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When Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a monster hurricane.

Serving as lead forecaster on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense hurricane. Although I am unprepared to predict that intensity at this time due to path variability, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the system drifts over exceptionally hot ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first AI model dedicated to tropical cyclones, and now the initial to beat traditional weather forecasters at their specialty. Across all tropical systems so far this year, Google’s model is the best – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to prepare for the disaster, potentially preserving lives and property.

How Google’s System Functions

Google’s model works by identifying trends that conventional lengthy physics-based weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” he said.

Understanding AI Technology

To be sure, the system is an instance of machine learning – a method that has been used in data-heavy sciences like meteorology for years – and is not generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a manner that its system only requires minutes to come up with an answer, and can do so on a desktop computer – in sharp difference to the flagship models that governments have used for decades that can take hours to process and need the largest supercomputers in the world.

Expert Responses and Upcoming Advances

Still, the reality that Google’s model could exceed earlier gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense storms.

“It’s astonishing,” said James Franklin, a former expert. “The sample is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

He noted that while Google DeepMind is outperforming all other models on predicting the future path of storms globally this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he stated he plans to discuss with Google about how it can enhance the AI results even more helpful for forecasters by offering additional internal information they can use to evaluate exactly why it is coming up with its conclusions.

“A key concern that troubles me is that while these forecasts seem to be highly accurate, the output of the model is kind of a black box,” said Franklin.

Broader Sector Developments

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its techniques – unlike most other models which are provided free to the general audience in their entirety by the governments that designed and maintain them.

The company is not alone in starting to use artificial intelligence to address difficult weather forecasting problems. The US and European governments also have their own AI weather models in the works – which have demonstrated improved skill over previous non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling previously difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.