Meteorologists often take a lot of flack when their weather predictions aren’t accurate–indeed it can be very annoying to cancel a big event for a blizzard that never materializes, or worse, very dangerous if an unexpected storm comes up while you are out hiking. Despite its foibles, though, weather forecasting has come a long way from its early roots of divining celestial signs and following limericks (“red sky at night, sailors delight; red sky in morning, sailors take warning”) to become a truly scientific endeavor.
As in any scientific field, uncertainty is inherent in the data that predict the path of a hurricane or separate “clear skies” from “chance of thunderstorms.” But the accuracy of weather forecasts has greatly improved over the last century, thanks in large part to advances in computer modeling.
Early efforts to predict the weather mathematically, by people like Vilhelm Bjerknes and Lewis Fry Richardson, highlighted the need for serious computational power to begin even approaching the level of complexity found in Earth’s atmosphere. Efforts to make weather prediction scientific, and the recognition that propagating a tiny error across a series of weather calculations could have a huge cumulative impact on the results, also spawned the field of chaos theory. Chaos theory, which includes the popular notion of “the butterfly effect,” aims to understand underlying patterns of behavior in complex systems (like weather) and to quantifying the uncertainty.
Strides in the accuracy of weather prediction are an everyday example of the scientific method in action. Modern meteorologists–as well as scientists who model long-term global climate systems–still contend with uncertainty, complexity, the limits of technology, and human errors. But through the iterative process of the scientific method (and aided by faster, more powerful computers), they are continuously fine-tuning their models and predictions. That’s good news for science and for anyone who wants to plan a picnic or avoid a lightening strike.
Venture inside one of the modern nerve centers for weather forecasting, the National Centers for Environmental Prediction, in Nate Silver’s New York Times Magazine piece “The Weatherman is Not a Moron.”
Learn about some of the first computer models, which were developed for weather prediction in our module Research Methods: Modeling
Read about how weather forecasting led to the rise of chaos theory in our module Data: Uncertainty, Error, and Confidence
Written by Christine Hoekenga
Christine is a freelance writer, editor, and content strategist, specializing in science and nature. She holds an Bachelor's degree in Environmental Science and Media Studies and a Master's of Science Writing. She has been working in science communication and education for nearly a decade as a journalist, an organizer for conservation groups, and a museum educator. Before joining the Visionlearning team, she served as the New Media and Online Community Manager for the Webby award-winning Smithsonian Ocean Portal. Christine is assisting Visionlearning with developing new modules and glossary terms, managing the blog, and outreach through social media.