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When Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he predicted that in a single day the storm would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had ever issued this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his confidence: “Approximately 40/50 AI simulation runs show Melissa becoming a Category 5 storm. While I am unprepared to predict that strength at this time due to track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the system moves slowly over very warm ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Models

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

The hurricane ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the region. The confident prediction likely gave residents extra time to get ready for the catastrophe, potentially preserving people and assets.

How The System Works

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

“They do it much more quickly than their physics-based cousins, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

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

AI training takes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the primary systems that governments have used for decades that can take hours to run and need some of the biggest supercomputers in the world.

Expert Reactions and Future Advances

Still, the fact that Google’s model could outperform previous gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense storms.

“It’s astonishing,” said James Franklin, a former expert. “The data is now large enough that it’s evident this is not a case of chance.”

Franklin noted that although the AI is beating all other models on forecasting the trajectory of storms globally this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It had difficulty with Hurricane Erin previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin stated he intends to discuss with the company about how it can make the DeepMind output more useful for experts by providing additional under-the-hood data they can use to assess exactly why it is producing its conclusions.

“The one thing that nags at me is that although these predictions appear really, really good, the results of the model is kind of a black box,” said Franklin.

Wider Industry Developments

There has never been a private, for-profit company that has developed a high-performance weather model which grants experts a peek into its techniques – in contrast to nearly all other models which are provided at no cost to the public in their full form by the authorities that designed and maintain them.

The company is not the only one in adopting AI to address challenging weather forecasting problems. The US and European governments also have their own AI weather models in the works – which have also shown better performance over earlier non-AI versions.

The next steps in AI weather forecasts seem to be new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the national monitoring system.