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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would become a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had ever issued such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Dependence on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI ensemble members show Melissa becoming a Category 5 storm. While I am unprepared to predict that intensity yet due to path variability, that remains a possibility.

“It appears likely that a phase of quick strengthening is expected as the storm moves slowly over very warm sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the first AI model focused on hurricanes, and now the first to outperform traditional weather forecasters at their own game. Across all 13 Atlantic storms so far this year, the AI is the best – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave residents extra time to prepare for the catastrophe, possibly saving people and assets.

How The System Works

Google’s model works by identifying trends that traditional time-intensive physics-based weather models may miss.

“The AI performs much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the recent AI weather models are competitive with and, in certain instances, more accurate than the slower physics-based forecasting tools we’ve relied upon,” he said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example of AI training – a technique that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a such a way that its system only takes a few minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the flagship models that governments have used for decades that can take hours to run and need the largest supercomputers in the world.

Professional Responses and Upcoming Developments

Nevertheless, the fact that Google’s model could outperform previous gold-standard legacy models so quickly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense storms.

“It’s astonishing,” said James Franklin, a former expert. “The data is sufficient that it’s evident this is not a case of chance.”

He said that while the AI is outperforming all competing systems on predicting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It had difficulty 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 stated he plans to discuss with Google about how it can make the DeepMind output even more helpful for experts by providing additional internal information they can utilize to assess exactly why it is coming up with its answers.

“A key concern that nags at me is that while these forecasts seem to be really, really good, the output of the model is kind of a opaque process,” remarked Franklin.

Broader Sector Trends

There has never been a private, for-profit company that has developed a high-performance forecasting system which allows researchers a view of its methods – in contrast to nearly all other models which are provided at no cost to the public in their entirety by the governments that designed and maintain them.

The company is not the only one in adopting AI to solve difficult weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the works – which have demonstrated improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies taking swings at previously difficult problems such as long-range forecasts and better advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. One company, WindBorne Systems, is also deploying its proprietary weather balloons to fill the gaps in the national monitoring system.