A scary-looking weather forecast showing a hurricane hitting the Gulf Coast in the second half of June swirled around social media this week—but don’t panic. It’s the season’s first “ghost hurricane.” Similar hype plays out every hurricane season, especially at the beginning: A cherry-picked, worst-case-scenario model run goes viral, but more often than not, will never come to fruition. Unofficially dubbed “ghost storms” or “ghost hurricanes,” these tropical systems regularly appear in weather models — computer simulations that help meteorologists forecast future conditions — but never seem to manifest in real life. The model responsible this week was the Global Forecast System, also known as the GFS or American model, run by the National Oceanic and Atmospheric Administration. It’s one of many used by forecasters around the world. All models have known biases or “quirks” where they tend to overpredict or underpredict certain things. The GFS is known to overpredict tropical storms and hurricanes in longer-term forecasts that look more than a week into the future, which leads to these false alarms. The GFS isn’t alone in this — all models struggle to accurately predict tropical activity that far in advance — but it is notorious for doing so. For example, the GFS could spit out a prediction for a US hurricane landfall about 10 days from now, only to have that chance completely disappear as the forecast date draws closer. This can occur at any time of the year, but is most frequent during hurricane season — June through November. It’s exactly what’s been happening over the past week as forecasters keep an eye out for the first storm of the 2025 Atlantic hurricane season. Why so many ghosts? No weather forecast model is designed in the exact same way as another, and that’s why each can generate different results with similar data. The reason the GFS has more false alarms when looking more than a week out than similar models – like Europe’s ECMWF, Canada’s CMC or the United Kingdom’s UKM – is because that’s exactly what it’s programmed to do, according to Alicia Bentley, the global verification project lead of NOAA’s Environmental Modeling Center. The GFS was built with a “weak parameterized cumulus convection scheme,” according to Bentley. In plain language, that means when the GFS thinks there could be thunderstorms developing in an area where tropical systems are possible – over the oceans – it’s more likely to jump to the conclusion that something tropical will develop than to ignore it. Other models aren’t built to be quite as sensitive to this phenomenon, and so they don’t show a tropical system until they’re more confident the right conditions are in place, which usually happens when the forecast gets closer in time. The western Caribbean Sea is one of the GFS’ favorite places to predict a ghost storm. That’s because of the Central American gyre: a large, disorganized area of showers and thunderstorms that rotates over the region and its surrounding water. The combination of abundant moisture and spin in the atmosphere makes it a prime breeding ground for storms, especially early in and during the peak of hurricane season. Given the model’s sensitivity, it’s quick to pounce on these possible storms. But this sensitivity has an advantage: By highlighting almost anything that could become tropical, the GFS misses very few actual storms. Its tendency to cry wolf isn’t ideal, but the GFS team found it was worth giving the model a higher chance at catching every storm and better predicting each one’s intensity than to prioritize fewer false alarms, Bentley explained. “It was critical to improve the probability of detection of tropical cyclone formation and tropical cyclone intensity forecasts… and we did achieve that,” Bentley said. During the 2024 hurricane season, the GFS had the least error when forecasting the intensity of tropical cyclones – tropical depressions, tropical storms and hurricanes – of any other global forecast models in its class: the ECMWF, the CMC and the UKM. However, the ECMWF and UKM outperformed the GFS in tropical track forecasts out more than five days in the future. Ghosts still help predict real threats Despite how often the GFS conjures ghost systems in its longer term forecasts, it can’t be discounted. “The crucial role of the forecaster is to understand a model’s known biases and use that knowledge to their advantage to produce a better forecast,” Bentley said. Knowing the GFS latches onto anything that could even vaguely become tropical well in advance helps forecasters keep an eye on areas where conditions may ultimately come together to create the next hurricane. The more reliable solution for predicting tropical behavior more than a few days in advance is to take advantage of ensemble forecasting, according to Bentley. “A deterministic model like the GFS produces one forecast at a time; it gives one answer,” Bentley explained. “An ensemble forecast can show you a variety of possible outcomes, as well as which forecast looks like a possible outlier.” Unlike social media clickbait, no well thought out forecast is made from a single model run. Forecasters use everything at their disposal – deterministic and ensemble models, observations, climatology and more – to predict weather as accurately as possible to give people the time and information they need to stay safe. The National Hurricane Center, for example, typically uses a blend of different types of models to make their forecasts. That strategy, combined with extensive expertise, led to their most accurate track forecasts on record for the Atlantic last season.
