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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting towards the coast of Jamaica. Not a single expert had ever issued this confident forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Increasing Dependence on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a most intense hurricane. Although I am unprepared to predict that strength at this time given track uncertainty, that is still plausible.

“There is a high probability that a period of quick strengthening is expected as the storm moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and currently the first to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is the best – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets.

How The System Functions

The AI system works by spotting patterns that traditional lengthy scientific prediction systems may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in short order 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,” Lowry said.

Clarifying Machine Learning

It’s important to note, the system is an instance of AI training – a technique that has been employed in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to generate an result, and can operate on a desktop computer – in sharp difference to the primary systems that governments have utilized for years that can take hours to process and require the largest high-performance systems in the world.

Expert Responses and Future Advances

Nevertheless, the reality that Google’s model could outperform previous 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 storms.

“I’m impressed,” said James Franklin, a retired expert. “The sample is sufficient that it’s evident this is not just chance.”

He said that while the AI is beating all other models on predicting the future path of storms globally this year, like many AI models it sometimes errs on extreme strength predictions inaccurate. It had difficulty with another storm previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he stated he plans to discuss with the company about how it can make the AI results even more helpful for experts by offering additional internal information they can use to evaluate the reasons it is producing its conclusions.

“A key concern that troubles me is that although these predictions seem to be really, really good, the output of the model is essentially a black box,” remarked Franklin.

Broader Sector Trends

Historically, no a private, for-profit company that has developed a high-performance forecasting system which allows researchers a peek into its techniques – unlike nearly all systems which are offered free to the public in their full form by the authorities that created and operate them.

The company is not alone in starting to use AI to solve difficult meteorological problems. The US and European governments are developing their own artificial intelligence systems in the development phase – which have also shown improved skill over previous traditional systems.

Future developments in AI weather forecasts seem to be new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the national monitoring system.