UNIVERSITY PARK, Pa. — When a hurricane approaches, providing a few extra hours’ notice can be the difference between life and death. Now, Penn State researchers report that applying a machine learning technique to a group of possible storm paths could help meteorologists provide more accurate medium-term forecasts and issue timely warnings to communities in the path of these potentially deadly storms.

In a study, the researchers used machine learning to remove certain groups of hurricane predictions from ensembles — sets of predictions from weather models that are based on a range of weather possibilities — to lower errors and improve forecasts four to six days ahead. Scientists use these ensemble models because weather is highly complex and trying to forecast even a single event creates huge amounts of data, said Jenni Evans, professor of meteorology and atmospheric science and director of the Institute for Computational and Data Sciences.

"The models are run slightly differently many, many times to create an ensemble of possible future states of the atmosphere. It’s this ensemble that is given to the forecasters,” said Evans. “We’re looking at 120 different forecasts at every time around the globe, then focusing on an individual typhoon or hurricane and asking, 'What will this storm do in the future?’ Now, if you give those predictions to a forecaster only a few hours before their forecast goes live, that's a huge amount of information to process. So, instead, we've used advanced statistics and machine learning to try to break down those 120 forecasts into between four and six clusters where each cluster represents a distinct prediction of the evolution of the storm from all of the other clusters."

Read the full story here: Application of machine learning can optimize hurricane track forecast