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

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and start shifting towards the coast of Jamaica. Not a single expert had previously made 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 cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Growing Reliance on AI Predictions

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense hurricane. Although I am unprepared to predict that strength yet due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the system drifts over exceptionally hot sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and currently the initial to beat traditional meteorological experts at their own game. Through all tropical systems so far this year, Google’s model is top-performing – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving lives and property.

How The Model Functions

The AI system works by identifying trends that conventional time-intensive scientific weather models may miss.

“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former forecaster.

“This season’s events has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a method that has been employed in data-heavy sciences like meteorology for years – and is not creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the flagship models that authorities have utilized for decades that can take hours to run and require the largest supercomputers in the world.

Expert Reactions and Future Advances

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

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

Franklin said that while Google DeepMind is beating all other models on forecasting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength forecasts wrong. It struggled with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, he stated he intends to talk with Google about how it can make the DeepMind output more useful for forecasters by offering additional internal information they can utilize to assess exactly why it is producing its answers.

“A key concern that troubles me is that while these predictions appear highly accurate, the results of the model is kind of a opaque process,” said Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has developed a high-performance forecasting system which grants experts a view of its methods – in contrast to nearly all systems which are offered free to the public in their full form by the authorities that designed and maintain them.

The company is not alone in adopting AI to address challenging meteorological problems. The authorities are developing their own artificial intelligence systems in the development phase – which have also shown improved skill over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies taking swings at previously difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.