🔗 Share this article

As Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had previously made such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Reliance on AI Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa becoming a most intense hurricane. Although I am unprepared to predict that strength yet due to track uncertainty, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the storm drifts over very warm ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first AI model focused on tropical cyclones, and now the first to beat standard weather forecasters at their specialty. Through all 13 Atlantic storms this season, Google’s model is top-performing – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts recorded in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to prepare for the catastrophe, potentially preserving lives and property.

How Google’s System Works

The AI system works by identifying trends that conventional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated 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 forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of AI training – a technique that has been used in research fields like weather science for years – and is not generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to generate 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 run and require some of the biggest high-performance systems in the world.

Professional Reactions and Future Developments

Nevertheless, the fact that the AI could outperform previous top-tier traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“It’s astonishing,” commented James Franklin, a former expert. “The data is sufficient that it’s pretty clear this is not just beginner’s luck.”

He said that while the AI is beating all competing systems on forecasting the future path of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, he stated he intends to discuss with the company about how it can enhance the AI results even more helpful for experts by providing additional under-the-hood data they can utilize to assess exactly why it is producing its conclusions.

“The one thing that nags at me is that while these predictions seem to be really, really good, the results of the system is kind of a black box,” said Franklin.

Broader Industry Trends

Historically, no a commercial entity that has produced a top-level forecasting system which grants experts a peek into its methods – in contrast to most other models which are provided free to the public in their full form by the authorities that created and operate them.

Google is not alone in starting to use artificial intelligence to solve difficult weather forecasting problems. The US and European governments are developing their respective AI weather models in the works – which have demonstrated better performance over earlier traditional systems.

The next steps in AI weather forecasts appear to involve startup companies tackling formerly difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the national monitoring system.