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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day 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 prediction for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa becoming a Category 5 hurricane. Although I am unprepared to forecast that strength yet given track uncertainty, that remains a possibility.

“There is a high probability that a phase of rapid intensification is expected as the storm moves slowly over exceptionally hot ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the first artificial intelligence system dedicated to hurricanes, and now the initial to beat traditional weather forecasters at their own game. Across all 13 Atlantic storms this season, the AI is top-performing – even beating experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave residents extra time to prepare for the disaster, potentially preserving lives and property.

The Way The Model Functions

Google’s model operates through identifying trends that traditional time-intensive scientific weather models may overlook.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

It’s important to note, the system is an instance of machine learning – a technique that has been used in data-heavy sciences like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a manner that its system only takes a few minutes to generate an result, and can do so on a desktop computer – in strong contrast to the primary systems that authorities have utilized for decades that can take hours to process and need some of the biggest high-performance systems in the world.

Professional Reactions and Future Developments

Nevertheless, the reality that Google’s model could exceed previous top-tier traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a retired expert. “The data is now large enough that it’s evident this is not just beginner’s luck.”

Franklin noted that while Google DeepMind is outperforming all other models on forecasting the trajectory of storms worldwide this year, like many AI models it sometimes errs on extreme strength predictions wrong. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, Franklin stated he intends to discuss with the company about how it can make the DeepMind output more useful for experts by offering extra under-the-hood data they can utilize to evaluate exactly why it is coming up with its answers.

“The one thing that nags at me is that although these predictions appear really, really good, the output of the system is essentially a opaque process,” said Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has developed a top-level weather model which grants experts a peek into its methods – in contrast to nearly all systems which are provided at no cost to the general audience in their entirety by the governments that created and operate them.

Google is not the only one in starting to use AI to solve challenging meteorological problems. The authorities are developing their own AI weather models in the works – which have also shown better performance over previous traditional systems.

The next steps in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the national monitoring system.