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

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made this confident prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 storm. While I am unprepared to forecast that intensity at this time due to track uncertainty, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the system drifts over exceptionally hot sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer AI model focused on tropical cyclones, and currently the first to outperform traditional meteorological experts at their specialty. Across all tropical systems this season, Google’s model is top-performing – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the disaster, potentially preserving people and assets.

The Way Google’s Model Works

Google’s model operates through spotting patterns that traditional time-intensive physics-based weather models may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former meteorologist.

“What this hurricane season has proven in quick time is that the newcomer AI weather models are on par with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry added.

Understanding Machine Learning

To be sure, the system is an instance of machine learning – a method that has been used in research fields like meteorology for years – and is not generative AI like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a manner that its model only requires minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the flagship models that governments have used for years that can require many hours to run and need the largest supercomputers in the world.

Professional Responses and Upcoming Developments

Nevertheless, the fact that the AI could outperform previous gold-standard traditional systems so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense storms.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin noted that although the AI is beating all other models on forecasting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, he said he intends to talk with Google about how it can make the AI results even more helpful for experts by providing extra internal information they can utilize to assess exactly why it is coming up with its conclusions.

“The one thing that troubles me is that while these predictions appear highly accurate, the results of the system is essentially a black box,” remarked Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has produced a top-level forecasting system which grants experts a view of its techniques – unlike nearly all systems which are provided at no cost to the public in their full form by the authorities that designed and maintain them.

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

The next steps in AI weather forecasts seem to be new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the US weather-observing network.