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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a monster hurricane.

As the primary meteorologist on duty, he forecasted that in a single day the storm would become a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had ever issued such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. Although I am unprepared to predict that strength yet due to track uncertainty, that is still plausible.

“It appears likely that a phase of rapid intensification will occur as the storm moves slowly over very warm sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and currently the first to beat traditional weather forecasters at their specialty. Across all tropical systems this season, Google’s model is top-performing – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum strength, among the most powerful landfalls recorded in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving people and assets.

How Google’s System Functions

Google’s model operates through identifying trends that traditional lengthy physics-based prediction systems may miss.

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the slower traditional weather models we’ve traditionally leaned on,” he said.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example of AI training – a method that has been employed in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a manner that its model only requires minutes to generate an result, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have used for years that can require many hours to process and require the largest supercomputers in the world.

Expert Responses and Upcoming Developments

Still, the fact that Google’s model could outperform earlier gold-standard legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

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

Franklin said that while Google DeepMind is beating all other models on predicting the future path of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he said he plans to talk with Google about how it can make the DeepMind output more useful for forecasters by offering extra internal information they can utilize to assess the reasons it is coming up with its conclusions.

“A key concern that nags at me is that although these forecasts seem to be really, really good, the results of the system is essentially a black box,” said Franklin.

Wider Industry Trends

There has never been a private, for-profit company that has produced a top-level forecasting system which allows researchers a view of its methods – unlike most systems which are offered free to the public in their full form by the authorities that created and operate them.

Google is not the only one in starting to use artificial intelligence to solve difficult weather forecasting problems. The authorities also have their own artificial intelligence systems in the development phase – which have also shown better performance over previous traditional systems.

Future developments in AI weather forecasts appear to involve startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the US weather-observing network.