Researchers from North Carolina State University have developed a new method for forecasting seasonal hurricane activity that is 15 percent more accurate than previous techniques. “This approach should give policymakers more reliable information than current state-of-the-art methods,” says Dr. Nagiza Samatova, an associate professor of computer science at NC State and co-author of a paper describing the work. “This will hopefully give them more confidence in planning for the hurricane season.” Conventional models used to predict seasonal hurricane activity rely on classical statistical methods using historical data. Hurricane predictions are challenging, in part, because there are an enormous number of variables in play – such as temperature and humidity – which need to be entered for different places and different times. This means there are hundreds of thousands of factors to be considered. The trick is in determining which variables at which times in which places are most significant. This challenge is exacerbated by the fact that we only have approximately 60 years of historical data to plug into the models. But now researchers have developed a “network motif-based model” that evaluates historical data for all of the variables in all of the places at all of the times in order to identify those combinations of factors that are most predictive of seasonal hurricane activity.
Visit Website | Image credit: Thinkstock