### Social Media Swirls With Season's First "Ghost Hurricane" Forecast

This week, social media buzzed with worrying predictions of a hurricane set to hit the Gulf Coast in late June. However, there's no need to panic just yet—this is likely the season's first "ghost hurricane." Each hurricane season, similar alarming forecasts emerge, only to fade away as mere scare tactics. These "ghost hurricanes" are essentially false alarms that crop up frequently in weather models but rarely evolve into actual storms.

The culprit behind this week's scare is the Global Forecast System (GFS), also known as the American model, which is managed by the National Oceanic and Atmospheric Administration (NOAA). Although the GFS is a widely used tool for meteorologists globally, it is notorious for overpredicting tropical storms and hurricanes when forecasting beyond a week into the future. This tendency to cry wolf is not unique to the GFS—all weather models struggle with predictions that far out—but the GFS is particularly known for it.

For instance, the GFS might predict a US hurricane landfall ten days from now, only for that storm to evaporate from the forecast as the date nears. This phenomenon is especially common during the hurricane season, from June through November. This exact scenario has unfolded over the past week as forecasters monitor the first potential storm of the 2025 Atlantic hurricane season.

Different weather models are designed with varying sensitivities, which is why they produce different results. Alicia Bentley, the global verification project lead at NOAA's Environmental Modeling Center, explains that the GFS was built with a "weak parameterized cumulus convection scheme." Simply put, it means the model is highly sensitive to potential thunderstorm activity, often jumping to the conclusion that a tropical system will develop.

The Western Caribbean Sea is a frequent hot spot for these ghost storms, largely due to the Central American gyre—a swirling area of showers and thunderstorms that creates prime conditions for storm formation. Given this sensitivity, while the GFS may produce more false alarms, it ensures that few genuine threats are overlooked.

Bentley points out that while the GFS's false alarms are not ideal, the model's design aims for a higher probability of detecting real storms and accurately predicting their intensity. Last year, the GFS had the least error in forecasting the intensity of tropical cyclones, compared to the ECMWF, CMC, and UKM models. However, in predicting storm tracks more than five days out, the ECMWF and UKM perform better.

Despite its flaws, the GFS remains a valuable tool for forecasters, who must understand each model's biases to create the most accurate forecasts. Ensemble forecasting, which considers a range of possible outcomes, provides a more reliable method for anticipating tropical weather patterns far in advance. Forecasters, like those at the National Hurricane Center, blend various models and expertise to offer the most accurate predictions, helping people stay safe and informed.