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As Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he predicted that in a single day the weather system would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had ever issued such a bold prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his confidence: “Roughly 40/50 Google DeepMind ensemble members show Melissa becoming a most intense storm. Although I am unprepared to predict that intensity yet due to path variability, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the storm drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat traditional weather forecasters at their specialty. Across all tropical systems this season, Google’s model is the best – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls ever documented in almost 200 years of data collection across the region. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the catastrophe, possibly saving people and assets.

How Google’s Model Functions

Google’s model works by identifying trends that traditional time-intensive physics-based weather models may miss.

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

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Understanding AI Technology

It’s important to note, Google DeepMind is an example of AI training – a method that has been used in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a manner that its system only requires minutes to come up with an result, and can do so on a desktop computer – in sharp difference to the flagship models that governments have utilized for decades that can take hours to run and need the largest supercomputers in the world.

Expert Responses and Upcoming Developments

Still, the fact that Google’s model could outperform previous gold-standard legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” said James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not a case of beginner’s luck.”

Franklin said that while 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 predictions wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin stated he intends to talk with the company about how it can make the AI results more useful for experts by providing additional internal information they can utilize to evaluate exactly why it is producing its answers.

“The one thing that nags at me is that although these forecasts appear really, really good, the output of the model is kind of a opaque process,” said Franklin.

Broader Sector Developments

Historically, no a commercial entity that has produced a top-level weather model which grants experts a view of its techniques – unlike most other models which are offered at no cost to the public in their entirety by the governments that created and operate them.

Google is not the only one in adopting artificial intelligence to solve challenging weather forecasting problems. The authorities also have their own artificial intelligence systems in the works – which have also shown improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve new firms taking swings at previously difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the national monitoring system.