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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would become a severe hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on 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 Google DeepMind simulation runs show Melissa reaching a most intense storm. While I am not ready to predict that strength yet due to path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification is expected as the system moves slowly over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the first AI model dedicated to hurricanes, and now the initial to beat traditional meteorological experts 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 eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in nearly two centuries of data collection across the region. The confident prediction probably provided residents additional preparation time to get ready for the disaster, potentially preserving people and assets.

The Way The System Functions

The AI system operates through identifying trends that traditional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“What this hurricane season has proven in quick time is that the newcomer AI weather models are on par with and, in some cases, more accurate than the slower physics-based weather models we’ve relied upon,” Lowry said.

Understanding AI Technology

It’s important to note, Google DeepMind is an example of AI training – a method that has been used in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the primary systems that governments have utilized for decades that can take hours to process and require some of the biggest supercomputers in the world.

Expert Responses and Upcoming Advances

Nevertheless, the reality that the AI could exceed previous top-tier legacy models so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest weather systems.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not a case of chance.”

He noted that although the AI is beating all competing systems on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

During the next break, Franklin said he intends to discuss with the company about how it can make the DeepMind output even more helpful for forecasters by offering additional internal information they can use to assess the reasons it is producing its conclusions.

“The one thing that nags at me is that although these forecasts seem to be really, really good, the results of the model is kind of a opaque process,” remarked Franklin.

Broader Sector Developments

There has never been a commercial entity that has developed a top-level forecasting system which allows researchers a peek into its techniques – unlike nearly all other models which are provided free to the public in their entirety by the governments that created and operate them.

Google is not the only one in adopting artificial intelligence to solve challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have demonstrated improved skill over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is even deploying its own weather balloons to fill the gaps in the US weather-observing network.