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As Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a most intense hurricane. Although I am not ready to predict that strength yet due to path variability, that remains a possibility.

“It appears likely that a period of rapid intensification will occur as the system moves slowly over very warm sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer AI model focused on hurricanes, and currently the first to outperform traditional weather forecasters at their specialty. Across all 13 Atlantic storms this season, Google’s model is top-performing – even beating human forecasters on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the disaster, possibly saving lives and property.

The Way Google’s Model Works

The AI system operates through spotting patterns that traditional lengthy physics-based weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a former forecaster.

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

Clarifying AI Technology

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

Machine learning processes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to generate an result, and can do so on a standard PC – in strong contrast to the flagship models that governments have used for decades that can take hours to run and require the largest high-performance systems in the world.

Professional Reactions and Future Developments

Still, the fact that Google’s model could outperform earlier top-tier traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“I’m impressed,” commented James Franklin, a former forecaster. “The data is now large enough that it’s evident this is not just chance.”

He said that while the AI is beating all other models on predicting the trajectory of hurricanes globally this year, like many AI models it occasionally gets high-end intensity predictions wrong. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he plans to talk with the company about how it can enhance the DeepMind output more useful for forecasters by offering extra under-the-hood data they can use to assess the reasons it is coming up with its answers.

“The one thing that troubles me is that while these forecasts seem to be really, really good, the output of the system is kind of a black box,” remarked Franklin.

Wider Industry Developments

Historically, no a private, for-profit company that has developed a high-performance weather model which allows researchers a view of its methods – unlike nearly all systems which are offered free to the public in their entirety by the governments that designed and maintain 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 better performance over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve new firms taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.