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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist 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 become a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 AI simulation runs show Melissa becoming a Category 5 storm. Although I am unprepared to predict that strength at this time due to path variability, that is still plausible.

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

Surpassing Traditional Systems

Google DeepMind is the first AI model dedicated to tropical cyclones, and currently the first to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is the best – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, among the most powerful landfalls ever documented in almost 200 years of data collection across the region. Papin’s bold forecast probably provided residents extra time to prepare for the catastrophe, potentially preserving lives and property.

How The System Works

The AI system operates through identifying trends that traditional time-intensive physics-based weather models may miss.

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

“What this hurricane season has proven in short order is that the recent AI weather models are on par with and, in certain instances, more accurate than the slower traditional forecasting tools we’ve relied upon,” Lowry added.

Clarifying AI Technology

It’s important to note, the system is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for years – and is not generative AI like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to generate an result, and can operate on a desktop computer – in strong contrast to the flagship models that governments have utilized for decades that can require many hours to run and require the largest high-performance systems in the world.

Professional Responses and Future Developments

Still, the reality that the AI could exceed previous top-tier legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The data is sufficient that it’s evident this is not a case of chance.”

Franklin said that while Google DeepMind is outperforming all competing systems on predicting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength forecasts inaccurate. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he said he plans to discuss with the company about how it can make the AI results more useful for forecasters by offering extra internal information they can utilize to evaluate the reasons it is coming up with its conclusions.

“A key concern that nags at me is that while these forecasts seem to be highly accurate, the output of the model is essentially a black box,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has developed a top-level forecasting system which grants experts a peek into its techniques – in contrast to most systems which are offered at no cost to the public in their full form by the authorities that created and operate them.

The company is not the only one in adopting AI to address difficult weather forecasting problems. The authorities also have their respective AI weather models in the development phase – which have also shown improved skill over previous non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to pursue this. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.