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As Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting towards the coast of Jamaica. Not a single expert had ever issued this confident prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa becoming a Category 5 storm. Although I am unprepared to predict that strength yet given path variability, that is still plausible.

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

Outperforming Traditional Systems

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and now the initial to beat traditional meteorological experts at their own game. Through all 13 Atlantic storms so far this year, Google’s model is the best – even beating human forecasters 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. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets.

How The System Functions

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

“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are on par with and, in certain instances, superior than the slower traditional forecasting tools we’ve relied upon,” Lowry added.

Understanding Machine Learning

To be sure, the system is an example of machine learning – a technique that has been used in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a manner that its model 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 used for years that can require many hours to process and need some of the biggest supercomputers in the world.

Expert Reactions and Future Advances

Nevertheless, the fact that Google’s model could outperform previous top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s evident this is not just beginner’s luck.”

He said that while Google DeepMind is outperforming all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions wrong. 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 intends to discuss with Google about how it can make the AI results even more helpful for forecasters by providing additional under-the-hood data they can utilize to assess the reasons it is producing its answers.

“A key concern that troubles me is that while these predictions seem to be really, really good, the results of the system is essentially a opaque process,” said Franklin.

Broader Industry Developments

There has never been a commercial entity that has produced a high-performance weather model which grants experts a view of its techniques – in contrast to most systems which are offered at no cost to the public in their full form by the governments that created and operate them.

Google is not the only one in adopting AI to solve difficult weather forecasting problems. The authorities are developing their own AI weather models in the development phase – which have also shown better performance over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms tackling 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 even deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.