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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 primary meteorologist on duty, he forecasted that in a single day the storm would become a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 storm. Although I am not ready to predict that intensity at this time given track uncertainty, that is still plausible.

“There is a high probability that a phase of quick strengthening is expected as the storm moves slowly over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the first AI model dedicated to hurricanes, and now the first to beat standard weather forecasters at their specialty. Across all tropical systems this season, Google’s model is the best – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast probably provided residents additional preparation time to prepare for the disaster, potentially preserving people and assets.

How The System Works

The AI system works by identifying trends that traditional lengthy scientific prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has proven in quick time is that the recent AI weather models are on par with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve relied upon,” he added.

Understanding Machine Learning

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a manner that its model only takes a few minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the flagship models that governments have utilized for years that can take hours to process and require the largest high-performance systems in the world.

Professional Reactions and Future Advances

Still, the reality that Google’s model could outperform previous top-tier legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a former forecaster. “The data is sufficient that it’s pretty clear this is not just beginner’s luck.”

He said that although the AI is outperforming all competing systems on predicting the trajectory of storms worldwide this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, Franklin stated he plans to talk 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 utilize to evaluate the reasons it is coming up with its conclusions.

“A key concern that troubles me is that while these forecasts appear really, really good, the output of the model is kind of a opaque process,” remarked Franklin.

Wider Sector Developments

There has never been a private, for-profit company that has produced a top-level forecasting system which grants experts a peek into its techniques – unlike nearly all other models which are offered free to the public in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use AI to address challenging weather forecasting problems. The authorities also have their own AI weather models in the works – which have also shown improved skill over earlier traditional systems.

The next steps in artificial intelligence predictions appear to involve new firms taking swings at formerly tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the national monitoring system.