🔗 Share this article

When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had previously made such a bold prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Reliance on AI Predictions

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 AI simulation runs show Melissa becoming a Category 5 storm. While I am unprepared to predict 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 system drifts over exceptionally hot sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer AI model dedicated to hurricanes, and now the initial to outperform traditional weather forecasters at their own game. Across all 13 Atlantic storms this season, Google’s model is the best – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls recorded in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to get ready for the disaster, possibly saving people and assets.

The Way The System Works

The AI system operates through spotting patterns that conventional lengthy scientific weather models may overlook.

“The AI performs far faster than their traditional counterparts, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has demonstrated in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

To be sure, the system is an example of AI training – a technique that has been used in research fields like weather science for years – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a such a way that its model only requires minutes to come up with an result, and can operate on a standard PC – in sharp difference to the primary systems that governments have utilized for years that can require many hours to run and require some of the biggest high-performance systems in the world.

Professional Reactions and Future Developments

Nevertheless, the reality that the AI could exceed previous gold-standard traditional systems so quickly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest storms.

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

He noted that although Google DeepMind is beating all other models on forecasting the future path of storms worldwide this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, he said he plans to discuss with the company about how it can enhance the AI results more useful for experts by offering additional internal information they can utilize to evaluate exactly why it is producing its conclusions.

“A key concern that nags at me is that although these forecasts seem to be really, really good, the results of the system is kind of a opaque process,” remarked Franklin.

Broader Industry Developments

There has never been a commercial entity that has produced a top-level forecasting system which allows researchers a view of its techniques – unlike nearly all other models which are offered free to the public in their entirety by the governments that designed and maintain them.

Google is not alone in starting to use artificial intelligence to solve challenging meteorological problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have also shown improved skill over earlier traditional systems.

Future developments in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. One company, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.