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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and start shifting towards the coast of Jamaica. No forecaster had previously made such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a most intense hurricane. Although I am not ready to predict that strength at this time due to path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the storm moves slowly over very warm ocean waters which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer AI model focused on hurricanes, and currently the first to beat standard weather forecasters at their own game. Across all 13 Atlantic storms this season, Google’s model is top-performing – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls recorded in almost 200 years of record-keeping across the region. The confident prediction probably provided people in Jamaica extra time to get ready for the disaster, possibly saving lives and property.

How Google’s Model Functions

The AI system works by spotting patterns that conventional lengthy physics-based weather models may miss.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in short order is that the recent AI weather models are on par with and, in some cases, more accurate than the less rapid traditional forecasting tools we’ve relied upon,” he said.

Understanding AI Technology

It’s important to note, the system is an example of machine learning – a method that has been employed in data-heavy sciences like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its model only requires minutes to come up with an result, and can do so on a standard PC – in strong contrast to the flagship models that governments have utilized for years that can take hours to run and require the largest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Still, the fact that Google’s model could outperform previous top-tier traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

“I’m impressed,” said James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

He said that although the AI is beating all competing systems on predicting the future path of storms globally this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, he said he plans to discuss with Google about how it can make the DeepMind output even more helpful for experts by providing extra internal information they can utilize to evaluate exactly why it is producing its answers.

“A key concern that troubles me is that while these predictions appear really, really good, the results of the system is kind of a opaque process,” remarked Franklin.

Wider Sector Developments

There has never been a commercial entity that has produced a top-level weather model which allows researchers a view of its techniques – in contrast to most other models which are provided at no cost to the general audience in their full form by the authorities that designed and maintain them.

Google is not the only one in adopting artificial intelligence to address challenging weather forecasting problems. The US and European governments are developing their respective AI weather models in the works – which have demonstrated better performance over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the US weather-observing network.