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As Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made such a bold forecast for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. Although I am not ready to predict 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 storm drifts over exceptionally hot sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and currently the first to beat standard meteorological experts at their own game. Through all tropical systems so far this year, the AI is the best – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in almost 200 years of data collection across the region. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving people and assets.

The Way Google’s Model Functions

The AI system operates through spotting patterns that traditional time-intensive scientific prediction systems may miss.

“The AI performs much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the slower physics-based weather models we’ve relied upon,” he added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a method that has been employed 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 system only takes a few minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that governments have used for decades that can take hours to process and require some of the biggest high-performance systems in the world.

Professional Responses and Future Advances

Nevertheless, the reality that Google’s model could exceed earlier gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest storms.

“It’s astonishing,” said James Franklin, a former expert. “The data is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

He noted that although Google DeepMind is beating all competing systems on forecasting the future path of storms globally this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin said he intends to discuss with the company about how it can enhance the AI results more useful for forecasters by providing additional internal information they can use to evaluate the reasons it is coming up with its answers.

“The one thing that nags at me is that although these predictions appear really, really good, the output of the system is kind of a opaque process,” said Franklin.

Broader Sector Developments

There has never been a commercial entity that has produced a high-performance forecasting system which grants experts a peek into its methods – unlike nearly all other models which are offered free to the general audience in their entirety by the governments that designed and maintain them.

Google is not alone in starting to use AI to address difficult meteorological problems. The US and European governments are developing their own AI weather models in the works – which have demonstrated improved skill over earlier traditional systems.

The next steps in AI weather forecasts seem to be new firms tackling previously tough-to-solve problems such as long-range forecasts and improved advance warnings of severe weather and flash flooding – and they are receiving US government funding to do so. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.