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When Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would become a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had previously made this confident prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI simulation runs indicate Melissa becoming a most intense storm. While I am unprepared to forecast that strength at this time due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the storm moves slowly over very warm ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

The AI model is the first AI model focused on hurricanes, and now the first to beat traditional meteorological experts at their specialty. Through all 13 Atlantic storms this season, Google’s model is the best – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving lives and property.

How The System Works

The AI system operates through identifying trends that traditional time-intensive physics-based prediction systems may overlook.

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

“This season’s events has demonstrated in short order is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid traditional forecasting tools we’ve relied upon,” Lowry added.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a technique that has been employed in research fields like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its system only takes a few minutes to generate an result, and can operate on a desktop computer – in strong contrast to the flagship models that governments have used for decades that can take hours to run and require some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Advances

Still, the fact that Google’s model could exceed earlier gold-standard legacy models so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense storms.

“It’s astonishing,” commented James Franklin, a former expert. “The sample is sufficient that it’s evident this is not just chance.”

Franklin said that although the AI is beating all competing systems on forecasting the future path of hurricanes globally this year, like many AI models it occasionally gets extreme strength predictions wrong. It struggled with Hurricane Erin previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he said he intends to discuss with the company about how it can make the AI results more useful for experts by offering additional under-the-hood data they can utilize to evaluate the reasons it is coming up with its conclusions.

“A key concern that troubles me is that while these predictions seem to be highly accurate, the output of the system is kind of a opaque process,” remarked Franklin.

Wider Sector Trends

There has never been a commercial entity that has developed a top-level forecasting system which grants experts a view of its techniques – in contrast to nearly all systems which are offered at no cost to the public in their entirety by the authorities that created and operate them.

The company is not the only one in starting to use AI to solve challenging meteorological problems. The authorities are developing their respective artificial intelligence systems in the works – which have demonstrated better performance over earlier traditional systems.

The next steps in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they have secured federal support to do so. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the national monitoring system.