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

As the lead forecaster on duty, he forecasted that in a single day the storm would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form 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 storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on AI Predictions

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa becoming a Category 5 storm. While I am not ready to predict that strength yet due to track uncertainty, that is still plausible.

“It appears likely that a period of quick strengthening will occur as the storm moves slowly over very warm sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Models

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat standard meteorological experts at their own game. Across all tropical systems this season, Google’s model is the best – surpassing human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

How The Model Works

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

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, superior than the less rapid physics-based weather models we’ve traditionally leaned on,” he said.

Clarifying AI Technology

To be sure, Google DeepMind is an example of machine learning – a technique that has been employed in data-heavy sciences like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the primary systems that authorities have utilized for decades that can take hours to run and need some of the biggest supercomputers in the world.

Expert Reactions and Future Advances

Still, the reality that the AI could exceed earlier gold-standard legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the most intense storms.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not a case of chance.”

Franklin said that although Google DeepMind is beating all other models on predicting the future path of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, he stated he plans to talk with the company about how it can make the AI results more useful for experts by offering 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 although these forecasts appear highly accurate, the results of the model is kind of a opaque process,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has produced a high-performance forecasting system which allows researchers a peek into its methods – unlike nearly all other models which are offered free to the public in their full form by the authorities that created and operate them.

Google is not the only one in adopting artificial intelligence to solve difficult weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have also shown improved skill over earlier non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and flash flooding – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the national monitoring system.