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When Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the weather system would become a severe hurricane and start shifting towards the coast of Jamaica. Not a single expert had previously made such a bold forecast for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa becoming a Category 5 storm. While I am not ready to predict that intensity at this time due to track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification is expected as the storm moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to beat standard meteorological experts at their own game. Through all tropical systems this season, the AI is the best – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the region. The confident prediction probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving lives and property.

The Way The Model Works

The AI system operates through identifying trends that traditional 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,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been employed in research fields like weather science for years – and is not generative AI like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an result, and can operate on a standard PC – in sharp difference to the primary systems that authorities have utilized for decades that can require many hours to run and need the largest supercomputers in the world.

Expert Reactions and Upcoming Developments

Nevertheless, the reality that the AI could exceed earlier top-tier legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a former expert. “The data is now large enough that it’s pretty clear this is not just chance.”

He noted that although the AI is outperforming all competing systems on forecasting the future path of storms globally this year, like many AI models it occasionally gets extreme strength forecasts 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 stated he plans to discuss with the company about how it can make the DeepMind output even more helpful for forecasters by providing additional under-the-hood data they can utilize to assess exactly why it is coming up with its conclusions.

“The one thing that troubles me is that while these forecasts seem to be highly accurate, the results of the system is essentially a black box,” said Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has produced a high-performance weather model which allows researchers a view of its techniques – unlike nearly all systems which are provided free to the public in their entirety by the authorities that created and operate them.

The company is not the only one in starting to use AI to solve challenging weather forecasting problems. The US and European governments also have their own 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 new firms taking swings at formerly tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the US weather-observing network.