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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. Although I am unprepared to forecast that strength yet due to path variability, that remains a possibility.

“It appears likely that a phase of quick strengthening is expected as the storm drifts over very warm sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer AI model dedicated to tropical cyclones, and now the initial to beat standard meteorological experts at their own game. Across all tropical systems this season, Google’s model is top-performing – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to get ready for the disaster, possibly saving people and assets.

How The System Works

Google’s model works by identifying trends that conventional time-intensive physics-based prediction systems may overlook.

“They do it far faster than their traditional counterparts, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the slower traditional forecasting tools we’ve relied upon,” he said.

Understanding AI Technology

To be sure, the system is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to generate an result, and can do so on a standard PC – in strong contrast to the primary systems that governments have utilized for years that can require many hours to process and need some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Advances

Still, the fact that the AI could exceed earlier gold-standard traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

“I’m impressed,” said James Franklin, a former expert. “The sample is sufficient that it’s evident this is not a case of chance.”

Franklin noted that although Google DeepMind is beating all competing systems on predicting the future path of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It had difficulty with another storm previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he stated he plans to talk with Google about how it can enhance the DeepMind output even more helpful for experts by providing extra internal information they can utilize to evaluate the reasons it is producing its conclusions.

“A key concern that nags at me is that while these forecasts appear really, really good, the output of the model is kind of a black box,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a peek into its techniques – in contrast to most systems which are offered at no cost to the public in their full form by the governments that created and operate them.

Google is not the only one in adopting artificial intelligence to address difficult meteorological problems. The US and European governments are developing their respective artificial intelligence systems in the works – which have also shown improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.