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As Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a major tropical system.

As the primary meteorologist on duty, he predicted that in a single day the weather system would become a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had previously made such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense hurricane. While I am unprepared to predict that intensity yet given track uncertainty, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the system moves slowly over exceptionally hot sea temperatures which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to hurricanes, and currently the initial to beat traditional weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, the AI is top-performing – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the region. The confident prediction probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving people and assets.

The Way The System Functions

The AI system operates through spotting patterns that traditional lengthy scientific weather models may overlook.

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

“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 slower physics-based weather models we’ve relied upon,” he said.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been employed in data-heavy sciences like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to generate an answer, and can operate on a standard PC – in sharp difference to the primary systems that governments have utilized for years that can require many hours to run and need some of the biggest high-performance systems in the world.

Expert Reactions and Future Developments

Nevertheless, the reality that Google’s model could exceed earlier top-tier traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the most intense storms.

“It’s astonishing,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not just beginner’s luck.”

He noted that although Google DeepMind is outperforming all competing systems on forecasting the trajectory of hurricanes globally this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It struggled with another storm previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin said he plans to discuss with the company about how it can enhance the DeepMind output even more helpful for experts by offering additional internal information they can use to evaluate the reasons it is producing its answers.

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

Wider Industry Developments

Historically, no a private, for-profit company that has produced a high-performance forecasting system which allows researchers a view of its techniques – unlike most other models which are offered at no cost to the public in their entirety by the authorities that designed and maintain them.

The company is not alone in adopting artificial intelligence to address challenging meteorological problems. The authorities are developing their own AI weather models in the works – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts seem to be startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and flash flooding – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.