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When Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued this confident forecast for rapid strengthening.

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

Growing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that Google’s model was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 storm. Although I am unprepared to predict that strength yet due to track uncertainty, that remains a possibility.

“There is a high probability that a period of rapid intensification is expected as the storm moves slowly over very warm ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer AI model dedicated to hurricanes, and currently the initial to outperform traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, the AI is top-performing – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave residents extra time to prepare for the disaster, potentially preserving people and assets.

The Way Google’s Model Works

The AI system works by spotting patterns that traditional time-intensive scientific weather models may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” stated Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the less rapid traditional forecasting tools we’ve relied upon,” Lowry added.

Clarifying AI Technology

It’s important to note, the system is an instance of AI training – a technique that has been used in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

AI training takes mounds of data and extracts trends from them in a such a way that its system only requires minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the flagship models that governments have utilized for decades that can require many hours to run and require some of the biggest supercomputers in the world.

Expert Responses and Future Developments

Nevertheless, the reality that the AI could outperform earlier gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense storms.

“It’s astonishing,” said James Franklin, a former expert. “The sample is now large enough that it’s evident this is not just beginner’s luck.”

He said that while the AI is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he said he intends to discuss 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 coming up with its answers.

“The one thing that nags at me is that although these forecasts appear highly accurate, the output of the system is essentially a black box,” said Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has produced a top-level weather model which allows researchers a peek into its techniques – in contrast to nearly all systems which are provided free to the general audience in their full form by the governments that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to solve difficult weather forecasting problems. The authorities are developing their own artificial intelligence systems in the works – which have demonstrated improved skill over earlier non-AI versions.

The next steps in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the national monitoring system.