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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting towards the coast of Jamaica. 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 initial occasion in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool 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 intensity yet given path variability, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the system drifts over very warm sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first AI model dedicated to hurricanes, and currently the first to outperform traditional weather forecasters at their specialty. Across all 13 Atlantic storms this season, the AI is top-performing – even beating experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest 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 disaster, possibly saving people and assets.

The Way Google’s System Works

The AI system works by identifying trends that traditional lengthy scientific weather models may miss.

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

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the slower traditional weather models we’ve relied upon,” he said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of AI training – a technique that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes large datasets and extracts trends from them in a manner that its model only takes a few minutes to come up with an result, and can do so on a standard PC – in sharp difference to the flagship models that governments have used for decades that can require many hours to run and require the largest supercomputers in the world.

Expert Responses and Upcoming Advances

Nevertheless, the reality that the AI could outperform earlier top-tier legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired expert. “The data is now large enough that it’s pretty clear this is not just chance.”

He noted that while Google DeepMind is outperforming all other models on predicting the trajectory of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he said he intends to discuss with Google about how it can enhance the DeepMind output more useful for experts by providing additional internal information they can utilize to assess the reasons it is coming up with its answers.

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

Broader Industry Developments

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its methods – in contrast to nearly all other models which are provided 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 meteorological problems. The authorities also have their own artificial intelligence systems in the works – which have also shown better performance over previous traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at formerly difficult problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the national monitoring system.