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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.

As the primary meteorologist on duty, he forecasted that in just 24 hours the weather system would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made this confident forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa becoming a most intense storm. Although I am unprepared to predict that intensity at this time due to track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the storm moves slowly over exceptionally hot ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and currently the first to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms this season, the AI is the best – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to prepare for the catastrophe, potentially preserving people and assets.

The Way The System Works

Google’s model works by spotting patterns that traditional lengthy physics-based prediction systems may overlook.

“The AI performs far faster than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid physics-based weather models we’ve relied upon,” he said.

Understanding AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a method that has been used in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a such a way that its system only takes a few minutes to generate an result, and can operate on a standard PC – in strong contrast to the primary systems that governments have used for years that can take hours to run and require the largest high-performance systems in the world.

Professional Responses and Future Advances

Still, the reality that the AI could exceed previous gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the world’s strongest storms.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not just beginner’s luck.”

He noted that although the AI is outperforming all competing systems on forecasting the future path of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity predictions wrong. It struggled with another storm previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he stated he plans to discuss with Google about how it can make the DeepMind output more useful for experts by providing additional under-the-hood data they can use to evaluate exactly why it is coming up with its conclusions.

“The one thing that troubles me is that although these predictions seem to be really, really good, the output of the model is kind of a opaque process,” said Franklin.

Wider Industry Developments

There has never been a commercial entity that has produced a top-level weather model which allows researchers a peek into its techniques – in contrast to most other models which are offered at no cost to the public in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use artificial intelligence to solve challenging weather forecasting problems. The authorities also have their respective artificial intelligence systems in the development phase – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies tackling formerly difficult problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and sudden deluges – and they have secured federal support to do so. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.