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When Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the weather system would become a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had ever issued such a bold forecast for rapid strengthening.

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. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa reaching a most intense hurricane. While I am unprepared to forecast that intensity at this time given track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the system drifts over exceptionally hot ocean waters which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and currently the first to beat standard weather forecasters at their own game. Across all 13 Atlantic storms so far this year, Google’s model is the best – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided residents extra time to get ready for the catastrophe, possibly saving lives and property.

The Way Google’s System Functions

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

“The AI performs far faster than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid traditional forecasting tools we’ve relied upon,” Lowry said.

Understanding AI Technology

To be sure, Google DeepMind is an example of AI training – a method that has been used in research fields like meteorology for years – and is distinct from generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to generate an result, and can operate on a standard PC – in sharp difference to the flagship models that authorities have utilized for years that can take hours to process and need some of the biggest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the fact that Google’s model could exceed previous gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest weather systems.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The sample is now large enough that it’s evident this is not just chance.”

He noted that while Google DeepMind is beating all competing systems on forecasting the trajectory of storms worldwide this year, like many AI models it occasionally gets extreme strength forecasts wrong. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he said he plans to discuss with the company about how it can make the DeepMind output more useful for forecasters by offering extra under-the-hood data they can use to assess exactly why it is coming up with its answers.

“A key concern that nags at me is that while these predictions appear really, really good, the output of the system is kind of a black box,” remarked Franklin.

Wider Industry Trends

There has never been a commercial entity that has developed a high-performance forecasting system which grants experts a peek into its techniques – unlike most systems which are offered free to the public in their full form by the governments that designed and maintain them.

The company is not alone in adopting AI to address difficult meteorological problems. The authorities are developing their own artificial intelligence systems in the works – which have demonstrated improved skill over previous traditional systems.

The next steps in AI weather forecasts seem to be startup companies tackling previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.