🔗 Share this article

As Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a major tropical system.

Serving as lead forecaster on duty, he predicted that in a single day the storm would become a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had ever issued such a bold prediction for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Growing Reliance on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense hurricane. While I am unprepared to predict that strength yet due to track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the system moves slowly over very warm ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Models

The AI model is the first AI model focused on hurricanes, and currently the first to beat traditional meteorological experts at their specialty. Through 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 maximum strength, among the most powerful landfalls recorded in almost 200 years of data collection across the region. The confident prediction likely gave people in Jamaica extra time to get ready for the catastrophe, possibly saving people and assets.

The Way The System Works

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

“The AI performs far faster than their traditional counterparts, and the computing power is more affordable and time consuming,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the less rapid traditional forecasting tools we’ve relied upon,” he added.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a technique that has been used in research fields like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have used for years that can require many hours to run and require the largest high-performance systems in the world.

Professional Responses and Upcoming Advances

Still, the fact that the AI could exceed previous gold-standard legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not a case of beginner’s luck.”

He said that although the AI is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin said he intends to discuss with the company about how it can enhance the DeepMind output even more helpful for forecasters by offering extra internal information they can utilize to assess exactly why it is producing its conclusions.

“The one thing that troubles me is that although these forecasts seem to be really, really good, the results of the model is kind of a black box,” remarked Franklin.

Broader Sector Developments

Historically, no a private, for-profit company that has produced a high-performance weather model which grants experts a peek into its techniques – in contrast to nearly all systems which are provided at no cost to the public in their full form by the authorities that created and operate them.

The company is not the only one in adopting AI to solve challenging meteorological problems. The authorities also have their respective AI weather models in the works – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve new firms tackling previously tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the national monitoring system.