🔗 Share this article

When Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had ever issued such a bold forecast for quick intensification.

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

Increasing Reliance on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 hurricane. Although I am unprepared to predict that strength yet given track uncertainty, that remains a possibility.

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

Surpassing Conventional Systems

The AI model is the first AI model dedicated to tropical cyclones, and currently the first to beat standard weather forecasters at their own game. Through all 13 Atlantic storms so far this year, the AI is the best – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the region. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving lives and property.

How The Model Functions

The AI system operates through spotting patterns that conventional lengthy physics-based prediction systems may overlook.

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

“This season’s events has demonstrated in quick time is that the recent AI weather models are competitive with and, in certain instances, superior than the slower traditional forecasting tools we’ve relied upon,” he added.

Clarifying Machine Learning

To be sure, the system is an example of AI training – a technique that has been employed in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a manner that its system only requires minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the flagship models that governments have used for years that can take hours to process and need the largest supercomputers in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the reality that the AI could exceed previous gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense storms.

“I’m impressed,” said James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not just chance.”

He said that although Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he stated he intends to talk with the company about how it can make the DeepMind output more useful for forecasters by offering additional internal information they can use to assess the reasons it is coming up with its answers.

“A key concern that nags at me is that while these predictions seem to be really, really good, the output of the system is kind of a opaque process,” said Franklin.

Broader Industry Trends

There has never been a commercial entity that has produced a top-level forecasting system which allows researchers a peek into its techniques – unlike nearly all other models which are offered free to the general audience in their entirety by the authorities that designed and maintain them.

Google 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 works – which have demonstrated improved skill over earlier non-AI versions.

Future developments in AI weather forecasts seem to be startup companies taking swings at formerly difficult problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they have secured federal support to do so. A particular firm, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the national monitoring system.