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As Developing Cyclone Melissa swirled off the coast of Haiti, weather expert 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 storm would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had previously made this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a system of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Predictions

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa reaching a most intense hurricane. Although I am unprepared to predict that intensity yet due to path variability, that remains a possibility.

“There is a high probability that a phase of quick strengthening will occur as the storm drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the first AI model focused on tropical cyclones, and currently the first to outperform traditional meteorological experts at their own game. Through all 13 Atlantic storms this season, Google’s model is top-performing – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

The Way The Model Functions

Google’s model operates through spotting patterns that traditional time-intensive physics-based weather models may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in some cases, more accurate than the less rapid traditional weather models we’ve relied upon,” he added.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of AI training – a method that has been employed in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets and extracts trends from them in a manner that its system only requires minutes to come up with an result, and can operate on a standard PC – in strong contrast to the flagship models that authorities have used for years that can require many hours to run and require the largest supercomputers in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the fact that Google’s model could exceed previous top-tier traditional systems so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense storms.

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

He noted that although 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 forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin stated he intends to discuss with Google about how it can make the AI results more useful for forecasters by offering additional internal information they can use to evaluate the reasons it is producing its conclusions.

“The one thing that troubles me is that while these predictions appear highly accurate, the output of the system is kind of a black box,” remarked Franklin.

Wider Sector Developments

There has never been a commercial entity that has developed a high-performance forecasting system which grants experts a peek into its methods – in contrast to most other models which are provided free to the public in their full form by the governments that created and operate them.

The company is not the only one in adopting artificial intelligence to solve challenging meteorological problems. The US and European governments also have their respective AI weather models in the development phase – which have demonstrated better performance over previous non-AI versions.

Future developments in AI weather forecasts seem to be startup companies tackling previously tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.