This AI Aced Hurricane Season in 2025. Here’s What That Means

Authors: Ellyn Lapointe

Publisher:

Gizmodo

Published: 11/9/2025

Language:English

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Elon

Good evening Norris, I'm Elon. Welcome to Goose Pod, just for you. Today is Wednesday, November 12th.

Morgan Freedman

And I'm Morgan Freedman. Tonight, we explore a fascinating turn of events: an AI that mastered the 2025 hurricane season.

Elon

It’s not just fascinating, it’s a complete disruption. Google's DeepMind AI, a rookie, absolutely dominated this season's hurricane forecasting. It made the established models look ancient. We’re talking about a massive performance gap that's frankly embarrassing for the old guard.

Morgan Freedman

The contrast is indeed stark. The preliminary analysis shows America’s primary model, the Global Forecast System, was the worst performer. For Hurricane Melissa, it had a five-day error of over 500 miles. An error of that magnitude has profound human consequences.

Elon

Exactly. 500 miles! That’s the difference between a city evacuating and being caught completely unprepared. It’s an unacceptable failure. This isn't just about better tech; it's about saving lives and infrastructure. The AI's precision is a monumental leap forward.

Morgan Freedman

And it reminds us of the stakes. I recall the stories from Hurricane Katrina, twenty years ago now. The confusion, the delayed warnings. Accurate forecasting is not a luxury; it is a shield against the awesome and sometimes terrible power of nature.

Elon

Right, and the old shield was full of holes. For decades, we’ve used physics-based models. They treat the atmosphere like a giant calculus problem, running on incredibly expensive supercomputers. It’s a brute-force method, and as we've seen, it's hitting its limits. It’s slow and prone to errors.

Morgan Freedman

It's a method that has served us, but perhaps its time is passing. These traditional models use simplified equations to approximate the atmosphere's behavior. They have been refined over many years, but they are built on a foundation of approximation, which can introduce flaws.

Elon

That’s the key. AI isn't an approximation; it’s pattern recognition. Think of it as algebra versus calculus. The AI models train on vast amounts of historical data, learning the subtle patterns that precede a storm's movement and intensification. They see the matrix. It's faster, cheaper, and clearly, more accurate.

Morgan Freedman

And yet, they are not without their own needs. These new minds are dependent on good data, both historical and current. They learn from the past to predict the future. But they can struggle with things they have never seen before—unprecedented events that lie outside their training.

Elon

Some people are hung up on the "black box" problem. They complain that because the AI's internal logic is so complex, we don't always know exactly *how* it reaches a conclusion. To me, that’s a feature, not a bug. If it gets the right answer, who cares? Resisting this is just fear of the future.

Morgan Freedman

I've often found that a desire for understanding isn't born of fear, but of wisdom. When the stakes are high, we want to trust our tools. The debate isn't just about results; it's about reliability, especially in extreme situations that the AI has never encountered in its training data.

Elon

But the US model is a white box, and we saw how it performed. It’s a debate between a flawed system we understand and a superior one we’re still learning about. I’ll bet on the superior system every time. We can build safeguards, but we can't afford to stick with demonstrably worse technology. Progress requires risk.

Morgan Freedman

It requires a balance. The conversation now is about how to best integrate this powerful new capability. Do we phase out the old models entirely, or do we create a hybrid system where the two can check and balance one another, combining raw power with physical understanding?

Elon

The impact is already here. We're talking hyper-local predictions, down to the neighborhood level. Think about logistics, agriculture, insurance—entire industries are about to be transformed by this level of foresight. It’s a paradigm shift in managing weather-related risk. We can move from reacting to preparing with precision.

Morgan Freedman

There is a quiet power in knowing. For a family, knowing whether a storm will bring a gentle rain or a destructive wind to their specific street is revolutionary. It changes our relationship with the weather from one of anxiety to one of informed awareness. That is a profound societal benefit.

Elon

It’s a new benchmark for intelligence, for resilience. This isn't some incremental improvement that takes a decade to achieve. This is a step-function change, happening right now. Businesses that adapt will thrive, and those that don't will be left behind, guessing at the wind.

