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

Serving as lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. No forecaster had previously made this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 hurricane. While I am not ready to predict that strength yet due to path variability, that remains a possibility.

“It appears likely that a phase of quick strengthening is expected as the storm drifts over exceptionally hot sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the first to outperform standard weather forecasters at their own game. Through all 13 Atlantic storms this season, Google’s model is top-performing – even beating experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided residents additional preparation time to prepare for the disaster, possibly saving lives and property.

The Way The System Functions

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

“They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are on par with and, in some cases, more accurate than the slower physics-based weather models we’ve relied upon,” he said.

Understanding Machine Learning

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like meteorology for years – and is not generative AI like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that governments have used for years that can take hours to run and require the largest high-performance systems in the world.

Expert Responses and Upcoming Developments

Still, the fact that Google’s model could exceed earlier gold-standard legacy models so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The data is sufficient that it’s pretty clear this is not just beginner’s luck.”

Franklin noted that while the AI is outperforming all competing systems on forecasting the trajectory of storms worldwide this year, like many AI models it sometimes errs on extreme strength predictions inaccurate. 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 plans to discuss with Google about how it can enhance the AI results even more helpful for forecasters by providing additional internal information they can use to evaluate exactly why it is producing its conclusions.

“A key concern that troubles me is that while these forecasts appear highly accurate, the output of the model is essentially a opaque process,” remarked Franklin.

Broader Industry Developments

Historically, no a commercial entity that has produced a high-performance weather model which grants experts a peek into its techniques – in contrast to nearly all other models 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 the only one in starting to use AI to solve difficult weather forecasting problems. The US and European governments also have their respective AI weather models in the development phase – which have also shown improved skill over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the US weather-observing network.