🔗 Share this article

As Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

As the primary meteorologist on duty, he forecasted that in just 24 hours the weather system would become a category 4 hurricane and start shifting towards the coast of Jamaica. Not a single expert had previously made such a bold prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Reliance on AI Forecasting

Meteorologists 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: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 storm. Although I am not ready to forecast that strength at this time due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the system moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first AI model focused on hurricanes, and currently the first to outperform traditional weather forecasters at their own game. Across all tropical systems this season, Google’s model is the best – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the catastrophe, potentially preserving lives and property.

How The Model Works

The AI system works by spotting patterns that traditional lengthy scientific weather models may miss.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” stated Michael Lowry, a former meteorologist.

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

Understanding Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been used in data-heavy sciences like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the primary systems that governments have used for decades that can require many hours to process and require some of the biggest high-performance systems in the world.

Professional Responses and Future Developments

Nevertheless, the fact that Google’s model could exceed previous top-tier legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a retired expert. “The data is now large enough that it’s evident this is not just chance.”

He said that although Google DeepMind is outperforming all competing systems on forecasting the trajectory of storms globally this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

In the coming offseason, he stated he plans to discuss with the company about how it can make the DeepMind output even more helpful for forecasters by providing extra under-the-hood data they can utilize to assess the reasons it is coming up with its conclusions.

“The one thing that troubles me is that while these predictions appear really, really good, the output of the model is essentially a black box,” remarked Franklin.

Wider Industry Developments

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its methods – in contrast to most systems 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 solve difficult meteorological problems. The authorities also have their own AI weather models in the works – which have demonstrated improved skill over earlier traditional systems.

The next steps in artificial intelligence predictions seem to be new firms tackling previously tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.