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

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon the AI system. During 25 October, Papin explained in his public discussion that Google’s model was a key factor for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa becoming a most intense storm. Although I am not ready to forecast that strength yet due to path variability, that is still plausible.

“There is a high probability that a phase of quick strengthening is expected as the storm moves slowly over very warm ocean waters which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

The AI model is the pioneer artificial intelligence system focused on hurricanes, and now the first to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls ever documented in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to prepare for the disaster, possibly saving people and assets.

How Google’s Model Functions

Google’s model operates through identifying trends that traditional time-intensive physics-based weather models may overlook.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

It’s important to note, the system is an example of AI training – a method that has been used in research fields like weather science for years – and is distinct from generative AI 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 generate an answer, and can do so on a standard PC – in sharp difference to the flagship models that governments have used for decades that can take hours to process and require the largest high-performance systems in the world.

Expert Reactions and Future Advances

Still, the fact that the AI could exceed previous top-tier legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“It’s astonishing,” commented James Franklin, a former expert. “The data is now large enough that it’s evident this is not a case of chance.”

He noted that although Google DeepMind is beating all competing systems on forecasting the future path of hurricanes globally this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin stated he plans to discuss with the company about how it can make the AI results even more helpful for forecasters by offering additional internal information they can utilize to assess exactly why it is coming up with its conclusions.

“The one thing that nags at me is that while these predictions seem to be highly accurate, the output of the model is essentially a opaque process,” remarked Franklin.

Wider Sector Developments

There has never been a commercial entity that has produced a top-level forecasting system which grants experts a peek into its techniques – unlike most other models which are provided free to the general audience in their full form by the governments that created and operate them.

The company is not the only one in adopting artificial intelligence to solve difficult meteorological problems. The US and European governments also have their respective artificial intelligence systems in the works – which have also shown improved skill over earlier traditional systems.

The next steps in AI weather forecasts seem to be new firms taking swings at formerly tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.