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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made this confident prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Growing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa becoming a most intense storm. While I am not ready to predict that intensity at this time given path variability, that is still plausible.

“There is a high probability that a period of rapid intensification is expected as the storm drifts over exceptionally hot ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system focused on hurricanes, and now the first to outperform traditional weather forecasters at their own game. Through all tropical systems so far this year, Google’s model is top-performing – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to prepare for the catastrophe, possibly saving people and assets.

How Google’s Model Works

Google’s model operates through identifying trends that conventional time-intensive scientific prediction systems may overlook.

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

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are on par with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

It’s important to note, the system is an example of AI training – a technique that has been employed in research fields like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its system only requires minutes to generate an answer, and can operate on a standard PC – in strong contrast to the primary systems that governments have used for years that can require many hours to process and need some of the biggest supercomputers in the world.

Professional Responses and Upcoming Advances

Still, the fact that the AI could exceed previous top-tier traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a retired forecaster. “The data is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that although the AI is outperforming all other models on predicting the trajectory of storms worldwide this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he said he intends to talk with the company about how it can enhance the AI results more useful for forecasters by providing additional internal information they can utilize to assess exactly why it is producing its answers.

“The one thing that troubles me is that although these forecasts seem to be really, really good, the output of the model is kind of a opaque process,” remarked Franklin.

Broader Industry Trends

Historically, no a commercial entity that has developed a high-performance forecasting system which grants experts a peek into its techniques – in contrast to most systems which are offered at no cost to the general audience in their full form by the authorities that created and operate them.

Google is not alone in adopting artificial intelligence to address challenging meteorological problems. The authorities are developing their respective artificial intelligence systems in the works – which have demonstrated better performance over earlier non-AI versions.

Future developments in artificial intelligence predictions appear to involve startup companies tackling previously tough-to-solve problems such as long-range forecasts and improved advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the national monitoring system.