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As Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a monster hurricane.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would become a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had ever issued such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 hurricane. While I am unprepared to predict that strength at this time given path variability, that is still plausible.

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

Outperforming Traditional Models

Google DeepMind is the first AI model focused on tropical cyclones, and currently the initial to beat traditional weather forecasters at their own game. Across all tropical systems so far this year, the AI is the best – surpassing human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the region. The confident prediction likely gave residents additional preparation time to get ready for the catastrophe, possibly saving lives and property.

How The System Works

The AI system works by identifying trends that traditional time-intensive physics-based weather models may miss.

“They do it far faster than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a ex meteorologist.

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

Clarifying AI Technology

It’s important to note, the system is an instance of AI training – a method that has been employed in research fields like meteorology for years – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to generate an result, and can operate on a standard PC – in strong contrast to the flagship models that authorities have used for years that can take hours to run and need the largest supercomputers in the world.

Expert Responses and Future Developments

Still, the reality that the AI could exceed previous gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not just chance.”

He noted that although Google DeepMind is outperforming all other models on predicting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It struggled with Hurricane Erin previously, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

During the next break, he stated he intends to discuss with Google about how it can enhance the AI results even more helpful for experts by providing additional internal information they can utilize to evaluate exactly why it is producing its conclusions.

“A key concern that nags at me is that while these predictions appear highly accurate, the results of the system is kind of a opaque process,” remarked Franklin.

Wider Sector Trends

There has never been a commercial entity that has produced a high-performance forecasting system which allows researchers a view of its methods – in contrast to most other models which are provided free to the general audience in their full form by the governments that created and operate them.

The company is not alone in adopting artificial intelligence to solve difficult meteorological problems. The authorities also have their own AI weather models in the works – which have also shown improved skill over earlier traditional systems.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the national monitoring system.