{"id":31608,"date":"2018-10-10T22:21:15","date_gmt":"2018-10-10T22:21:15","guid":{"rendered":"https:\/\/one.sightlinemg.com\/navytimes\/uncategorized\/2018\/10\/10\/hurricane-forecasting-has-improved-but-more-must-be-learned-to-perfect-the-science\/"},"modified":"2026-08-08T19:37:45","modified_gmt":"2026-08-08T19:37:45","slug":"hurricane-forecasting-has-improved-but-more-must-be-learned-to-perfect-the-science","status":"publish","type":"post","link":"https:\/\/one.sightlinemg.com\/navytimes\/news\/your-navy\/2018\/10\/10\/hurricane-forecasting-has-improved-but-more-must-be-learned-to-perfect-the-science\/","title":{"rendered":"Hurricane forecasting has improved but more must be learned to perfect the science"},"content":{"rendered":"\n

Hurricane Michael,<\/a> an unprecedented Category 4 hurricane, has made landfall in the panhandle region of Florida.<\/p>\n\n\n\n

Hurricanes can cause immense damage due to the winds, waves and rain, not to mention the chaos as the general population prepares for severe weather.<\/p>\n\n\n\n

The latter is getting more relevant, as the monetary damage from disasters is trending up<\/a>. The growing coastal population and infrastructure<\/a>, as well as rising sea level<\/a>, likely contribute to this increase in costs of damage.<\/p>\n\n\n\n

This makes it all the more imperative to get early and accurate forecasts out to the public, something researchers like us are actively contributing to.<\/p>\n\n\n\n

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A storm chaser climbs into his vehicle during the eye of Hurricane Michael to retrieve equipment after a hotel canopy collapsed in Panama City Beach, Fla., on Wednesday. (Gerald Herbert\/AP)<\/figcaption><\/figure>\n\n\n\n

Hurricane forecasts have traditionally focused on predicting a storm\u2019s track and intensity<\/a>. The track and size of the storm determine which areas may be hit. To do so, forecasters use models \u2013 essentially software programs, often run on large computers.<\/p>\n\n\n\n

Unfortunately, no single forecast model is consistently better than other models at making these predictions. Sometimes these forecasts show dramatically different paths, diverging by hundreds of miles. Other times, the models are in close agreement. In some cases<\/a>, even when models are in close agreement, the small differences in track have very large differences in storm surge, winds and other factors that impact damage and evacuations.<\/p>\n\n\n\n

What\u2019s more, several empirical factors in the forecast models are either determined under laboratory conditions or in isolated field experiments. That means that they may not necessarily fully represent the current weather event.<\/p>\n\n\n\n

So, forecasters use a collection of models to determine a likely range of tracks and intensities. Such models include the NOAA\u2019s Global Forecast System<\/a> and European Centre for Medium-Range Weather Forecasts global models.<\/p>\n\n\n\n

The FSU Superensemble<\/a> was developed by a group at our university, led by meteorologist T.N. Krishnamurti, in the early 2000s. The Superensemble combines output from a collection of models, giving more weight to the models that showed better predicted past weather events, such as Atlantic tropical cyclone events.<\/p>\n\n\n\n

A forecaster\u2019s collection of models can be made larger by tweaking the models and slightly changing the starting conditions. These perturbations attempt to account for uncertainty. Meteorologists cannot know the exact state of the atmosphere and the ocean at the time of the start of the model. For example, tropical cyclones are not observed well enough to have sufficient detail about winds and rain. For another example, the sea surface temperature is cooled by the passage of a storm, and if the area remains cloud-covered these cooler waters are much less likely to be observed by satellite.<\/p>\n\n\n\t