🔗 Share this article

As Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the storm would become a severe hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had previously made this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense storm. Although I am not ready to predict that intensity at this time given path variability, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the system moves slowly over exceptionally hot sea temperatures which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

The AI model is the pioneer artificial intelligence system focused on hurricanes, and now the initial to beat standard meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the disaster, possibly saving people and assets.

The Way Google’s System Works

The AI system operates through identifying trends that traditional lengthy scientific prediction systems may overlook.

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in quick time is that the newcomer AI weather models are on par with and, in certain instances, superior than the slower traditional weather models we’ve relied upon,” he added.

Clarifying 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 a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a such a way that its system only requires minutes to generate an result, and can do so on a standard PC – in sharp difference to the flagship models that authorities have utilized for decades that can take hours to process and require the largest supercomputers in the world.

Professional Responses and Future Developments

Still, the reality that Google’s model could exceed earlier gold-standard traditional systems 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 expert. “The sample is now large enough that it’s evident this is not just beginner’s luck.”

He noted that while Google DeepMind is outperforming all competing systems on predicting the future path of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, he stated he plans to talk with the company about how it can make the DeepMind output more useful for experts by offering extra internal information they can use to evaluate exactly why it is coming up with its answers.

“A key concern that nags at me is that while these predictions appear highly accurate, the output of the system is kind of a opaque process,” remarked Franklin.

Wider Industry Developments

Historically, no a commercial entity that has developed a high-performance forecasting system which grants experts a peek into its methods – in contrast to most other models which are provided at no cost to the general audience in their entirety by the governments that created and operate them.

The company is not the only one in adopting artificial intelligence to solve difficult meteorological problems. The US and European governments also have their respective artificial intelligence systems in the development phase – which have also shown improved skill over earlier traditional systems.

The next steps in artificial intelligence predictions appear to involve new firms tackling previously tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. One company, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the national monitoring system.