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

Serving as primary meteorologist on duty, he predicted 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 this confident prediction for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense storm. Although I am unprepared to forecast that strength at this time given path variability, that is still plausible.

“It appears likely that a phase of rapid intensification will occur as the storm drifts over very warm ocean waters which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and currently the initial to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls ever documented in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to prepare for the catastrophe, potentially preserving people and assets.

How The Model Works

The AI system operates through spotting patterns that conventional lengthy scientific prediction systems may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, superior than the less rapid traditional weather models we’ve traditionally leaned on,” he added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a method that has been employed in data-heavy sciences like meteorology for years – and is not generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to generate an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have used for decades that can require many hours to process and require the largest high-performance systems in the world.

Expert Responses and Future Developments

Nevertheless, the reality that the AI could outperform previous gold-standard traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a former expert. “The data is sufficient that it’s evident this is not a case of beginner’s luck.”

He said that while Google DeepMind is outperforming all other models on predicting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

During the next break, Franklin stated he plans to discuss with the company about how it can make the AI results more useful for experts by offering additional under-the-hood data they can utilize to assess the reasons it is producing its conclusions.

“The one thing that troubles me is that although these predictions seem to be really, really good, the output of the model is kind of a opaque process,” said Franklin.

Wider Industry Developments

There has never been a private, for-profit company that has developed a high-performance weather model which allows researchers a peek into its methods – in contrast to most systems which are offered free to the general audience in their full form by the authorities that created and operate them.

Google is not alone in adopting artificial intelligence to solve challenging meteorological problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have also shown improved skill over earlier traditional systems.

Future developments in AI weather forecasts appear to involve startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.