🔗 Share this article

As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day 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 possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa reaching a most intense hurricane. While I am not ready to predict that intensity at this time due to track uncertainty, that is still plausible.

“It appears likely that a phase of rapid intensification will occur as the system moves slowly over very warm ocean waters which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer AI model dedicated to tropical cyclones, and now the first to outperform traditional meteorological experts at their specialty. Through all tropical systems so far this year, the AI is top-performing – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls ever documented in almost 200 years of data collection across the region. The confident prediction likely gave people in Jamaica extra time to get ready for the disaster, potentially preserving lives and property.

How Google’s Model Works

Google’s model works by spotting patterns that conventional time-intensive physics-based weather models may overlook.

“They do it far faster than their traditional counterparts, and the computing power is more affordable and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the slower physics-based weather models we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a technique that has been used in data-heavy sciences like weather science 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 model only takes a few minutes to come up with an result, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have used for years that can require many hours to run and require the largest supercomputers in the world.

Professional Responses and Upcoming Advances

Nevertheless, the fact that the AI could exceed earlier gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest storms.

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

Franklin noted that although Google DeepMind is beating all other models on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It struggled with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he plans to talk with Google about how it can enhance the DeepMind output even more helpful for experts by offering additional under-the-hood data they can utilize to evaluate exactly why it is coming up with its conclusions.

“The one thing that troubles me is that while these forecasts seem to be highly accurate, the output of the system is kind of a black box,” said Franklin.

Broader Sector Developments

Historically, no a commercial entity that has developed a high-performance forecasting system which allows researchers a peek into its methods – in contrast to most systems which are provided at no cost to the public in their entirety by the governments that created and operate them.

The company is not the only one in adopting AI to solve challenging meteorological problems. The authorities are developing their own AI weather models in the development phase – which have demonstrated better performance over earlier traditional systems.

Future developments 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 flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the national monitoring system.