🔗 Share this article

As Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

As the lead forecaster on duty, he forecasted that in a single day the storm would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made this confident forecast for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s new DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa becoming a most intense hurricane. Although I am not ready to forecast that strength at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening is expected as the storm moves slowly over very warm sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first AI model focused on tropical cyclones, and now the first to outperform standard weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is the best – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided residents extra time to prepare for the catastrophe, potentially preserving people and assets.

How Google’s Model Works

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

“The AI performs far faster than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex meteorologist.

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

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a method that has been used in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning 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 operate on a desktop computer – in sharp difference to the flagship models that governments have used for years that can require many hours to process and require the largest supercomputers in the world.

Professional Responses and Upcoming Advances

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 world’s strongest storms.

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

Franklin noted that although Google DeepMind is outperforming all other models on forecasting the future path of storms globally this year, similar to other systems it sometimes errs on extreme strength predictions inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin stated he intends to discuss with Google about how it can make the DeepMind output even more helpful for experts by providing additional internal information they can use to assess the reasons it is coming up with its answers.

“The one thing that nags at me is that while these forecasts appear highly accurate, the output of the model is kind of a opaque process,” said Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has produced a top-level forecasting system which grants experts a peek into its methods – unlike nearly all other models which are provided free to the public in their entirety by the governments that designed and maintain them.

The company is not alone in adopting artificial intelligence to address difficult weather forecasting problems. The authorities are developing their own artificial intelligence systems in the works – which have demonstrated improved skill over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve new firms tackling previously difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the national monitoring system.