🔗 Share this article

As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had previously made such a bold prediction for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa reaching a most intense hurricane. Although I am unprepared 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 storm drifts over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and now the initial to outperform traditional weather forecasters at their own game. Across all 13 Atlantic storms this season, Google’s model is top-performing – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls recorded in nearly two centuries of data collection across the region. The confident prediction probably provided people in Jamaica extra time to prepare for the disaster, potentially preserving lives and property.

The Way The Model Functions

The AI system operates through spotting patterns that conventional lengthy physics-based weather models may overlook.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

To be sure, the system is an instance of AI training – a method that has been used in data-heavy sciences like weather science 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 takes a few minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the primary systems that authorities have utilized for years that can take hours to run and need the largest supercomputers in the world.

Expert Reactions and Future Advances

Still, the reality that the AI could exceed earlier top-tier traditional systems so quickly is nothing short of amazing to meteorologists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is sufficient that it’s evident this is not just chance.”

Franklin noted that although the AI is beating all other models on predicting the trajectory of storms worldwide this year, like many AI models it sometimes errs on high-end intensity predictions inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he said he intends to discuss with Google about how it can enhance the AI results more useful for experts by offering additional internal information they can use to assess the reasons it is producing its conclusions.

“The one thing that nags at me is that while these predictions seem to be really, really good, the output of the system is essentially a black box,” remarked Franklin.

Wider Industry Developments

There has never been a commercial entity that has produced a top-level weather model which grants experts a peek into its methods – unlike nearly all other models which are provided at no cost to the public in their entirety by the governments that designed and maintain them.

The company is not alone in adopting AI to solve challenging weather forecasting problems. The US and European governments also have their own AI weather models in the development phase – which have demonstrated better performance over previous traditional systems.

Future developments in AI weather forecasts appear to involve new firms taking swings at formerly tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the national monitoring system.