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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.

Serving as lead forecaster on duty, he predicted 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 prediction for quick intensification.

But, 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 did become a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense hurricane. While 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 quick strengthening is expected as the system moves slowly over exceptionally hot ocean waters which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer AI model focused on hurricanes, and currently the initial to beat standard meteorological experts at their specialty. Across all 13 Atlantic storms this season, the AI is top-performing – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls ever documented in almost 200 years of data collection across the region. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving lives and property.

The Way Google’s Model Functions

Google’s model operates through identifying trends that traditional lengthy physics-based weather models may overlook.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the slower physics-based weather models we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

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

Machine learning processes large datasets and pulls out patterns from them in a manner that its system only requires minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the primary systems that governments have utilized for years that can take hours to run and require the largest supercomputers in the world.

Professional Reactions and Upcoming Advances

Still, the reality that the AI could outperform previous gold-standard traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not a case of beginner’s luck.”

He said that while Google DeepMind is outperforming all other models on predicting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, he stated he plans to discuss with the company about how it can enhance the AI results more useful for experts by providing extra internal information they can use to assess exactly why it is coming up with its answers.

“The one thing that troubles me is that although these forecasts seem to be really, really good, the output of the model is kind of a black box,” remarked Franklin.

Broader Industry Developments

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a peek into its methods – in contrast to nearly all other models which are provided at no cost to the general audience in their full form by the governments that designed and maintain them.

The company is not the only one in starting to use AI to solve difficult weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the works – which have demonstrated better performance over earlier traditional systems.

Future developments in AI weather forecasts appear to involve startup companies tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the national monitoring system.