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As Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would become a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made this confident forecast 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. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 storm. Although I am unprepared to predict that intensity yet given path variability, that is still plausible.

“It appears likely that a period of quick strengthening will occur as the storm drifts over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

The AI model is the first artificial intelligence system dedicated to hurricanes, and currently the initial to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms this season, Google’s model is the best – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the region. Papin’s bold forecast likely gave residents additional preparation time to prepare for the catastrophe, possibly saving people and assets.

How Google’s System Works

The AI system works by spotting patterns that conventional time-intensive scientific weather models may overlook.

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a former forecaster.

“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 forecasting tools we’ve traditionally leaned on,” he added.

Understanding AI Technology

To be sure, the system is an instance of AI training – a technique that has been employed in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its model 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 years that can take hours to process and need the largest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Nevertheless, the reality that the AI could exceed earlier gold-standard traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is sufficient that it’s evident this is not a case of chance.”

He noted that although Google DeepMind is beating all other models on forecasting the future path of storms worldwide this year, like many AI models it sometimes errs on high-end intensity predictions inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he intends to talk with Google about how it can make the DeepMind output more useful for experts by providing additional internal information they can utilize to evaluate the reasons it is coming up with its conclusions.

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

Broader Industry Trends

Historically, no a commercial entity that has produced a top-level forecasting system which grants experts a view of its techniques – unlike most other models which are offered at no cost to the general audience in their full form by the authorities that created and operate them.

The company is not alone in adopting AI to solve difficult weather forecasting problems. The US and European governments are developing their own AI weather models in the works – which have demonstrated improved skill over previous traditional systems.

The next steps in AI weather forecasts seem to be startup companies tackling formerly difficult problems such as long-range forecasts and improved early alerts of severe weather and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the national monitoring system.