🔗 Share this article

As Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued such a bold forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 storm. While I am not ready to forecast that strength yet due to path variability, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected as the system drifts over very warm sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first AI model dedicated to hurricanes, and now the first to beat traditional weather forecasters at their own game. Across all tropical systems this season, the AI is top-performing – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful landfalls recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided residents additional preparation time to get ready for the disaster, possibly saving lives and property.

How The System Works

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

“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in quick time is that the recent AI weather models are on par with and, in some cases, superior than the slower traditional weather models we’ve traditionally leaned on,” he added.

Understanding AI Technology

It’s important to note, the system is an example of AI training – a technique that has been employed in research fields like meteorology for years – and is distinct from 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 requires minutes to generate an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have utilized for decades that can require many hours to process and need some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the fact that the AI could outperform previous top-tier traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest weather systems.

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

He noted that although Google DeepMind is outperforming all competing systems on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, he said he plans to discuss with Google about how it can make the DeepMind output more useful for experts by providing extra under-the-hood data they can utilize to assess exactly why it is coming up with its answers.

“The one thing that nags at me is that although these forecasts seem to be highly accurate, the output of the system is kind of a opaque process,” said Franklin.

Wider Sector Trends

Historically, no a commercial entity that has developed a high-performance forecasting system which grants experts a peek into its techniques – unlike most other models which are provided free to the public in their entirety by the authorities that designed and maintain them.

The company is not alone in starting to use artificial intelligence to address difficult meteorological problems. The authorities also have their respective AI weather models in the development phase – which have demonstrated improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the US weather-observing network.