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

Serving as lead forecaster 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 Jamaican shoreline. Not a single expert had previously made this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 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 Category 5 hurricane. Although I am unprepared to predict that intensity yet given path variability, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the storm moves slowly over exceptionally hot ocean waters which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer AI model dedicated to tropical cyclones, and now the first to beat traditional meteorological experts at their own game. Through all tropical systems so far this year, Google’s model is the best – even beating experts on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the catastrophe, possibly saving lives and property.

How The System Functions

The AI system operates through spotting patterns that conventional time-intensive physics-based prediction systems may overlook.

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in quick time is that the newcomer AI weather models are on par with and, in some cases, superior than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

To be sure, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a such a way that its system only requires minutes to come up with an result, and can operate on a desktop computer – in sharp difference to the flagship models that governments have utilized for years that can require many hours to run and require some of the biggest high-performance systems in the world.

Professional Reactions and Future Advances

Nevertheless, the fact that Google’s model could outperform earlier top-tier traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense storms.

“I’m impressed,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not just chance.”

Franklin noted that although Google DeepMind is beating all competing systems on predicting the trajectory of storms globally this year, like many AI models it occasionally gets extreme strength predictions wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he plans to talk with the company about how it can make the DeepMind output more useful for forecasters by offering extra under-the-hood data they can utilize to evaluate the reasons it is producing its conclusions.

“The one thing that nags at me is that although these forecasts seem to be really, really good, the output of the model is essentially a black box,” remarked Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has developed a high-performance weather model which allows researchers a view of its methods – unlike most other models which are offered at no cost to the general audience in their full form by the authorities that created and operate them.

Google is not the only one in starting to use AI to solve challenging meteorological problems. The US and European governments also have their own AI weather models in the works – which have also shown better performance over previous non-AI versions.

The next steps in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.