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

As the primary meteorologist on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa reaching a most intense storm. While I am not ready to forecast that intensity yet due to path variability, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the system drifts over very warm sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first AI model focused on hurricanes, and currently the initial to beat traditional meteorological experts at their own game. Across all 13 Atlantic storms this season, Google’s model is the best – even beating experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the disaster, potentially preserving people and assets.

How Google’s System Works

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

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer artificial intelligence systems are competitive with and, in some cases, more accurate than the less rapid physics-based weather models we’ve relied upon,” he added.

Understanding Machine Learning

To be sure, the system is an instance of AI training – a method that has been employed in data-heavy sciences like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have used for decades that can require many hours to process and require some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Still, the fact that the AI could exceed earlier gold-standard legacy models so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

He noted that while Google DeepMind is outperforming all competing systems on predicting the trajectory of storms globally this year, similar to other systems it sometimes errs on high-end intensity predictions wrong. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he said he intends to discuss with the company about how it can make the AI results more useful for forecasters by offering additional under-the-hood data they can utilize to evaluate the reasons it is coming up with its answers.

“A key concern that troubles me is that although these forecasts appear highly accurate, the output of the model is kind of a opaque process,” said Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has produced a top-level weather model which allows researchers a view of its methods – in contrast to most systems which are provided free to the public in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use artificial intelligence to address challenging meteorological problems. The US and European governments are developing their respective AI weather models in the development phase – which have demonstrated improved skill over earlier traditional systems.

The next steps in artificial intelligence predictions seem to be startup companies tackling formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.