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When Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

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

But, 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. True to the forecast, Melissa evolved into a system of remarkable power that tore through Jamaica.

Growing Reliance on AI Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a most intense hurricane. Although I am not ready to forecast that intensity yet given path variability, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the storm drifts over very warm sea temperatures which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and currently the first to outperform standard weather forecasters at their own game. Through all tropical systems so far this year, the AI is the best – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in nearly two centuries of data collection across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to prepare for the disaster, possibly saving lives and property.

How The System Functions

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

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer artificial intelligence systems are competitive with and, in some cases, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he added.

Clarifying AI Technology

To be sure, the system is an instance of AI training – a technique that has been used in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes 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 do so on a desktop computer – in sharp difference to the primary systems that authorities have used for years that can take hours to run and require the largest supercomputers in the world.

Professional Responses and Upcoming Advances

Nevertheless, the fact that the AI could outperform earlier gold-standard traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest weather systems.

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

He noted that while Google DeepMind is beating all other models on forecasting the trajectory of storms worldwide this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

During the next break, he said he plans to talk with the company about how it can make the DeepMind output even more helpful for experts by providing additional internal information they can use to assess exactly why it is producing its conclusions.

“A key concern that nags at me is that while these forecasts appear highly accurate, the results of the system is essentially a black box,” said Franklin.

Broader Industry Developments

There has never been a commercial entity that has developed a high-performance forecasting system which allows researchers a peek into its methods – in contrast to most other models which are offered at no cost to the general audience in their entirety by the governments that designed and maintain them.

Google is not the only one in starting to use artificial intelligence to address challenging weather forecasting problems. The US and European governments are developing their respective AI weather models in the works – which have demonstrated better performance over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts 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 own weather balloons to address deficiencies in the national monitoring system.