Perhaps because chaos theory has been a part of meteorological thinking for nearly four decades, professional weather forecasters have become comfortable treating uncertainty the way a stock trader or poker player might. When weather.gov says that there’s a 20 percent chance of rain in Central Park, it’s because the National Weather Service recognizes that our capacity to measure and predict the weather is accurate only up to a point. “The forecasters look at lots of different models: Euro, Canadian, our model — there’s models all over the place, and they don’t tell the same story,” Ben Kyger, a director of operations for the National Oceanic and Atmospheric Administration, told me. “Which means they’re all basically wrong.” The National Weather Service forecasters who adjusted temperature gradients with their light pens were merely interpreting what was coming out of those models and making adjustments themselves. “I’ve learned to live with it, and I know how to correct for it,” Kyger said. “My whole career might be based on how to interpret what it’s telling me.”
Despite their astounding ability to crunch numbers in nanoseconds, there are still things that computers can’t do, contends Hoke at the National Weather Service. They are especially bad at seeing the big picture when it comes to weather. They are also too literal, unable to recognize the pattern once it’s subjected to even the slightest degree of manipulation. Supercomputers, for instance, aren’t good at forecasting atmospheric details in the center of storms. One particular model, Hoke said, tends to forecast precipitation too far south by around 100 miles under certain weather conditions in the Eastern United States. So whenever forecasters see that situation, they know to forecast the precipitation farther north.
But there are literally countless other areas in which weather models fail in more subtle ways and rely on human correction. Perhaps the computer tends to be too conservative on forecasting nighttime rainfalls in Seattle when there’s a low-pressure system in Puget Sound. Perhaps it doesn’t know that the fog in Acadia National Park in Maine will clear up by sunrise if the wind is blowing in one direction but can linger until midmorning if it’s coming from another. These are the sorts of distinctions that forecasters glean over time as they learn to work around potential flaws in the computer’s forecasting model, in the way that a skilled pool player can adjust to the dead spots on the table at his local bar.
Among the National Weather Service’s detailed records is a thorough comparison of how well the computers are doing by themselves alongside the value that humans are contributing. According to the agency’s statistics, humans improve the accuracy of precipitation forecasts by about 25 percent over the computer guidance alone. They improve the temperature forecasts by about 10 percent. Humans are good enough, in fact, that when the organization’s Cray supercomputer burned down, in 1999, their high-temperature forecasts remained remarkably accurate. “You almost can’t have a meeting without someone mentioning the glory days of the Cray fire,” Kyger said, pointing to a mangled, half-burnt piece of the computer that was proudly displayed in the office where I met him. “If you weren’t here for that, you really weren’t part of the brotherhood.”