There’s a ‘ghost hurricane’ in the forecast. It could help predict a real one
TruthLens AI Suggested Headline:
"Forecast of 'ghost hurricane' highlights challenges in tropical storm prediction"
CNN
9.1
TruthLens AI Summary
The recent weather forecast depicting a hurricane approaching the Gulf Coast in late June has stirred concern on social media, but experts are advising against panic. This forecast is an example of a 'ghost hurricane,' a term used for tropical systems that appear in weather models but rarely materialize. The model behind this particular prediction is the Global Forecast System (GFS), operated by the National Oceanic and Atmospheric Administration (NOAA). While the GFS is a widely used forecasting tool, it has a known tendency to overpredict tropical storms and hurricanes, especially when forecasting beyond a week. This pattern often leads to false alarms, particularly during hurricane season, which runs from June to November. The GFS's propensity to generate these ghost storms is attributed to its design, which makes it more sensitive to potential storm formations compared to other models, such as the European Centre for Medium-Range Weather Forecasts (ECMWF) or Canada's CMC model.
The GFS's sensitivity allows it to predict tropical storms that might otherwise go unnoticed, contributing to its ability to catch actual storms as they develop. Alicia Bentley, a NOAA expert, explains that while this can result in many false alerts, it is a calculated trade-off aimed at improving the model's detection capabilities. The GFS has shown the least error in forecasting the intensity of tropical cyclones compared to its peers during the last hurricane season. However, for accurate long-term track forecasts, forecasters often rely on ensemble forecasting, which considers a range of potential outcomes rather than a single model's prediction. This multifaceted approach, incorporating various models and expert insights, is critical for generating reliable forecasts and ensuring public safety during hurricane season.
TruthLens AI Analysis
The article sheds light on the phenomenon of "ghost hurricanes," a term used to describe weather predictions that show potential hurricanes that never actually materialize. It highlights the tendency of certain weather models, particularly the GFS, to generate alarmist forecasts that often go viral but do not correspond to real-world events. This phenomenon is especially notable at the beginning of hurricane season, which can lead to unnecessary panic among the public.
Purpose Behind the Publication
The intent behind sharing this article seems to be to educate the public about the nature of weather forecasting models, particularly their limitations. By clarifying that ghost hurricanes are common and often misleading, the article aims to mitigate public anxiety regarding impending storms. This serves to foster a more informed understanding of meteorological predictions and the science behind them.
Public Perception and Implications
The article likely seeks to promote a sense of calm among communities that may be worried about potential hurricanes. By framing the issue as a common occurrence, it helps to reduce fear and encourages people to rely on established meteorological expertise rather than sensationalized social media reports. This could also indirectly support trust in weather forecasting institutions by highlighting the complexities involved in making accurate predictions.
Hidden Agendas or Information
There appears to be no significant hidden agenda in this article. Instead, it genuinely focuses on educating the public about misleading forecasts, which is beneficial in helping people navigate the often sensationalized nature of weather reporting. However, underpinning this could be a broader motive to improve public literacy around science and data interpretation, especially as it pertains to climate-related concerns.
Manipulative Aspects
The article maintains a neutral tone, focusing on factual information rather than sensationalism. Its approach minimizes the risk of manipulation, as it avoids alarmist language and instead provides context about the forecasting models' limitations. The language used is straightforward and aims to inform rather than incite panic.
Comparison with Other Reports
When compared to other articles on similar topics, this one stands out by prioritizing educational content over sensationalism. While other reports may focus on the potential devastation of hurricanes, this article emphasizes the science of forecasting, which could suggest a shift towards a more responsible reporting style in the industry.
Impact on Society and Economy
An accurate understanding of weather phenomena like ghost hurricanes can play a crucial role in disaster preparedness and response strategies. If the public is better informed about the nature of these forecasts, they may be less likely to engage in panic buying or other behaviors that can disrupt local economies during hurricane season.
Community Responses
The article likely resonates more with scientifically-minded communities or individuals who appreciate data-driven discussions. It could also attract attention from those interested in meteorology or environmental studies, as it delves into the intricacies of weather forecasting.
Market and Economic Influence
While this article may not have a direct impact on stock markets or specific stocks, it emphasizes the importance of accurate weather forecasting for industries such as agriculture, insurance, and tourism. Companies in these sectors may benefit from improved public understanding of weather predictions.
Geopolitical Relevance
In terms of global power dynamics, the article indirectly touches on the importance of robust meteorological systems, which can be crucial for disaster preparedness and response. As climate change continues to influence weather patterns, countries with advanced forecasting capabilities may have a strategic advantage in managing natural disasters.
AI Involvement in Writing
While it is possible that AI tools were used in crafting the article, the style and content suggest a human touch, particularly in the nuanced explanations provided about the forecasting models. If AI were involved, it might have assisted in data organization or language refinement but likely did not dictate the narrative.
Final Thoughts
The article serves as a timely reminder of the complexities and limitations of weather forecasting, especially regarding hurricanes. Its educational tone and focus on factual information contribute to a more informed public discourse on an increasingly critical issue. Overall, the article is reliable, presenting data in a clear and comprehensible manner.