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When Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the storm would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa reaching a most intense hurricane. While I am unprepared to forecast that strength yet due to track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the storm moves slowly over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and now the first to beat standard weather forecasters at their own game. Across all 13 Atlantic storms so far this year, the AI is top-performing – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful landfalls ever documented in nearly two centuries of record-keeping across the region. The confident prediction likely gave residents extra time to get ready for the catastrophe, potentially preserving lives and property.

The Way The Model Works

Google’s model works by spotting patterns that conventional lengthy physics-based 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 meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in certain instances, superior than the less rapid physics-based weather models we’ve relied upon,” he added.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been employed in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to generate an answer, and can operate on a desktop computer – in sharp difference to the flagship models that authorities have used for years that can take hours to run and need some of the biggest high-performance systems in the world.

Professional Responses and Upcoming Developments

Still, the fact that the AI could exceed previous top-tier legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

Franklin noted that although the AI is outperforming all other models on forecasting the trajectory of storms globally this year, like many AI models it sometimes errs on high-end intensity predictions inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he intends to discuss with Google about how it can make the AI results even more helpful for experts by providing additional under-the-hood data 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 predictions seem to be highly accurate, the results of the model is kind of a black box,” said Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has produced a high-performance weather model which grants experts a peek into its techniques – unlike most other models which are provided free to the general audience in their full form by the governments that designed and maintain them.

The company is not alone in starting to use AI to address difficult weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have also shown better performance over previous traditional systems.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the national monitoring system.