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

As the primary meteorologist on duty, he predicted that in a single day the storm would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 hurricane. While I am unprepared to predict that strength yet due to path variability, that is still plausible.

“There is a high probability that a phase of quick strengthening will occur as the system moves slowly over very warm sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat traditional weather forecasters at their own game. Across all tropical systems this season, the AI is top-performing – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving lives and property.

The Way Google’s Model Works

The AI system works by spotting patterns that traditional lengthy scientific weather models may miss.

“The AI performs much more quickly 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 quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid traditional weather models we’ve traditionally leaned on,” he added.

Clarifying AI Technology

To be sure, Google DeepMind is an example of machine learning – a method that has been employed in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to generate an answer, and can do so on a standard PC – in strong contrast to the primary systems that authorities have utilized for decades that can require many hours to process and require some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Advances

Still, the reality that Google’s model could exceed earlier gold-standard legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to predict the most intense storms.

“I’m impressed,” said James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not just chance.”

He noted that while the AI is beating all other models on forecasting the trajectory of hurricanes globally this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, he said he intends to discuss with the company about how it can make the AI results even more helpful for experts by offering additional under-the-hood data they can utilize to evaluate the reasons it is producing its answers.

“A key concern that nags at me is that although these forecasts seem to be really, really good, the output of the system is essentially a black box,” remarked Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has produced a top-level weather model which grants experts a peek into its methods – unlike most systems which are provided at no cost to the general audience in their entirety by the authorities that created and operate them.

The company is not alone in starting to use artificial intelligence to address challenging weather forecasting problems. The authorities are developing their respective AI weather models in the works – which have demonstrated better performance over previous non-AI versions.

Future developments in AI weather forecasts seem to be 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 federal support to do so. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.