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When Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise 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 tore through Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his confidence: “Roughly 40/50 AI simulation runs show Melissa becoming a Category 5 storm. Although I am not ready to forecast that strength yet due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the storm moves slowly over exceptionally hot sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and now the initial to outperform traditional meteorological experts at their specialty. Across all tropical systems this season, Google’s model is the best – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum intensity, among the most powerful landfalls recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the disaster, possibly saving lives and property.

How The System Functions

The AI system works by spotting patterns that conventional lengthy scientific weather models may miss.

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

“This season’s events has proven in short order is that the recent AI weather models are competitive with and, in certain instances, more accurate than the slower physics-based weather models we’ve relied upon,” he added.

Understanding AI Technology

To be sure, the system is an instance of machine learning – a technique that has been used in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a manner that its system only requires minutes to come up with an result, and can do so on a standard PC – in sharp difference to the primary systems that governments have used for decades that can require many hours to run and need the largest high-performance systems in the world.

Expert Reactions and Upcoming Developments

Still, the fact that the AI could exceed earlier top-tier legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense storms.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is now large enough that it’s evident this is not a case of chance.”

He noted that although the AI is outperforming all other models on predicting the future path of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he said he plans to talk with Google about how it can make the AI results even more helpful for forecasters by providing additional internal information they can use to evaluate the reasons it is producing its conclusions.

“The one thing that nags at me is that while these predictions appear highly accurate, the results of the system is kind of a black box,” remarked Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has produced a high-performance forecasting system which grants experts a view of its methods – unlike most systems which are offered free to the general audience in their entirety by the governments that created and operate them.

The company is not alone in starting to use artificial intelligence to address difficult weather forecasting problems. The US and European governments also have their own AI weather models in the development phase – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies tackling formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the national monitoring system.