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As Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

As the primary meteorologist on duty, he predicted that in a single day the weather system would become a severe 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 had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are heavily relying upon Google DeepMind. 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 AI simulation runs show Melissa reaching a most intense hurricane. While I am not ready to forecast that intensity at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the storm moves slowly over very warm ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer AI model focused on hurricanes, and now the first to outperform standard weather forecasters at their own game. Across all 13 Atlantic storms this season, the AI is the best – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided residents additional preparation time to get ready for the disaster, possibly saving lives and property.

How Google’s System Works

Google’s model operates through identifying trends that conventional time-intensive scientific weather models may miss.

“They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in quick time is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the slower traditional forecasting tools we’ve relied upon,” Lowry said.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a method that has been used in research fields like meteorology for years – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the primary systems that governments have utilized for years that can require many hours to run and require the largest high-performance systems in the world.

Expert Reactions and Upcoming Developments

Nevertheless, the reality that Google’s model could exceed previous gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the world’s strongest storms.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is now large enough that it’s evident this is not a case of chance.”

He said that while the AI is outperforming all competing systems on forecasting the future path of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with another storm previously, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

During the next break, he said he plans to talk with Google about how it can make the AI results more useful for forecasters by providing additional 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 predictions appear really, really good, the output of the model is essentially a opaque process,” remarked Franklin.

Wider Sector Developments

There has never been a commercial entity that has developed a high-performance weather model which grants experts a view of its methods – in contrast to nearly all other models which are provided free to the general audience in their full form by the authorities that created and operate them.

The company is not alone in adopting artificial intelligence to solve challenging weather forecasting problems. The authorities also have their own artificial intelligence systems in the development phase – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts seem to be new firms tackling formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even deploying its own weather balloons to fill the gaps in the US weather-observing network.