sub header2

[View PDF]


Nagiza Samatova NCSU/ORNL
Fred Semazzi NCSU


  • Develop predictive forecasting methodology for climate extremes (e.g., hurricanes, droughts, rainfalls)
  • Devise scalable algorithms for predictive mining of large-scale climate complex networks
  • Provide mechanistic insights about the key factors contributing to extreme events variability
  • Demonstrate high predictive skill for North Atlantic seasonal hurricane activity


  • Provide policy makers more reliable information on seasonal climate extremes
  • Scalable large-scale graph mining algorithms of broader applicability (e.g., bioenergy)
  • Advance our understanding of the mechanisms that influence hurricane variability and behavior
  • International impact managing meningitis epidemic outbreaks driven by climate extremes

2012 Accomplishments

15 percent more accurate forecast of seasonal hurricane activity
Comparative climate networks analytics & machine learning methods

"Novel data-driven methods promise to excel beyond the traditional methods in climate prediction tools"
(Fred Semazzi, Nobel Prize co-winner, climate scientist)

Z. Chen, W. Hendrix, H. Guan, I. Tetteh, A. Choudhary, F. Semazzi, N. Samatova, "Discovery of extreme events-related communities in contrasting groups of physical system networks," Data Mining and Knowledge Discovery, 27(2), p. 225-258, 2012.