 You’ve probably heard it before… “It must be nice to have a job where you’re wrong half the time”. Or, when it rained when it wasn’t supposed to or there was more snow than expected. Was the weather person wrong again? Let me ask you, what would be your definition of “wrong”? If it didn’t rain at your house, but it rained at your neighbor’s house a few blocks away, would the forecast for tomorrow necessarily be wrong? Or take a thunderstorm 5 miles away. Is every single forecast going to be right? No. Imagine you have 5 kids. Each morning (Monday-Thursday), they request juice for breakfast. You get 3 requests for apple juice and 2 requests for orange juice. Knowing this, you might write a set of mathematical equations to figure out how much juice you need for the week (i.e. Orange juice = ⅖ x Number of Days). As Friday morning rolls around and with my model in hand, you pour 2 glasses of orange juice and 3 glasses of apple juice, thinking your model has predicted the correct outcome. Then, the unthinkable happens. One child comes down for breakfast and wants orange juice today instead of apple juice. Does this mean that your model is wrong? You might be surprised to know that weather forecasts are accurate about 80% of the time. Over a 24 hour period, that averages between 90-94%. Let’s compare this number to sports. For number’s sake, meteorologists are batting around 80% or .800. Who has been the best hitter in Major League Baseball this past season? x x of the x x. His batting average: . ( %). What does that mean? It means he hit the ball % of the time he got to bat. He was “wrong” x% of the time. And he’s the BEST batter in baseball! Let’s take it a step further: if the Patriots go all season with a “good” record, make it to the playoffs, and lose in the Super Bowl. Are they a bad team? No, they made one mistake, but it was a big one that cost them the Super Bowl.  2021 AFC Standings (I80 Sports Blog) Meteorologists can (and do) the same thing. The storm “shifted further south” or the cold front was “stronger than we thought”. Let’s think of football players like variables in the atmosphere. You practice and run scrimmages, but when it comes to the big game on Sunday, other factors may come into play and expectations might be missed. Sure, the coach can blame the quarterback for running a different play or the running back not getting far enough along on the field. Investors, economists, sports analysts, doctors, and politicians all make forecasts. Now, if you ask me if it’s going to rain right now… PoP or the “Probability of Precipitation” describes the chance of rain occurring at any point in a selected area. This is defined as: PoP= C (the confidence that precipitation will occur somewhere in the forecast area) x A (the percent of the area that will receive measurable precipitation). A college basketball player during “March Madness” may hit his free throws 97% of the time, but he will be judged if he misses the 1 shot that cost his team a chance at the Sweet 16. Armchair forecasters tend to remember the few negative outcomes compared to the numerous positive ones. In 2005, the National Weather Service made a near perfect forecast of Hurricane Katrina, anticipating its exact landfall 60 hours in advance. As for Hurricane Laura in 2020, people praised the fact that the storm went off course, saving several parishes from the devastating storm surge that was forecast. Forecasting has changed considerably since the UK’s first earliest published forecasts in 1861 by Royal Navy Officer Robert Fitzroy began publishing forecasts even 30 years ago. Fitzroy would draw weather charts using observations for a select number of locations based on how weather evolved in the past. But, his forecasts were often wrong and the press was quick to criticize. Supercomputers helped pave the way for modern forecasting in the 1950s, with a spacing over 750 km. Today’s models are much more complex and predict more variables.Think of variables in the atmosphere like a bracket- we know their “ranking”, who is most likely to win, and how they’ve “played” in the past. You start off with a global “snapshot” of the atmosphere at a given time, mapped out onto three-dimensional gridded points that span the entire globe and stretch into the stratosphere (and sometimes higher than 33,000 ft). Using a supercomputer and several equations, the snapshot is stepped forward in time, producing raw forecast data. It is then up to the human forecasters to interpret the data and transform it into something meaningful for the general public. The atmosphere is a chaotic system, one small change in the initial conditions can have consequences over time and downstream from a location. This is also known as the butterfly effect. Forecasts out to 3 days are considered precise. But, with data constantly changing, some forecasts need to be updated multiple times a day. It’s easy to also forget that the atmosphere above our heads is a river of moisture. It’s always flowing, moving, changing. If a storm track changes by even 50-60 miles, that could mean the difference between two inches and a foot of snow! Just a small change in available moisture in the atmosphere can also mean a big difference. If the air temperature changes by a few degrees, the liquid to snow ratio can change. If one inch of rainfall equals about 10 inches of snow and the air temperature is at 32 degrees, a Chinook wind rolling through northern Colorado may warm the air and produce less snowfall. Forecasts for big storms can still be a roller coaster, suddenly shifting tracks or intensity. Forecasters in the U.S. routinely examine several models. Models have their pitfalls and some do better with various scenarios (i.e. short-term vs. flat terrain). As one lead forecaster in the Juneau, Alaska office put it in a forecast discussion that went viral, “Picking a model of choice for the day is a little like speed-dating: Too little time/information to make up the mind leading to regrets by the end of the date/shift,” the forecaster wrote, before launching into a comparison of various models and what they see in Southeast Alaska’s developing weather systems.” The forecaster eventually decides to date/choose the European (ECMWF) weather model, but wonders if the North American Mesoscale (NAM) model might be the better choice.  To learn more about the history of weather forecasting, please click here!
©2023 Meteorologist Sharon Sullivan References:
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