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As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Forecasting

Meteorologists 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: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a most intense hurricane. Although I am unprepared to predict that intensity yet due to path variability, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the system drifts over exceptionally hot sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and currently the first to outperform standard meteorological experts at their own game. Through all tropical systems so far this year, Google’s model is the best – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

The Way Google’s System Works

The AI system works by identifying trends that traditional lengthy physics-based weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve relied upon,” he said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been used in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

AI training processes mounds of data and extracts trends 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 utilized for years that can take hours to process and require the largest high-performance systems in the world.

Professional Responses and Future Advances

Nevertheless, the reality that Google’s model could exceed earlier top-tier traditional systems so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

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

Franklin noted that while the AI is outperforming all other models on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he said he intends to discuss with the company about how it can enhance the DeepMind output even more helpful for forecasters by offering extra under-the-hood data they can utilize to assess the reasons it is producing its conclusions.

“The one thing that troubles me is that while these forecasts appear really, really good, the output of the system is kind of a black box,” said Franklin.

Wider Industry Trends

Historically, no a commercial entity that has developed a high-performance forecasting system which allows researchers a peek into its techniques – in contrast to nearly all systems which are offered at no cost to the general audience in their entirety by the authorities that created and operate them.

The company is not the only one in adopting AI to solve difficult weather forecasting problems. The US and European governments also have their own AI weather models in the development phase – which have also shown better performance over earlier traditional systems.

The next steps in AI weather forecasts seem to be startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.