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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made this confident forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise 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 remarkable power 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 clarified in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 hurricane. Although I am not ready to forecast that strength at this time due to track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the storm moves slowly over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first AI model dedicated to hurricanes, and currently the first to outperform traditional weather forecasters at their specialty. Across all tropical systems this season, the AI is top-performing – even beating experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in nearly two centuries of record-keeping across the region. The confident prediction probably provided people in Jamaica additional preparation time to get ready for the disaster, potentially preserving lives and property.

How The System Works

Google’s model works by identifying trends that conventional lengthy scientific prediction systems may miss.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has demonstrated in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry added.

Understanding AI Technology

It’s important to note, Google DeepMind is an example of AI training – a technique that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a manner that its system only requires minutes to generate an result, and can do so on a standard PC – in sharp difference to the flagship models that governments have used for decades that can take hours to run and need some of the biggest high-performance systems in the world.

Professional Responses and Future Developments

Nevertheless, the reality that the AI could exceed previous top-tier legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to predict the most intense storms.

“I’m impressed,” said James Franklin, a retired expert. “The data is now large enough that it’s evident this is not a case of beginner’s luck.”

Franklin noted that although the AI is outperforming all other models on predicting the future path of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity predictions wrong. It had difficulty with another storm previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he said he plans to discuss with Google about how it can make the AI results even more helpful for experts by offering additional under-the-hood data they can use to assess exactly why it is producing its answers.

“A key concern that nags at me is that although these predictions seem to be really, really good, the output of the model is essentially a opaque process,” said Franklin.

Wider Sector Developments

There has never been a commercial entity that has developed a high-performance forecasting system which allows researchers a peek into its methods – in contrast to most other models which are offered at no cost to the public in their entirety by the governments that created and operate them.

Google is not the only one in starting to use artificial intelligence to solve challenging meteorological problems. The US and European governments are developing their own artificial intelligence systems in the works – which have demonstrated better performance over previous non-AI versions.

The next steps in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is also launching its proprietary weather balloons to fill the gaps in the US weather-observing network.