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

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

However, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing 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 key factor for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa reaching a Category 5 hurricane. Although I am not ready to forecast that strength at this time given path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification is expected as the storm moves slowly over exceptionally hot ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Models

The AI model is the first AI model dedicated to hurricanes, and now the initial to beat traditional meteorological experts at their own game. Across all tropical systems so far this year, Google’s model is the best – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful landfalls recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to get ready for the disaster, potentially preserving people and assets.

How The Model Functions

The AI system works by identifying trends that traditional time-intensive physics-based prediction systems may overlook.

“They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in short order is that the recent AI weather models are on par with and, in certain instances, superior than the less rapid traditional weather models we’ve relied upon,” Lowry added.

Clarifying Machine Learning

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

Machine learning processes large datasets and extracts trends from them in a manner that its model only takes a few minutes to generate an result, and can do so on a desktop computer – in sharp difference to the primary systems that authorities have used for decades that can require many hours to process and need the largest supercomputers in the world.

Professional Reactions and Future Advances

Still, the reality that the AI could outperform previous gold-standard legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense storms.

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

Franklin said that although Google DeepMind is outperforming all other models on predicting the future path of storms 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 category 5 above the Caribbean.

During the next break, Franklin said he plans to discuss with the company about how it can make the AI results more useful for forecasters by offering additional under-the-hood data they can use to assess the reasons it is coming up with its answers.

“The one thing that troubles me is that although these predictions seem to be highly accurate, the output of the model is kind of a black box,” remarked Franklin.

Broader Industry Developments

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a view of its methods – in contrast to nearly all systems which are provided at no cost to the general audience in their full form by the authorities that designed and maintain them.

The company is not the only one in starting to use AI to address challenging meteorological problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have also shown improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they have secured federal support to do so. One company, WindBorne Systems, is also deploying its proprietary weather balloons to fill the gaps in the US weather-observing network.