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When Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the weather system would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had ever issued such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense storm. Although I am unprepared to forecast that strength at this time due to path variability, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the system drifts over very warm sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the pioneer AI model focused on tropical cyclones, and now the first to outperform traditional meteorological experts at their own game. Across all tropical systems so far this year, Google’s model is the best – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, 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 disaster, possibly saving people and assets.

How The Model Works

The AI system works by spotting patterns that conventional lengthy physics-based prediction systems may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is less expensive and time consuming,” stated 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 some cases, more accurate than the slower physics-based weather models we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

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

Machine learning processes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to generate an result, and can operate on a standard PC – in strong contrast to the primary systems that authorities have used for years that can require many hours to process and need some of the biggest high-performance systems in the world.

Professional Reactions and Future Developments

Still, the fact that Google’s model could exceed earlier gold-standard legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not just beginner’s luck.”

Franklin noted that while the AI is outperforming all competing systems on forecasting the future path of storms globally this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he plans to discuss with the company about how it can enhance the AI results more useful for forecasters by providing additional under-the-hood data they can utilize to evaluate the reasons it is producing its answers.

“A key concern that nags at me is that although these predictions seem to be really, really good, the output of the system is kind of a opaque process,” said Franklin.

Broader Sector Trends

There has never been a commercial entity that has developed a high-performance forecasting system which allows researchers a view of its techniques – unlike most systems which are provided at no cost to the general audience in their entirety by the governments that designed and maintain them.

Google is not the only one in starting to use artificial intelligence to solve challenging meteorological problems. The authorities 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 long-range forecasts and improved advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the US weather-observing network.