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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had previously made such a bold forecast for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI simulation runs show Melissa becoming a most intense storm. Although I am unprepared to forecast that intensity yet given path variability, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the storm moves slowly over very warm ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to hurricanes, and now the first to beat traditional meteorological experts at their own game. Across all tropical systems so far this year, the AI is the best – surpassing human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls ever documented in nearly two centuries of record-keeping across the region. The confident prediction probably provided people in Jamaica extra time to get ready for the catastrophe, possibly saving lives and property.

How The System Works

The AI system operates through spotting patterns that conventional time-intensive physics-based prediction systems may overlook.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a ex meteorologist.

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

Clarifying AI Technology

To be sure, Google DeepMind is an example of machine learning – a technique that has been used in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to come up with an result, and can do so on a standard PC – in sharp difference to the flagship models that authorities have used for years that can take hours to process and need some of the biggest high-performance systems in the world.

Expert Responses and Future Advances

Still, the fact that Google’s model could exceed previous top-tier legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense weather systems.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not just beginner’s luck.”

Franklin said that although Google DeepMind is beating all competing systems on forecasting the future path of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength predictions inaccurate. It had difficulty with another storm previously, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

During the next break, Franklin said he intends to talk with the company about how it can enhance the DeepMind output even more helpful for forecasters by offering extra under-the-hood data they can utilize to evaluate the reasons it is coming up with its conclusions.

“A key concern that troubles me is that while these forecasts appear highly accurate, the results of the model is kind of a black box,” remarked Franklin.

Broader Industry Developments

Historically, no a commercial entity that has developed a high-performance weather model which allows researchers a view of its methods – unlike most systems which are offered at no cost to the general audience in their full form by the governments that designed and maintain them.

Google is not the only one in adopting artificial intelligence to address challenging weather forecasting problems. The authorities also have their respective AI weather models in the works – which have demonstrated better performance over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the national monitoring system.