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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

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 in the direction of the Jamaican shoreline. No forecaster had previously made such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 hurricane. Although I am unprepared to forecast that strength yet given path variability, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the storm moves slowly over very warm ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the first to beat standard meteorological experts at their specialty. Through all tropical systems this season, Google’s model is top-performing – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful landfalls ever documented in almost 200 years of data collection across the region. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the disaster, potentially preserving people and assets.

The Way Google’s Model Works

The AI system works by spotting patterns that conventional lengthy physics-based prediction systems may miss.

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

“What this hurricane season has demonstrated in quick time is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the slower physics-based weather models we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

To be sure, the system is an example of AI training – a technique that has been used in research fields like weather science for a long time – 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 requires minutes to generate an result, and can operate on a desktop computer – in sharp difference to the primary systems that governments have used for decades that can take hours to process and require the largest high-performance systems in the world.

Professional Responses and Future Advances

Nevertheless, the fact that the AI could outperform earlier gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” said James Franklin, a former forecaster. “The data is now large enough that it’s evident this is not a case of beginner’s luck.”

He noted that although Google DeepMind is beating all other models on forecasting the trajectory of hurricanes globally this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

During the next break, he said he intends to discuss with Google about how it can make the AI results even more helpful for forecasters by offering additional internal information they can use to evaluate the reasons it is producing its answers.

“A key concern that nags at me is that although these forecasts seem to be really, really good, the output of the model is kind of a opaque process,” remarked Franklin.

Broader Industry Trends

Historically, no a commercial entity that has produced a high-performance forecasting system which grants experts a view of its methods – in contrast to nearly all other models which are provided at no cost to the public in their entirety by the governments that created and operate them.

The company is not alone in starting to use artificial intelligence to address challenging meteorological problems. The US and European governments are developing their respective AI weather models in the development phase – which have also shown better performance over earlier non-AI versions.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the national monitoring system.