North Carolina State University (NCSU) researchers report that their new method for forecasting seasonal hurricane activity is 15 percent more accurate than previous techniques.
Hurricane prediction is challenging because there are hundreds of thousands of factors to consider, as variables such as temperature and humidity need to be entered for different places and times. Experts also must determine which variables at which times and in which places are most significant, and they only have about 60 years of historical data to plug into models.
The NCSU team say they 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. The groups of key factors are then entered into a program to create statistical models that present the hurricane activity for the forthcoming season on a probability scale.
The network model also enabled the team to confirm previously identified predictive groups of factors as well as identify several new predictive groups.
From NCSU News
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