🔗 Share this article

When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he predicted that in a single day the storm would become a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made this confident prediction for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a most intense storm. While I am unprepared to predict that strength at this time given track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the system drifts over very warm sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and currently the first to beat standard weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, the AI is the best – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided residents additional preparation time to get ready for the catastrophe, possibly saving people and assets.

The Way The System Works

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

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“This season’s events has proven in short order is that the recent AI weather models are on par with and, in certain instances, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he said.

Clarifying AI Technology

To be sure, the system is an instance of AI training – a technique that has been used in research fields like weather science for a long time – and is not creative artificial intelligence 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 answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have utilized for decades that can require many hours to run and require some of the biggest high-performance systems in the world.

Professional Responses and Future Developments

Still, the fact that Google’s model could outperform earlier gold-standard legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former expert. “The sample is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

He said that although the AI is outperforming all competing systems on forecasting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It struggled with another storm previously, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he stated he plans to talk with Google about how it can make the AI results more useful for experts by providing extra under-the-hood data they can use to evaluate the reasons it is producing its answers.

“A key concern that nags at me is that although these predictions appear really, really good, the results of the system is essentially a black box,” remarked Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has produced a top-level weather model which grants experts a peek into its techniques – in contrast to most other models which are offered free to the public in their full form by the authorities that designed and maintain them.

The company is not alone in adopting artificial intelligence to solve challenging meteorological problems. The authorities are developing their own artificial intelligence systems in the development phase – which have demonstrated better performance over earlier traditional systems.

The next steps in artificial intelligence predictions seem to be new firms taking swings at formerly tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.