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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he predicted 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. Not a single expert had previously made this confident forecast for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Growing Dependence on AI Forecasting

Meteorologists are heavily relying upon the AI system. During 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 show Melissa reaching a most intense hurricane. Although I am not ready to forecast that intensity at this time due to path variability, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the storm drifts over very warm sea temperatures which is the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Models

The AI model is the first AI model focused on tropical cyclones, and currently the first to beat traditional weather forecasters at their own game. Through all tropical systems so far this year, the AI is the best – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

The Way Google’s Model Functions

The AI system operates through identifying trends that traditional time-intensive physics-based prediction systems may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the less rapid traditional forecasting tools we’ve relied upon,” Lowry added.

Clarifying Machine Learning

It’s important to note, the system is an instance of AI training – a technique that has been employed in research fields like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to generate an result, and can do so on a standard PC – in strong contrast to the flagship models that governments have utilized for decades that can require many hours to process and require the largest high-performance systems in the world.

Expert Responses and Upcoming Advances

Still, the fact that the AI could exceed previous gold-standard legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the most intense storms.

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

He said that although Google DeepMind is outperforming all other models on forecasting the future path of storms globally this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing rapid intensification 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 DeepMind output even more helpful for forecasters by offering extra internal information they can utilize to evaluate exactly why it is producing its conclusions.

“A key concern that troubles me is that while these predictions seem to be really, really good, the output of the system is essentially a opaque process,” said Franklin.

Wider Sector Developments

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its techniques – in contrast to nearly all systems which are provided free to the public in their full form by the governments that designed and maintain them.

Google is not the only one in adopting artificial intelligence to address difficult weather forecasting problems. The authorities also have their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.