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

As the lead forecaster on duty, he predicted that in just 24 hours the storm would become a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin explained in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa reaching a most intense storm. Although I am not ready to forecast that strength yet given path variability, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the storm moves slowly over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the initial to outperform standard weather forecasters at their specialty. Across all tropical systems so far this year, Google’s model is top-performing – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to get ready for the catastrophe, potentially preserving lives and property.

The Way Google’s Model Works

The AI system works by identifying trends that conventional lengthy physics-based weather models may overlook.

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

“This season’s events has proven in short order is that the recent AI weather models are on par with and, in some cases, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been used in research fields like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the flagship models that governments have used for decades that can require many hours to process and need some of the biggest high-performance systems in the world.

Professional Reactions and Future Advances

Still, the fact that Google’s model could outperform previous top-tier legacy models so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense storms.

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

Franklin noted that although Google DeepMind is beating all competing systems on forecasting the trajectory of storms globally this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he stated he intends to talk with Google about how it can make the AI results more useful for experts by offering additional under-the-hood data they can use to evaluate exactly why it is producing its answers.

“A key concern that troubles me is that although these predictions seem to be highly accurate, the results of the system is kind of a black box,” said Franklin.

Wider Industry Developments

There has never been a commercial entity that has produced a high-performance forecasting system which grants experts a view of its techniques – unlike nearly all other models which are offered free to the general audience in their entirety by the authorities that created and operate them.

Google is not alone in starting to use artificial intelligence to address challenging weather forecasting problems. The US and European governments also have their own AI weather models in the development phase – which have also shown improved skill over previous traditional systems.

The next steps in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the national monitoring system.