🔗 Share this article

When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a major tropical system.

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and begin a turn in the direction of 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 guise of Google’s recently introduced DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa reaching a most intense storm. Although I am not ready to forecast that intensity at this time given track uncertainty, that remains a possibility.

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

Outperforming Traditional Systems

Google DeepMind is the pioneer AI model focused on tropical cyclones, and currently the first to beat standard weather forecasters at their own game. Through all 13 Atlantic storms this season, the AI is top-performing – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving lives and property.

The Way The Model Functions

The AI system works by spotting patterns that conventional time-intensive scientific prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the slower physics-based weather models we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, Google DeepMind is an example of machine learning – 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.

AI training processes mounds of data and pulls out patterns from them in a manner that its model only requires 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 take hours to run and need the largest high-performance systems in the world.

Professional Responses and Upcoming Developments

Still, the reality that the AI could exceed earlier top-tier legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“It’s astonishing,” commented James Franklin, a former forecaster. “The data is sufficient that it’s pretty clear this is not a case of chance.”

Franklin said that although Google DeepMind is beating all other models on forecasting the future path of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin said he intends to talk with Google about how it can enhance the AI results even more helpful for forecasters by providing extra under-the-hood data they can utilize to evaluate the reasons it is producing its conclusions.

“The one thing that troubles me is that although these forecasts seem to be really, really good, the output of the model is kind of a black box,” remarked Franklin.

Wider Sector Trends

Historically, no a private, for-profit company that has developed a high-performance forecasting system which allows researchers a peek into its methods – in contrast to nearly all systems 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 starting to use AI to solve challenging weather forecasting problems. The US and European governments also have their respective AI weather models in the development phase – which have also shown improved skill over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the US weather-observing network.