Why do meteorologists talk about spaghetti during hurricane season?


AUSTIN (KXAN) – As we all know, it has been a truly active and record-breaking hurricane season as we continue to monitor newly named storms out in the Atlantic. We have now seen 27 named storms so far, and there is still more than a month of hurricane season left.

Have you ever heard your favorite meteorologist mention spaghetti during hurricane season? Well, we aren’t talking about our preferred Italian restaurant.

When a meteorologist is trying to predict the path of a hurricane or tropical storm, we look at several different models that show possible paths. When several of these paths are plotted out they appear like noodles tossed on a map. Hence the name, “spaghetti models.”

So how do these models come up with these paths?

It starts with collecting data. Humidity, temperature, wind speed and pressure are all examples of observed data that is being collected. Ocean buoys, ships and satellites help us record this for areas oversea, and weather stations instruments for areas over land. And we aren’t just talking about conditions at the surface. Aircrafts, and weather balloons help us gather data in the upper atmosphere.

So what happens now with this absurd amount of collected observational data?

It gets stored in a supercomputer which calculates several different computer models. You might hear a meteorologist talk about some of these models — like the GFS, American Model or the EURO model. Each model looks at the data differently and uses different equations to calculate a forecast. Supercomputers can make up to 200 quadrillion calculations per second, which means they can make a whole lot of predictions in a short amount of time, and determine several different paths for a storm.

That brings us back to the spaghetti models. You’ll notice it looks very similar to the shape of the National Hurricane Center’s cone of uncertainty, which is their way of illustrating spaghetti models.

The tighter the model spread (the thinner the cone) means we have a higher confidence in the forecast, but if the models or cones appear wider, that means our confidence in the forecast is lower.

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