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When Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn towards the Jamaican shoreline. No forecaster had ever issued such a bold prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 AI ensemble members show Melissa becoming a Category 5 hurricane. Although I am unprepared to forecast that intensity yet due to track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the storm moves slowly over exceptionally hot sea temperatures which is the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and currently the initial to outperform standard weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is the best – even beating human forecasters on track predictions.

The hurricane 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 likely gave people in Jamaica extra time to prepare for the disaster, potentially preserving people and assets.

The Way The System Functions

Google’s model works by spotting patterns that conventional lengthy physics-based prediction systems may miss.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a former forecaster.

“What this hurricane season has proven in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, superior than the slower physics-based weather models we’ve traditionally leaned on,” he added.

Clarifying AI Technology

To be sure, the system is an instance of machine learning – a technique that has been used in data-heavy sciences like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that governments have used for years that can take hours to process and need some of the biggest supercomputers in the world.

Expert Responses and Upcoming Advances

Nevertheless, the fact that Google’s model could exceed earlier top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

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

Franklin noted that although Google DeepMind is outperforming all competing systems on forecasting the future path of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, Franklin stated he plans to talk with Google about how it can enhance the AI results even more helpful for experts by offering additional under-the-hood data they can utilize to evaluate exactly why it is coming up with its answers.

“The one thing that troubles me is that while these forecasts appear really, really good, 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 produced a high-performance weather model which allows researchers a view of its techniques – unlike nearly all systems which are offered at no cost to the general audience in their full form by the authorities that designed and maintain them.

Google is not alone in starting to use artificial intelligence to address challenging meteorological problems. The authorities are developing their respective AI weather models in the development phase – which have also shown improved skill over previous non-AI versions.

Future developments in AI weather forecasts seem to be new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the US weather-observing network.