Rm. 275

(850) 644-3479 phone
(850) 644-4841 fax

This email address is being protected from spambots. You need JavaScript enabled to view it.

Dr. Sura joined the Department of Meteorology at Florida State University as an Assistant Professor in early 2008 through the Extreme Events in Climate Cluster (which is part of FSU's Pathways of Excellence program). He received his Ph.D. from the University of Hamburg, Germany, in 2000. Before joining Florida State University he worked as a postdoctoral researcher at Scripps Institution of Oceanography, University of California San Diego, and as a research scientist at the NOAA-CIRES Climate Diagnostics Center in Boulder, Colorado.

Dr. Sura's current research is focused on the stochastic-dynamical understanding of extreme events in climate. Extreme events in climate (such as hurricanes, droughts, windstorms etc.) are by definition rare, but they can have a significant impact on affected people and countries. In non-technical terms, an extreme event is a high-impact, hard-to-predict phenomenon that is beyond our normal (Gaussian bell curve) expectations. In technical terms, an extreme event is often defined as the non-normal (non-Gaussian) tail of the data's probability density function (PDF). Understanding extremes has become an important objective in climate variability research because climate (and weather) risk assessment depends on knowing and understanding the non-Gaussian tails of PDFs.

In recent years, new tools that make use of advanced stochastic-dynamical theory have evolved to evaluate extreme events and the physics that govern these events. These tools take advantage of the non-Gaussian structure of the PDF by linking a stochastic (probabilistic) model derived from first physical principles to the observed non-Gaussianity. The detailed assessment of non-Gaussian variability in the atmosphere and the ocean is of great practical significance because it provides a framework to predict the probability of extreme events in the climate system.