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

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had ever issued such a bold forecast for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa becoming a Category 5 storm. While I am unprepared to forecast that intensity at this time due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the system drifts over exceptionally hot ocean waters which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first AI model focused on hurricanes, and currently the initial to beat standard meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is the best – surpassing human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the disaster, potentially preserving lives and property.

How The System Works

Google’s model operates through spotting patterns that conventional 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 time consuming,” said Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are competitive with and, in certain instances, superior than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

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

Machine learning takes large datasets and pulls out patterns from them in a manner that its system only requires minutes to come up with an result, and can do so on a standard PC – in sharp difference to the primary systems that authorities have utilized for decades that can take hours to run and require the largest high-performance systems in the world.

Professional Responses and Upcoming Advances

Still, the reality that the AI could outperform previous gold-standard legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

“It’s astonishing,” said James Franklin, a retired expert. “The sample is sufficient that it’s evident this is not a case of beginner’s luck.”

He said that although the AI is outperforming all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions wrong. It struggled with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he plans to talk with the company about how it can make the AI results even more helpful for experts by providing extra under-the-hood data they can utilize to evaluate the reasons it is producing its answers.

“A key concern that troubles me is that while these predictions seem to be highly accurate, the results of the model is essentially a black box,” said Franklin.

Wider Industry Developments

There has never been a commercial entity that has produced a top-level forecasting system which allows researchers a view of its techniques – unlike nearly all systems which are provided free to the general audience in their full form by the authorities that created and operate them.

Google is not alone in starting to use artificial intelligence to address challenging weather forecasting problems. The authorities also have their own AI weather models in the development phase – which have also shown better performance over earlier non-AI versions.

The next steps in artificial intelligence predictions appear to involve new firms taking swings at previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they have secured federal support to pursue this. One company, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the US weather-observing network.