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When Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and start shifting in the direction of 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 the tech giant’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a system of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists 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 show Melissa reaching a Category 5 storm. Although I am not ready to forecast that intensity at this time due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the system drifts over exceptionally hot sea temperatures which represent the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer AI model focused on hurricanes, and now the first to beat standard meteorological experts at their specialty. Through all 13 Atlantic storms this season, the AI is top-performing – surpassing human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the catastrophe, potentially preserving lives and property.

The Way Google’s Model Functions

Google’s model operates through spotting patterns that traditional lengthy scientific prediction systems may overlook.

“The AI performs far faster than their traditional counterparts, and the computing power is more affordable and demanding,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are on par with and, in some cases, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” he added.

Understanding AI Technology

To be sure, Google DeepMind is an example of AI training – a method that has been used in research fields like weather science for years – and is not generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an answer, and can do so on a standard PC – in sharp difference to the primary systems that governments have utilized for years that can take hours to process and need the largest high-performance systems in the world.

Expert Reactions and Future Advances

Still, the fact that Google’s model could outperform previous top-tier traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense storms.

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

He noted that while the AI is outperforming all competing systems on predicting the future path of storms worldwide this year, similar to other systems it occasionally gets extreme strength predictions wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin said he plans to talk with Google about how it can enhance the AI results even more helpful for forecasters by providing extra internal information they can utilize to assess exactly why it is producing its conclusions.

“The one thing that troubles me is that although these predictions seem to be highly accurate, the results of the system is kind of a black box,” said Franklin.

Wider Industry Developments

There has never been a commercial entity that has produced a high-performance weather model which grants experts a view of its techniques – unlike nearly all systems which are offered free to the general audience in their entirety by the authorities that designed and maintain them.

Google is not alone in adopting AI to solve difficult weather forecasting problems. The US and European governments are developing their own AI weather models in the works – which have also shown better performance over earlier traditional systems.

The next steps in AI weather forecasts seem to be startup companies tackling formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the national monitoring system.