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When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had previously made such a bold prediction for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a system of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a most intense hurricane. While I am not ready to forecast that intensity at this time given track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the system moves slowly over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and currently the initial to outperform standard weather forecasters at their own game. Through all 13 Atlantic storms this season, the AI is the best – even beating experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls recorded in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided residents additional preparation time to get ready for the disaster, potentially preserving people and assets.

The Way Google’s Model Works

Google’s model works by spotting patterns that conventional time-intensive physics-based prediction systems may overlook.

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

“This season’s events has proven in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example of AI training – a method that has been used in data-heavy sciences like meteorology 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 requires minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have used for years that can take hours to run and require some of the biggest supercomputers in the world.

Professional Reactions and Future Developments

Nevertheless, the reality that the AI could outperform previous top-tier traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The data is sufficient that it’s evident this is not just chance.”

Franklin said that although the AI is beating all competing systems on predicting the future path of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, he stated he intends to discuss with the company about how it can make the AI results even more helpful for forecasters by providing additional under-the-hood data they can use to evaluate exactly why it is coming up with its conclusions.

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

Broader Industry Developments

Historically, no a private, for-profit company that has produced a top-level weather model which grants experts a peek into its techniques – in contrast to most systems which are offered at no cost to the general audience in their full form by the governments that designed and maintain them.

The company is not alone in adopting artificial intelligence to solve challenging meteorological problems. The authorities also have their respective AI weather models in the development phase – which have demonstrated better performance over previous non-AI versions.

Future developments in AI weather forecasts appear to involve new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the national monitoring system.