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When Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. No forecaster had previously made such a bold forecast for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a most intense storm. Although I am unprepared to forecast that strength at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening will occur as the storm moves slowly over exceptionally hot ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the first AI model focused on hurricanes, and now the initial to beat standard weather forecasters at their specialty. Across all tropical systems so far this year, Google’s model is top-performing – even beating human forecasters on track predictions.

Melissa ultimately struck in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the region. The confident prediction likely gave residents additional preparation time to get ready for the disaster, possibly saving people and assets.

How Google’s System Works

The AI system operates through identifying trends that conventional lengthy scientific weather models may overlook.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“This season’s events has proven in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

To be sure, the system is an instance of AI training – a method that has been employed in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to generate an result, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have used for decades that can require many hours to run and require the largest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the fact that Google’s model could exceed earlier top-tier traditional systems so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the most intense storms.

“It’s astonishing,” commented James Franklin, a retired expert. “The data 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 trajectory of storms worldwide this year, like many AI models it sometimes errs on extreme strength predictions wrong. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he plans to discuss with Google about how it can enhance the DeepMind output more useful for forecasters by offering extra internal information they can use to assess the reasons it is coming up with its answers.

“A key concern that troubles me is that although these forecasts appear really, really good, the results of the system is essentially a opaque process,” remarked Franklin.

Wider Industry Developments

Historically, no a commercial entity that has developed a top-level forecasting system which allows researchers a peek into its techniques – in contrast to most other models which are offered free to the public in their entirety by the governments that designed and maintain them.

Google is not alone in adopting AI to solve difficult meteorological problems. The US and European governments also have their respective AI weather models in the works – which have demonstrated improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at previously difficult problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and sudden deluges – and they have secured federal support to do so. One company, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.