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As Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and begin a turn towards the coast of Jamaica. No forecaster had ever issued this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise 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 system of remarkable power that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI ensemble members show Melissa becoming a most intense storm. Although I am unprepared to predict that strength yet due to path variability, that is still plausible.

“There is a high probability that a phase of quick strengthening will occur as the storm moves slowly over very warm sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the first AI model focused on hurricanes, and now the initial to outperform standard meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving people and assets.

How The Model Works

Google’s model works by identifying trends that conventional time-intensive physics-based weather models may miss.

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

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

Clarifying Machine Learning

To be sure, Google DeepMind is an example of machine learning – a technique that has been used in data-heavy sciences like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the primary systems that authorities have used for years that can take hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Future Developments

Still, the fact that Google’s model could exceed previous top-tier traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” said James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not just chance.”

He said that while Google DeepMind is beating all other models on predicting the future path of storms globally this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

During the next break, he stated he plans to discuss with Google about how it can make the AI results more useful for experts by providing extra internal information they can use to evaluate the reasons it is producing its conclusions.

“The one thing that troubles me is that while these predictions seem to be really, really good, the output of the system is kind of a opaque process,” said Franklin.

Broader Industry Trends

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 other models which are offered at no cost to the public in their full form by the governments that designed and maintain them.

Google is not alone in starting to use AI to address challenging meteorological problems. The US and European governments are developing their own AI weather models in the development phase – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.