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Breakdown: Why spaghetti plots can be useful

Published: Sep. 18, 2021 at 3:44 AM CDT|Updated: Sep. 18, 2021 at 3:48 AM CDT
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MEMPHIS, Tenn. (WMC) - During hurricane season you have probably seen the spaghetti models. Sometimes all the models are going in the same direction and other times it looks like a whole bunch of noodles are going every which way. When these spaghetti noodles are all clustered in the same area and direction, forecast confidence is high.

While spaghetti plots can be very helpful, there are some hang ups when it comes to the plots. They don’t show impacts of a storm like rainfall, wind, or storm surge abut they do show where and when systems will track, which are important too.

The models that are shown on a spaghetti plot don’t derive at the results the same way. The models are based on different things like statistics, based on atmospheric dynamics, and others are based entirely on climatology and persistence of the atmospheric environment.

Pure statistical models such as the CLP5 uses weather from the past while the XTRP uses the most recent storm movement and will extend the storm’s movement out to five days and it is always a straight line. Yet other models, referred to as TABs (or Trajectory and Beta models) follow the winds.

The statistical-dynamical weather models combine statistics like the location of the storm, time of year and past hurricanes behavior along with the steering flow. Even more complex than the statistical-dynamical weather models are the dynamical weather models, which use the current state of the atmosphere using observations from the ground, ocean, and air, as well as complex physics equations, to forecast. You may be familar with some of the names of these types of models which include the American Global Forecast System (GFS), and the hurricane models (HWRF and HMON) and there are many others.

There is another set of models that can also be useful and they are referred to as ensembles. Ensembles are made up of over 50 weather models with various levels of experience and different levels of accuracy.

One major advantage spaghetti models have is when most of the models overlap, this is a big confidence booster for forecasters because models have the same idea, even if they are getting to it in various ways.

Another confidence booster is consistency between forecast model runs. When the model is shown several times a day and with each time the models show similar ideas and stay consistent with those ideas, it can be helpful for forecasters. When models change from run to run, this means that either the atmosphere is changing or the model does not have a good handle on what’s happening.

However these models that you see on the spaghetti plot can’t be relied on completely but they are very useful.

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