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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had ever issued this confident forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI simulation runs show Melissa becoming a most intense storm. While I am not ready to forecast that strength yet due to path variability, that is still plausible.

“There is a high probability that a period of rapid intensification is expected as the system moves slowly over very warm ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first AI model dedicated to hurricanes, and now the initial to beat traditional weather forecasters at their specialty. Through all 13 Atlantic storms this season, Google’s model is the best – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the catastrophe, possibly saving lives and property.

How Google’s System Works

The AI system operates through spotting patterns that conventional time-intensive physics-based weather models 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 meteorologist.

“This season’s events has demonstrated in short order is that the recent AI weather models are on par with and, in certain instances, more accurate than the slower physics-based forecasting tools we’ve relied upon,” he said.

Understanding Machine Learning

To be sure, the system is an example of AI training – a technique that has been employed in research fields like meteorology for years – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a such a way that its model only requires minutes to generate an result, and can do so on a desktop computer – in sharp difference to the flagship models that authorities have used for years that can take hours to process and require the largest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the fact that Google’s model could exceed earlier top-tier traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

He said that although Google DeepMind is beating all other models on predicting the trajectory of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to category 5 above the Caribbean.

During the next break, he said he plans to talk with Google about how it can enhance the AI results more useful for forecasters by offering additional under-the-hood data they can utilize to assess the reasons it is coming up with its answers.

“The one thing that nags at me is that while these predictions seem to be highly accurate, the results of the model is essentially a opaque process,” remarked Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has produced a high-performance forecasting system which allows researchers a view of its techniques – in contrast to most other models which are provided free to the general audience in their entirety by the authorities that designed and maintain them.

Google is not alone in adopting artificial intelligence to address challenging weather forecasting problems. The US and European governments also have their own AI weather models in the works – which have also shown improved skill over previous non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling formerly difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the national monitoring system.