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The Human Touch
With computers now running the show, what’s left for human forecasters to do?
In terms of day-to-day weather like temperatures, perhaps not much. “For a lot of the routine weather, the forecast models are so good now that there’s really not that much that the human forecasters are going to add,” says Schumacher, who is also an associate professor in the Department of Atmospheric Science at Colorado State University.
But don’t think humans are unnecessary just yet. “A forecaster might tweak what the computer tells you if they know their area really well and they know that models struggle with a certain kind of weather situation,” says Henson.
One such situation is precipitation, which is more challenging to forecast than temperature, says Matt Kelsch, a hydro meteorologist at the University Corporation for Atmospheric Research in Boulder. “Temperature is a continuous field, meaning there’s a temperature everywhere,” he explains. “But precipitation is a discontinuous field, meaning there’s a lot of places there is none, and then some places that it can be raining or snowing very hard.” And local geography — mountain ranges, coastlines or the Great Lakes — can affect precipitation in ways that models may not handle well. Particularly for forecasts within 24 to 36 hours, Kelsch says, a meteorologist’s experience with the forecasting area comes into play.
Forecasting high-impact situations such as hurricanes, tornadoes and floods is more challenging and comes with much higher stakes. “Especially when it comes to extreme weather, human judgment is really important,” Henson says.
What Are the Chances?
The further in the future your picnic is scheduled, the harder it is to predict rain or shine. But since the 1950s, ever faster computers have been producing increasingly accurate weather forecasts. “Many of the world’s largest and most powerful supercomputers are devoted to atmospheric research — to forecasting [weather] and to studying climate change,” Henson says.
According to National Oceanic and Atmospheric Administration, today’s five-day forecast is accurate about 90 percent of the time. The seven-day forecast is correct 80 percent of the time, and a 10-day forecast reflects the weather that actually occurs about 50 percent of the time.
What about major events? Based on National Hurricane Center forecasts since 2010, a hurricane’s eye made landfall, on average, just 47 miles from where a prediction 24 hours earlier said it would. That’s only about one-sixth of an average hurricane’s total size. “Twenty-four hours before a hurricane strikes land, we’ve already pretty much nailed down where it will go,” says Judt. Going out to five days, the error in the forecasts since 2010 is about 220 miles.
These stats are more impressive when you consider how much meteorologists have improved the number of days out to which an accurate forecast can be made. For instance, today’s five-day hurricane forecast is more reliable than the four-day forecast in the early 2000s, and more reliable than a three-day forecast in the 1990s. And a 2015 Nature paper revealed that three- to 10-day forecasts have been improving by about a day per decade — meaning a modern six-day forecast is as accurate as a five-day forecast 10 years ago.
Chaos Rules
As forecasts improve, one question naturally arises: How much better can they get?
Unfortunately, the chaotic nature of our atmosphere seriously limits our ability to model it — and therefore to predict what it will do next. You’ve probably heard that a butterfly flapping its wings in Hong Kong might cause the weather to change in New York. The idea of this “butterfly effect” — in which minuscule changes can have huge impacts on the development of a dynamic system — was coined in 1972 by mathematician and meteorologist Edward Lorenz.
In practice, this means that a single weather model run more than once with even the most subtle differences in starting conditions can produce very different predictions. Since no measurement is perfect — every observation has an associated uncertainty — these small imperfections can cause big changes in what a model predicts. These changes get bigger and bigger the further ahead you try to predict.
Because of this, the potential predictability limit of weather is about two weeks, says Henson. “[Lorenz] essentially said there’s just no way you can predict weather features beyond that time because those little butterfly wing flaps and countless other little things will add up to so many big changes, and there’s so much uncertainty beyond that range, that it’s just impossible to say anything,” he says.
Judt, whose work focuses on the theoretical limit of accuracy in weather forecasting, says we’ll never be able to predict thunderstorms more than a couple of hours in advance, regardless of how good observations become. For hurricanes and winter storms, which are much bigger and therefore easier to spot in advance, the theoretical limit is two to three weeks — “so there’s still a couple of days to be gained, if not a whole week,” he says.
“We could forecast perfectly if we had perfect knowledge of the atmosphere and if we had perfect weather models,” Judt says. But we will never be able to measure everything about every point in the atmosphere all the time with ultimate precision, and our models will never be flawless. “So we will never be able to actually achieve perfect forecasts.”
Building a Better Forecast
There are more ways to improve forecasts than taking better observations and improving our weather models. Understanding how people use forecasts and warnings allows meteorologists to provide information in the most useful way.
One of the biggest challenges for meteorologists is condensing a forecast, which represents a spread of possible weather conditions to expect, into a single icon or a few sentences that appear in your weather app.
Take, for instance, today’s chance of rain in your area. This could mean slightly different things coming from different meteorologists, but in general, it’s not simply the odds that you, personally, will witness rain that day. Most forecasters calculate this number by multiplying their confidence that rain will occur by the area in which the rain might happen. So a 40 percent chance of rain might be a 100 percent chance in 40 percent of your county, or, a 60 percent chance across 70 percent of your county.