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

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – released 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 Forecasting

Meteorologists are heavily relying upon the AI system. During 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense hurricane. While I am not ready to forecast that intensity yet due to track uncertainty, that is still plausible.

“There is a high probability that a phase of quick strengthening will occur as the system moves slowly over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Models

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. Across all tropical systems so far this year, Google’s model is the best – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts recorded 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 catastrophe, possibly saving lives and property.

How The Model Works

Google’s model works by spotting patterns that conventional lengthy scientific weather models may overlook.

“The AI performs far faster than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a ex meteorologist.

“This season’s events has proven in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the slower physics-based forecasting tools we’ve traditionally leaned on,” Lowry said.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been employed in research fields like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the flagship models that governments have used for years that can require many hours to process and require the largest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the reality that the AI could exceed previous top-tier traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s evident this is not just chance.”

Franklin noted that while the AI is outperforming all other models on forecasting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength predictions wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

During the next break, he stated he plans to talk with Google about how it can enhance the DeepMind output more useful for forecasters by providing extra internal information they can use to evaluate exactly why it is producing its answers.

“The one thing that troubles me is that although these forecasts appear highly accurate, the output of the model is kind of a black box,” said Franklin.

Wider Industry Trends

There has never been a commercial entity that has produced a top-level forecasting system which grants experts a view of its methods – unlike most systems which are provided at no cost to the general audience in their full form by the governments that created and operate them.

Google is not alone in adopting AI to address challenging weather forecasting problems. The authorities also have their own AI weather models in the works – which have demonstrated better performance over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.