Machine Learning Could Improve Hurricane Prediction - Liwaiwai

Applying a machine learning technique to a group of possible storm paths could help meteorologists provide more accurate medium-term hurricane forecasts. This approach could also help them issue timely warnings to communities in the path of these potentially deadly storms, report researchers. In a new 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. "…WHEN YOU ARE FACING A HURRICANE, IT'S IMPORTANT TO KNOW WHAT TYPE OF STORM YOU'RE GOING TO GET--AND WHEN YOU'RE GOING TO GET IT." Scientists use these ensemble models because weather is highly complex and trying to forecast even a single event creates huge amounts of data, says Jenni Evans, professor of meteorology and atmospheric science and director of the Institute for Computational and Data Sciences at Penn State. "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," says 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," she says.