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When Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. No forecaster had ever issued this confident forecast for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 hurricane. While I am not ready to forecast that intensity yet given track uncertainty, that is still plausible.

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

Surpassing Conventional Systems

Google DeepMind is the pioneer AI model focused on tropical cyclones, and now the first to outperform standard meteorological experts at their specialty. Across all tropical systems so far this year, the AI is the best – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at maximum strength, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the disaster, potentially preserving lives and property.

The Way Google’s System Works

The AI system operates through spotting patterns that traditional lengthy physics-based weather models may overlook.

“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 meteorologist.

“What this hurricane season has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve relied upon,” Lowry added.

Understanding AI Technology

To be sure, Google DeepMind is an instance of AI training – a technique that has been used in data-heavy sciences like meteorology for years – and is not generative AI like ChatGPT.

AI training processes large datasets and extracts trends from them in a such a way that its model only requires minutes to generate an result, and can operate on a standard PC – in strong contrast to the primary systems that governments have used for decades that can take hours to run and require some of the biggest supercomputers in the world.

Professional Responses and Upcoming Advances

Still, the reality that the AI could exceed earlier gold-standard legacy models so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” commented James Franklin, a retired forecaster. “The data is sufficient that it’s evident this is not just beginner’s luck.”

He said that while the AI is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength forecasts wrong. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he plans to discuss with Google about how it can make the AI results even more helpful for forecasters by providing extra internal information they can use to assess exactly why it is producing its conclusions.

“A key concern that nags at me is that although these forecasts seem to be highly accurate, the results of the system is essentially a opaque process,” remarked Franklin.

Broader Sector Trends

There has never been a private, for-profit company that has produced a high-performance forecasting system which allows researchers a view of its techniques – in contrast to most other models which are provided free to the public in their full form by the authorities that created and operate them.

Google is not alone in adopting artificial intelligence to address challenging meteorological problems. The US and European governments are developing their own artificial intelligence systems in the works – which have demonstrated improved skill over earlier traditional systems.

The next steps in artificial intelligence predictions seem to be new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they have secured 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.