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When Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a major tropical system.

As the lead forecaster on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made such a bold prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Reliance on AI Forecasting

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa becoming a Category 5 hurricane. Although I am not ready to forecast that strength yet given path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the system moves slowly over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer AI model dedicated to hurricanes, and now the first to outperform traditional meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts recorded in almost 200 years of data collection across the region. Papin’s bold forecast probably provided residents extra time to prepare for the catastrophe, potentially preserving people and assets.

The Way Google’s System Functions

Google’s model works by spotting patterns that conventional time-intensive physics-based weather models may overlook.

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

“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 slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

To be sure, the system is an instance of AI training – a method that has been employed in research fields like meteorology for years – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a manner that its system only requires minutes to generate an result, and can operate on a standard PC – in strong contrast to the primary systems that authorities have utilized for years that can take hours to process and need some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that Google’s model could outperform earlier gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a former forecaster. “The sample is sufficient that it’s pretty clear this is not a case of chance.”

He said that while Google DeepMind is outperforming all other models on forecasting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, he stated he intends to talk with the company about how it can enhance the AI results more useful for forecasters by providing additional internal information they can utilize to assess exactly why it is producing its conclusions.

“A key concern that troubles me is that although these predictions seem to be highly accurate, the output of the model is essentially a opaque process,” said Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has developed a top-level weather model which grants experts a view of its techniques – unlike nearly all other models which are offered free to the public in their full form by the authorities that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to solve difficult meteorological problems. The authorities also have their own artificial intelligence systems in the works – which have also shown improved skill over earlier traditional systems.

Future developments in AI weather forecasts seem to be new firms tackling formerly difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they are receiving US government funding to do so. One company, WindBorne Systems, is also launching its own atmospheric sensors to fill the gaps in the national monitoring system.