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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had ever issued this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Forecasting

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his confidence: “Roughly 40/50 Google DeepMind ensemble members show Melissa becoming a most intense hurricane. Although I am unprepared to predict that strength yet given track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the system drifts over exceptionally hot ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

The AI model is the pioneer artificial intelligence system focused on hurricanes, and now the initial to beat traditional meteorological experts at their own game. Through all tropical systems so far this year, the AI is top-performing – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to get ready for the catastrophe, potentially preserving people and assets.

The Way Google’s System Works

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

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“What this hurricane season has proven in quick time is that the recent AI weather models are on par with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve relied upon,” Lowry added.

Understanding AI Technology

It’s important to note, Google DeepMind is an example of AI training – a technique that has been employed in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a such a way that its model only takes a few minutes to generate an result, and can do so on a desktop computer – in sharp difference to the flagship models that governments have used for years that can take hours to process and need the largest supercomputers in the world.

Professional Responses and Future Advances

Still, the reality that Google’s model could exceed earlier gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former expert. “The sample is sufficient that it’s pretty clear this is not a case of chance.”

Franklin said that although the AI is beating all other models on predicting the future path of storms globally this year, like many AI models it sometimes errs on extreme strength predictions inaccurate. It struggled with another storm previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin said he intends to talk with Google about how it can make the DeepMind output even more helpful for forecasters by providing extra under-the-hood data they can utilize to evaluate exactly why it is producing its conclusions.

“The one thing that nags at me is that although these predictions appear highly accurate, the output of the model is kind of a opaque process,” remarked Franklin.

Broader Sector Trends

There has never been a private, for-profit company that has produced a high-performance weather model which allows researchers a peek into its techniques – in contrast to nearly all systems which are provided free to the public in their entirety by the authorities that created and operate them.

The company is not the only one in starting to use artificial intelligence to address challenging weather forecasting problems. The US and European governments are developing their own AI weather models in the works – which have demonstrated improved skill over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at previously difficult problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.