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When Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

As the primary meteorologist on duty, he forecasted that in a single day the storm would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made such a bold forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. While I am not ready to forecast that intensity yet due to track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the storm drifts over very warm sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Models

The AI model is the first artificial intelligence system dedicated to hurricanes, and now the initial to beat standard meteorological experts at their own game. Through all 13 Atlantic storms so far this year, Google’s model is the best – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the region. The confident prediction probably provided residents extra time to prepare for the disaster, potentially preserving people and assets.

The Way Google’s System Works

Google’s model works by spotting patterns that conventional time-intensive physics-based prediction systems may miss.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the slower traditional forecasting tools we’ve relied upon,” Lowry added.

Understanding AI Technology

To be sure, Google DeepMind is an example of machine learning – a method that has been employed in data-heavy sciences like meteorology for a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an result, and can operate on a standard PC – in strong contrast to the primary systems that governments have used for decades that can take hours to process and require some of the biggest high-performance systems in the world.

Expert Reactions and Future Developments

Nevertheless, the fact that Google’s model could exceed earlier gold-standard legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

“It’s astonishing,” said James Franklin, a former forecaster. “The data is now large enough that it’s evident this is not a case of beginner’s luck.”

Franklin noted that although Google DeepMind is outperforming all competing systems on predicting the trajectory of storms globally this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to category 5 above the Caribbean.

In the coming offseason, Franklin stated he plans to discuss with Google about how it can make the AI results more useful for forecasters by offering extra internal information they can utilize to assess exactly why it is producing its conclusions.

“The one thing that troubles me is that while these predictions seem to be highly accurate, the output of the model is essentially a opaque process,” said Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has developed a top-level forecasting system which grants experts a peek into its methods – unlike nearly all other models which are provided free to the public in their full form by the governments that created and operate them.

The company is not the only one in starting to use artificial intelligence to address challenging weather forecasting problems. The US and European governments are developing their own artificial intelligence systems in the works – which have also shown better performance over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even launching its own atmospheric sensors to address deficiencies in the US weather-observing network.