Top 10 Greatest Moments in Math History: Avoiding Catastrophe with Chaos

Top 10 Greatest Moments in Math History

Moment 10: Avoiding Catastrophe with Chaos
In 1900, America’s worst natural disaster hit Galveston, Texas in the form of a category 5 hurricane bringing 130 mph winds and a 15 foot storm surge. More than 8,000 people were killed by the storm. Why was it so deadly? There was little advance warning as ship-to-shore communication was not yet possible [18].

With modern radar, we are warned of hurricanes weeks before they make landfall. Radar allows us to see the storm where it is, but it is math that allows forecasters to predict what the hurricane’s path will be. As demonstrated by Ed Lorenz in 1963, “any unmeasured weather on very small space scales can cause huge differences in the forecast the farther out in time one projects the weather” [19]. Ed Lorenz was the mathematician who famously summarized chaos theory with, “a butterfly flapping its wings in Beijing could affect the weather thousands of miles away some days later” [class notes]. This theory was later dubbed “The Butterfly Effect” [class notes]. 

We’ve all seen hurricane prediction maps on television showing us where the next hurricane may hit land. Sometimes the prediction paths are close and forecasters have a pretty good idea where the hurricane will travel. Sometimes though, one or two prediction paths are much different from the rest. Why? Computer-based equations crunch data from weather balloons, satellites, the surface, and aircraft-based measurement instruments and spit out a prediction [19]. Depending on how the data is interpreted, small variations in the initial inputted data can lead to very different outputted predictions. This is due to a “sensitive dependence on initial conditions [19]”, the heart of chaos theory.

Works Cited:

[18] National Weather Association, “National Weather Service Commemorates 1900 Galveston Hurricane”, 2009,

[19] Spencer, Roy W., “Weather, Chaos, and Climate Change”, March 30, 2009,

[20] Hurricane prediction map,

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