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When Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had ever issued such a bold forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 AI simulation runs show Melissa becoming a Category 5 hurricane. While I am unprepared to forecast that intensity at this time given track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening is expected as the system drifts over very warm ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the pioneer AI model focused on hurricanes, and now the first to beat traditional weather forecasters at their own game. Across all 13 Atlantic storms this season, Google’s model is the best – surpassing human forecasters on track predictions.

The hurricane ultimately struck 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 likely gave people in Jamaica extra time to prepare for the disaster, potentially preserving lives and property.

How The System Works

The AI system works by identifying trends that conventional lengthy scientific prediction systems may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in some cases, more accurate than the slower physics-based forecasting tools we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, the system is an instance of machine learning – a technique that has been used in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to generate an answer, and can operate on a standard PC – in sharp difference to the primary systems that authorities have used for decades that can require many hours to run and require the largest supercomputers in the world.

Professional Reactions and Future Advances

Still, the reality that the AI could exceed previous 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,” commented James Franklin, a former expert. “The data is sufficient that it’s pretty clear this is not just chance.”

He said that although Google DeepMind is outperforming all other models on predicting the trajectory of storms worldwide this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, he said he intends to talk with the company about how it can make the AI results more useful for forecasters by offering extra under-the-hood data they can use to evaluate exactly why it is coming up with its answers.

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

Broader Industry Trends

There has never been a private, for-profit company that has developed a high-performance weather model which grants experts a view of its techniques – unlike nearly all other models which are offered free to the public in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use AI to address challenging weather forecasting problems. The US and European governments are developing their own artificial intelligence systems in the development phase – which have demonstrated better performance over previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve new firms tackling previously difficult problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they have secured federal support to do so. One company, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the national monitoring system.