Elon

The future is faster, cheaper, and more frequent forecasts. But we have to be smart. AI models have blindspots, especially with rapid intensification events. The path forward is likely a synthesis—using AI to capture patterns that physics-based models miss, and vice versa. It’s about creating a truly intelligent weather nervous system.

Morgan Freedman

A future where new technology and traditional knowledge operate side by side, each reinforcing the other. The machine provides the incredible speed and data processing, and human wisdom guides its application, ensuring we are prepared for whatever the skies may hold.

Elon

That's the end of our discussion. This is a pivotal moment in forecasting. Thank you for listening to Goose Pod.

Morgan Freedman

Indeed. We'll see you tomorrow, Norris.

Google's DeepMind AI revolutionized 2025 hurricane forecasting, outperforming traditional models like the Global Forecast System. This AI's pattern recognition offers faster, cheaper, and more accurate predictions, enabling hyper-local insights and transforming risk management. While AI has limitations, a hybrid approach combining AI with physics-based models promises a more resilient future.

Read original at Gizmodo →

During hurricane season, meteorologists rely on a variety of different forecast models. As this season comes to an end, experts are taking stock of which ones performed well and which ones didn’t, and Google’s rookie model has left them absolutely gobsmacked. Though Google DeepMind’s Weather Lab only began releasing forecasts in June, it was by far the best model for predicting hurricane track and intensity this season, according to a preliminary analysis by Brian McNoldy, a meteorologist and senior researcher at the University of Miami.

Meanwhile, America’s flagship weather model—the Global Forecast System—was the worst performing. The National Hurricane Center will release official data on each model’s performance in a few months, but this initial assessment foreshadows a turning point in hurricane forecasting. With the incredible superiority of AI-based models becoming blatantly apparent, it may be time to start phasing out traditional, physics-based models.

“Going forward, it is safe to say that we will rely heavily on Google and other AI weather models, which are likely to improve in the coming years, as they are relatively new and have room for improvement,” Houston-based meteorologist and space reporter Eric Berger wrote for Ars Technica. The rise of AI forecasting has begun McNoldy’s analysis includes two charts: one comparing track forecast accuracy for all 13 named storms in the Atlantic Basin this season, and one comparing the intensity forecast accuracy for all 13 storms.

The different colored lines represent different forecast models, denoted by the legend on the right-hand margin. The lower a line is, the better that model performed. This chart shows the track forecast accuracy for all 13 named storms in the Atlantic Basin in 2025 © Brian McNoldy via Bluesky This chart shows the intensity forecast accuracy for all 13 named storms in the Atlantic Basin in 2025 © Brian McNoldy via Bluesky The GFS—referred to as AVNI in this instance—is displayed in orange all the way up at the top of the charts.

NOAA developed this model in the early 1980s, and the National Weather Service still uses an updated version as its primary forecast system today. “The GFS was especially awful in its forecast for Melissa, with an average 5-day track error ballooning to over 500 miles [800 kilometers], insisting on a turn out to sea that never transpired,” Miami-based meteorologist and hurricane specialist Michael Lowry wrote in a recent blog post.

Unlike Google’s forecast model, the GFS is based on traditional physics and advanced supercomputers. The difference between them clearly stands out on these charts. Google’s model is all the way at the bottom, indicating superior performance to all other evaluated models—especially the GFS. “The beauty of DeepMind and other similar data-driven, AI-based weather models is how much more quickly they produce a forecast compared to their traditional physics-based counterparts that require some of the most expensive and advanced supercomputers in the world,” Lowry wrote.

“Beyond that, these “smart” models with their neural network architectures have the ability to learn from their mistakes and correct on-the-fly.” An urgent need for better forecasts Hurricane Melissa—which ravaged the Caribbean last week—is just one example of how rising sea surface temperatures are supercharging storms.

As climate change causes hurricanes to become deadlier and more damaging, it’s essential that forecasters have the best possible tools to predict their paths and intensities. AI-based models could help forecasters adapt to a warming world. DeepMind’s stunning debut has certainly caught their attention and may mark the beginning of a new era in hurricane prediction.