The Forecast Wars on Weather Twitter

For this week’s Infinite Scroll column, Brady Brickner-Wood is filling in for Kyle Chayka.

What you expected from this past weekend’s winter storm likely depended on where you got your weather news. If you watched the Weather Channel or visited the National Weather Service web page, you’d have learned about the “increasing threat for accumulating snow” or “possible” freezing rain in your area, each report hedged with a modicum of uncertainty. Snow and rain can be difficult events to predict because long-range models shift from day to day, and sometimes from hour to hour, with general patterns of pressure, precipitation, air speed, and temperature fluctuating constantly in the run-up to a big storm. This is why professional meteorologists speak in potentialities and probabilities, identifying trends across many different models to determine the likelihood of a given outcome. But probabilities are less sexy than proclamations, ambiguities less attractive than assurances—or so the rising number of storm-hyping accounts on social media seem to suggest. “I’ve looked at EVERY Major Weather Model that exists,” the weather influencer Brady Harris wrote on X, on Friday. “I’ve looked at numbers. I’ve looked at the trends. They all point to 1 THING.” That thing? Snow—and not just any run-of-the-mill snow, but, according to Harris, the “Big Snowstorm we’ve all been waiting for.”

Yes, weather influencers exist, and their accounts—along with those of social-media-driven weather brands—have become increasingly popular thanks to their flair for the dramatic. Compared to their credentialled meteorologist counterparts, engagement-driven accounts run by private weather services and amateur storm chasers tend to exaggerate possibilities and foment hype for anticipated weather events, presenting forecasts as facts and predictions as guarantees. Despite using the same models as the professionals—anyone can freely access National Oceanic and Atmospheric Administration data, which are collected in part by government-owned satellites, planes, weather balloons, buoys, radar systems, and weather stations—private forecasters and solo enthusiasts are not beholden to the American Meteorological Society’s best practices, which caution against overstating certainty and posting raw data without explanation. When a private, subscription-based company such as BAM Weather posts graphics of individual model runs to social media, for instance, the run data may not be indicative of a wider trend or probability; the forecast scenario may vanish from a run conducted only a few hours later. While there is nothing implicitly malicious or unethical about posting individual model data or visuals to social media, there are risks. A single model run may be misinterpreted as an actual meteorological forecast, or, more concerning, an inevitability. If you’re scrolling social media and see a Rothko-like weather map warning of a massive winter storm—surely you’ll take notice.

BAM Weather is a vital member of what can be referred to as “Weather Twitter”; it produces graphics and model data that feed into the intense social-media hype cycle before a big storm. Weather influencers often cite these graphics when making grand statements or predictions about an upcoming storm, sometimes even calling out the model data as inaccurate or biased. Last week, as forecasts for the expected storm over the weekend intensified, BAM posted a trio of model runs that hinted at an updated storm track: “New ECMWF is in. NW again.” On the map, everything north of Tennessee was blanketed in purples and pinks and blues, signalling heavy snow, while everything south was bone white—meaning no snow, not even an inch. Mitch West, a South Carolina-based weather influencer, took umbrage with these selective model runs, writing on X that “BAM must be stopped. He is family. But they have won the battle today. We won yesterday. Tomorrow is a new day. The South must take back what is ours.” For West, who is a storm chaser, snow in the South would be a rare and sacred gift, one that the weekend storm was portending to produce throughout the region. After tracking many meteorological forecasts, he had gone on record anticipating a “long duration winter storm” across the Southeast. But then BAM pulled up to the party with its individual model runs, making inferences that swung the narrative. Tomorrow would produce a new model run, West promised—and it would hopefully show that snow was set to dump on the South.

In this way, Weather Twitter’s various factions and dramas mirror that of professional-sports discourse. Like the weather, there is no surefire way to predict the result of a sporting event or a player’s performance despite the overwhelming amount of data and advanced metrics at the disposal of both fans and professional analysts alike. This does not prevent the sports-media ecosystem from orbiting around prediction-making, an obsession that has only ballooned in the age of legal sports betting. Debate shows, podcasts, and pre-game analyses are dominated by broadcasters and former players projecting unprojectable events with stone-cold confidence; online, sports discourse is fuelled by hot takes and preposterous hypotheses, the whole enterprise a ceaseless pontification of what is yet to come. Picking winners and losers, heroes and villains, pathways to success and failure, generates excitement for an event and manufactures a sense of urgency for maximal viewing pleasure. If an analyst is correct, they can claim intellectual superiority over others in the field; if they’re wrong, they can blame any number of unforeseen forces for the error. Some sports commentators dive deep into the data, running simulations and calculating expected probability outcomes before making their picks. Maybe such meticulous data mining pays off—but maybe not. Many of the most popular sports pundits cast off analytics as superfluous nerd drivel. (Terms like “eye test” and “gut check” are often cited as more reliable metrics.) In other words, these analysts are about as likely to make a correct pick as a corgi on TikTok that predicts sporting-event results by hitting a beach ball into a basket.