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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would become a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense storm. While I am unprepared to forecast that strength at this time due to path variability, that is still plausible.

“There is a high probability that a phase of quick strengthening will occur as the system drifts over exceptionally hot ocean waters which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the first AI model dedicated to hurricanes, and now the first to outperform traditional meteorological experts at their specialty. Through all tropical systems this season, Google’s model is the best – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls recorded in almost 200 years of data collection across the region. Papin’s bold forecast likely gave residents additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

How The System Functions

Google’s model works by spotting patterns that traditional lengthy scientific weather models may miss.

“The AI performs far faster than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“This season’s events has proven in quick time is that the newcomer AI weather models are competitive with and, in certain instances, superior than the less rapid physics-based weather models we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of machine learning – a method that has been used in data-heavy sciences like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a such a way that its model only requires minutes to generate an answer, and can do so on a standard PC – in sharp difference to the flagship models that governments have utilized for years that can require many hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Still, the reality that Google’s model could outperform earlier top-tier traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that although Google DeepMind is outperforming all other models on forecasting the future path of storms worldwide this year, like many AI models it occasionally gets extreme strength predictions inaccurate. It struggled with another storm previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

In the coming offseason, Franklin said he plans to talk with Google about how it can enhance the DeepMind output more useful for experts by providing extra internal information they can utilize to evaluate exactly why it is producing its conclusions.

“A key concern that nags at me is that while these predictions appear highly accurate, the output of the model is kind of a opaque process,” said Franklin.

Broader Industry Trends

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

Google is not the only one in adopting artificial intelligence to address challenging weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the development phase – which have also shown better performance over previous traditional systems.

Future developments in AI weather forecasts seem to be new firms tackling formerly 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. A particular firm, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.