Hurricane Ian: What are ‘spaghetti models’? How do you read them?

By June each year, professional and amateur meteorologists start looking to the tropics and colorful squiggly lines on maps that could mean a storm is heading your way.

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Nature delivers the storm, but science delivers the lines. The squiggly lines, popularly called spaghetti models, are an important tool as it turns out, letting professional meteorologists gather information about various aspects of tropical systems.

What do they do, how do they work and what can you learn from them? Here’s what we know.

What are they?

Each of the colored lines represents one prediction for the path the storm could take. Computers use data from satellites, historical tracks, temperature readings, wind patterns and other information to create a likely path for the storm.

The lines represent different paths created using different types of information. For instance, a computer model may use water temperature to create a possible path for a storm, and another may use wind patterns to come up with another possible path.

How do you use them to track storms?

A forecast is made by combining the different forecasts from a collection (or “ensemble”) of models.

Professionals who track storms urge the amateur storm watcher to use the spaghetti models with a grain of salt. Because they are created in different ways using different pieces of information, they are not necessarily accurate early on in the process.

In fact, if you notice the tracks early after the storm is born, you’ll see the outliers – lines that shoot off from the general cluster of potential forecast paths. Again, those lines represent a computer’s idea of a path depending on the factors it is using to track the storm.

While the lines can give you a general idea of the storm’s movement, it is not until later in the process that the lines converge to form a more direct path for a storm. And even then, they can be wrong.

A hurricane can follow a likely path, but if it grows into a very large system it can create its own set of circumstances that can change the direction, speed or strength of the storm.

And sometimes the data the computer uses creates a scenario that does not include a factor that turns out to be necessary for a correct forecast.

However, generally the more models used, the better the forecast.

“With ensemble models, you can have upwards of 90 forecasts. That helps us see better where a storm may go,” Assistant Professor of Meteorology Stephen Mullens at the University of Florida told The Miami Herald.

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