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When Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. No forecaster had ever issued such a bold forecast for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa becoming a Category 5 hurricane. Although I am unprepared to predict that intensity yet given track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the system drifts over very warm sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first AI model focused on hurricanes, and now the initial to outperform traditional meteorological experts at their own game. Across all tropical systems this season, Google’s model is the best – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, 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 get ready for the catastrophe, potentially preserving lives and property.

How The System Functions

The AI system works by spotting patterns that traditional lengthy physics-based weather models may miss.

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, more accurate than the slower physics-based weather models we’ve relied upon,” Lowry added.

Clarifying AI Technology

It’s important to note, the system is an example of machine learning – a method that has been employed in data-heavy sciences like meteorology for years – and is not creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a such a way that its system only takes a few minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the primary systems that governments have utilized for decades that can take hours to run and require the largest supercomputers in the world.

Expert Responses and Future Developments

Still, the reality that the AI could exceed earlier gold-standard traditional systems so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense storms.

“I’m impressed,” said James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not just chance.”

Franklin said that although the AI is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity predictions inaccurate. 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 stated he intends to discuss with Google about how it can make the DeepMind output even more helpful for forecasters by offering additional under-the-hood data they can utilize to evaluate exactly why it is producing its conclusions.

“The one thing that troubles me is that although these predictions seem to be really, really good, the output of the model is kind of a opaque process,” said Franklin.

Wider Sector Trends

Historically, no a private, for-profit company that has developed a high-performance weather model which allows researchers a view of its methods – unlike most other models which are provided free to the public in their full form by the governments that designed and maintain them.

Google is not alone in starting to use artificial intelligence to address difficult meteorological problems. The authorities are developing their own AI weather models in the works – which have demonstrated better performance over previous non-AI versions.

The next steps in AI weather forecasts appear to involve new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.