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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and start shifting towards the coast of Jamaica. Not a single expert had previously made such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on 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: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a most intense storm. Although I am unprepared to predict that strength yet due to track uncertainty, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the storm moves slowly over exceptionally hot sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the first artificial intelligence system focused on tropical cyclones, and now the initial to beat traditional weather forecasters at their own game. Through all tropical systems so far this year, Google’s model is top-performing – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the region. Papin’s bold forecast likely gave residents additional preparation time to prepare for the disaster, potentially preserving people and assets.

How The System Works

Google’s model operates through spotting patterns that conventional lengthy scientific prediction systems may miss.

“The AI performs far faster than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve relied upon,” Lowry said.

Understanding Machine Learning

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

Machine learning processes mounds of data and pulls out patterns from them in a such a way that its model only requires minutes to come up with an result, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have utilized for decades that can require many hours to process and need the largest high-performance systems in the world.

Expert Responses and Future Developments

Nevertheless, the fact that the AI could exceed previous top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former forecaster. “The data is now large enough that it’s pretty clear this is not just beginner’s luck.”

Franklin noted that while Google DeepMind is beating all other models on predicting the future path of hurricanes globally this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin stated he intends to talk with the company about how it can make the DeepMind output even more helpful for experts by offering additional under-the-hood data they can use to assess the reasons it is producing its answers.

“The one thing that troubles me is that although these forecasts appear really, really good, the output of the model is kind of a opaque process,” remarked Franklin.

Wider Sector Developments

There has never been a private, for-profit company that has produced a top-level forecasting system which grants experts a peek into its methods – in contrast to most other models which are provided free to the public in their full form by the governments that created and operate them.

Google is not alone in adopting AI to solve challenging meteorological problems. The authorities are developing their own AI weather models in the works – which have demonstrated improved skill over previous traditional systems.

Future developments in AI weather forecasts appear to involve startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they have secured federal support to do so. One company, WindBorne Systems, is even deploying its own weather balloons to fill the gaps in the US weather-observing network.