🔗 Share this article

When Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. No forecaster had previously made such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 AI ensemble members show Melissa reaching a Category 5 storm. While I am unprepared to forecast that intensity at this time given track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening will occur as the storm moves slowly over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer AI model focused on hurricanes, and now the first to beat traditional weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave residents additional preparation time to prepare for the catastrophe, possibly saving people and assets.

The Way The Model Works

Google’s model operates through spotting patterns that traditional time-intensive physics-based prediction systems may overlook.

“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

“This season’s events has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example 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.

Machine learning processes mounds of data and extracts trends from them in a such a way that its system only requires minutes to generate an result, and can do so on a standard PC – in sharp difference to the primary systems that governments have used for years that can require many hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Future Advances

Nevertheless, the reality that Google’s model could exceed previous top-tier legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not a case of chance.”

He said that although Google DeepMind is outperforming all competing systems on predicting the future path of storms globally this year, like many AI models it occasionally gets high-end intensity predictions wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he intends to talk with Google about how it can make the AI results more useful for forecasters by offering additional internal information they can utilize to evaluate exactly why it is coming up with its conclusions.

“A key concern that troubles me is that while these predictions appear highly accurate, the output of the system is kind of a black box,” remarked Franklin.

Wider Sector Developments

There has never been a commercial entity that has produced a high-performance weather model which grants experts a view of its techniques – in contrast to nearly all other models which are provided free to the general audience in their full form by the authorities that designed and maintain them.

Google is not alone in adopting artificial intelligence to address challenging weather forecasting problems. The US and European governments are developing their own artificial intelligence systems in the works – which have also shown improved skill over previous traditional systems.

Future developments in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.