The Error Cone and Visualizing Uncertainty

Tropical Storm Karen Advisory 3

The National Hurricane Center’s 3rd advisory issued for Tropical Storm Karen.

When we’re kids, one of the first subjects in which we learn the concepts of probability and uncertainty is the weather. It’s perhaps the only area of our life in which we all use probabilistic models on a daily basis to guide our decisions—decisions that can come back to bite us. It’s one thing when Nature decides to deliver on that 10% chance of rain; it can be catastrophic when a hurricane makes good on a 10% chance landfall.

In a post last week, I wrote about conveying uncertainty in exoplanet detection—a matter of curiosity. But conveying uncertainty in a hurricane’s predicted track is a matter of public safety. So it would make sense for the National Hurricane Center to take great pains in communicating uncertainty to the public. Its method of visualizing it is known as the “error cone.”

Originating at the current location of the hurricane’s center, it expands along the predicted path to show how the forecasted path becomes more uncertain in the longer term. To be specific, the edge of the cone represents a 67% chance that the hurricane remains inside the cone based on the accuracy of the past five years of forecasts.

But there are some well-known issues with the error cone. For starters, it can give the false impression that it represents the extent of the storm itself, not the extent of its predicted track. Interpreted that way, it seems that the storm expands over time. Another is that by drawing a hard line in the sand at the 67% contour, it gives people just outside the cone a false sense of security, despite the fact that there’s a 1-in-6 chance the hurricane will deviate outside of the cone towards them. (If you’re wondering why it’s not 1-in-3, it’s there’s also a 1-in-6 chance it goes outside the cone on the other side.)

The issue is that a hurricane’s predicted path isn’t a probability—it’s a probability distribution. Some places are more probable than others to lie along the path, but there’s no clear-cut boundary. Choosing an arbitrary 67% contour is convenient, but it’s an awful way to convey the full distribution of possible tracks.

A team of scientists led by Jonathan Cox of Clemson University recently published an alternative method of visualizing a hurricane’s predicted path that looks like this:

What they’ve done is simulate the hurricane’s path hundreds of times, but rigged the simulation’s settings so that it should have the same statistical distribution as the error cone. It’s a bit like loading dice. There’s an element of randomness in each track, but after generating hundreds of tracks, they cluster around the original, predicted track. They also check after each track to make sure the overall set is similar to the error cone. If they’re making too many tracks outside the error cone, they reset the simulations so it will make more inside of it. It’s another application of Monte Carlo models.

The authors don’t claim to have evidence yet that this method leads to a more accurate public perception. (I can think of one possible objection: since the tracks must necessarily diverge, the decreased density makes the tracks appear fainter, which could give a false impression that the storm will get weaker.) But they do report results from a small focus group in their study and found that almost all preferred their new method: in addition to giving a better sense of the dynamic nature of hurricane tracks, it was also simply more visually interesting.


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