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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. No forecaster had ever issued this confident prediction for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a most intense hurricane. Although I am unprepared to forecast that intensity yet due to path variability, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the system moves slowly over very warm sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the first AI model dedicated to hurricanes, and now the initial to beat standard meteorological experts at their own game. Across all tropical systems this season, Google’s model is top-performing – even beating experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving lives and property.

How Google’s System Works

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

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

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

It’s important to note, the system is an instance of machine learning – a method that has been used in research fields like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the flagship models that governments have utilized for years that can require many hours to run and need the largest supercomputers in the world.

Professional Responses and Future Advances

Still, the fact that the AI could outperform previous top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense storms.

“I’m impressed,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s evident this is not just beginner’s luck.”

He said that although Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity predictions wrong. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he intends to discuss with Google about how it can make the AI results more useful for forecasters by offering extra internal information they can use to assess the reasons it is coming up with its answers.

“A key concern that troubles me is that although these forecasts appear highly accurate, the output of the system is essentially a opaque process,” said Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has developed a top-level forecasting system which allows researchers a peek into its techniques – in contrast to nearly all other models which are offered free to the public in their entirety by the governments that created and operate them.

The company is not the only one in adopting AI to solve challenging meteorological problems. The US and European governments also have their own AI weather models in the development phase – which have demonstrated improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be new firms taking swings at formerly difficult problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.