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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had previously made this confident prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Dependence on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a most intense storm. While I am unprepared to forecast that strength at this time given path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the system moves slowly over exceptionally hot ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system focused on hurricanes, and now the initial to outperform traditional meteorological experts at their own game. Across all tropical systems this season, the AI is top-performing – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls ever documented in almost 200 years of data collection across the region. The confident prediction likely gave residents extra time to prepare for the disaster, possibly saving lives and property.

The Way The Model Functions

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

“They do it far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“This season’s events has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid physics-based weather models we’ve relied upon,” Lowry added.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a method that has been employed in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training takes mounds of data and extracts trends from them in a manner that its system only requires minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the primary systems that governments have utilized for decades that can require many hours to process and require the largest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Still, the reality that Google’s model could exceed previous top-tier traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense weather systems.

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

He said that although Google DeepMind is beating all competing systems on forecasting the future path of storms globally this year, similar to other systems it sometimes errs on extreme strength predictions inaccurate. It had difficulty with another storm previously, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin stated he plans to discuss with the company about how it can make the DeepMind output more useful for forecasters by offering additional internal information they can use to evaluate the reasons it is producing its answers.

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

Broader Sector Developments

There has never been a commercial entity that has developed a high-performance forecasting system which allows researchers a view of its techniques – in contrast to most systems which are offered at no cost to the public in their full form by the governments that created and operate them.

Google is not alone in adopting artificial intelligence to solve difficult weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the works – which have demonstrated improved skill over previous non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the national monitoring system.