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As Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would become a severe hurricane and start shifting towards the coast of Jamaica. No forecaster had ever issued such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 storm. Although I am not ready to forecast that strength yet due to track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the system drifts over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the first to outperform traditional meteorological experts at their specialty. Across all tropical systems this season, Google’s model is top-performing – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful landfalls recorded in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided residents additional preparation time to get ready for the catastrophe, possibly saving people and assets.

The Way The System Works

The AI system operates through spotting patterns that conventional time-intensive scientific prediction systems may miss.

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in certain instances, superior than the slower traditional forecasting tools we’ve relied upon,” he added.

Clarifying AI Technology

To be sure, Google DeepMind is an example of AI training – a technique that has been used in data-heavy sciences like meteorology for years – and is not creative artificial intelligence 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 primary systems that governments have used for years that can take hours to run and require some of the biggest supercomputers in the world.

Professional Responses and Upcoming Developments

Still, the fact that the AI could exceed previous top-tier legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to predict 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 just chance.”

He said that while Google DeepMind is beating all competing systems on forecasting the trajectory of hurricanes globally this year, like many AI models it occasionally gets extreme strength predictions wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, he said he plans to talk with Google about how it can make the AI results more useful for experts by offering additional internal information they can utilize to assess exactly why it is coming up with its answers.

“The one thing that troubles me is that although these predictions seem to be highly accurate, the results of the system is kind of a opaque process,” remarked Franklin.

Broader Sector Trends

There has never been a commercial entity that has developed a top-level forecasting system 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 created and operate them.

Google is not the only one in starting to use AI to address difficult meteorological problems. The authorities are developing their own AI weather models in the development phase – which have demonstrated improved skill over earlier traditional systems.

Future developments in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the national monitoring system.