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As Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.

As the primary meteorologist on duty, he forecasted that in a single day the storm would become a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued such a bold prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a key factor for his certainty: “Roughly 40/50 AI simulation runs show Melissa reaching a most intense storm. While I am not ready to forecast that strength at this time given track uncertainty, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the system moves slowly over very warm sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer AI model focused on hurricanes, and currently the initial to beat standard meteorological experts at their own game. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the region. Papin’s bold forecast likely gave residents additional preparation time to get ready for the disaster, possibly saving lives and property.

How Google’s System Works

Google’s model operates through spotting patterns that traditional time-intensive scientific weather models may overlook.

“They do it far faster than their physics-based cousins, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are on par with and, in some cases, more accurate than the slower physics-based weather models we’ve relied upon,” Lowry added.

Clarifying AI Technology

It’s important to note, the system is an instance of machine learning – a technique that has been used in research fields like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the primary systems that governments have utilized for years that can take hours to process and need some of the biggest high-performance systems in the world.

Expert Reactions and Future Advances

Still, the fact that the AI could exceed earlier gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the most intense storms.

“It’s astonishing,” said James Franklin, a retired expert. “The data is sufficient that it’s pretty clear this is not just chance.”

Franklin said that while the AI is beating all other models on forecasting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity predictions wrong. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, he said he intends to discuss with the company about how it can enhance the DeepMind output more useful for forecasters by offering additional internal information they can use to assess exactly why it is coming up with its conclusions.

“A key concern that nags at me is that although these forecasts seem to be really, really good, the results of the system is essentially a opaque process,” remarked Franklin.

Broader Industry Trends

There has never been a commercial entity that has produced a top-level weather model which grants experts a peek into its techniques – in contrast to nearly all other models which are provided free to the public in their full form by the governments that designed and maintain them.

Google is not alone in starting to use AI to address difficult weather forecasting problems. The US and European governments also have their own AI weather models in the development phase – which have demonstrated better performance over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the national monitoring system.