🔗 Share this article

As Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting towards the coast of Jamaica. Not a single expert had ever issued such a bold prediction for quick intensification.

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

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa becoming a Category 5 storm. Although I am not ready to forecast that intensity yet given track uncertainty, that remains a possibility.

“There is a high probability that a phase of rapid intensification will occur as the system drifts over very warm sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

The AI model is the pioneer AI model focused on tropical cyclones, and now the first to beat traditional meteorological experts 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 maximum intensity, one of the strongest coastal impacts recorded in nearly two centuries of data collection across the region. Papin’s bold forecast probably provided residents extra time to get ready for the catastrophe, potentially preserving lives and property.

The Way Google’s System Works

Google’s model operates through spotting patterns that conventional lengthy physics-based prediction systems may miss.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a former forecaster.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are on par with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry added.

Understanding Machine Learning

To be sure, the system is an example of AI training – a method that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its system only requires minutes to generate an result, and can do so on a desktop computer – in strong contrast to the primary systems that governments have utilized for years that can require many hours to process and need the largest high-performance systems in the world.

Professional Responses and Upcoming Developments

Nevertheless, the reality that Google’s model could exceed earlier top-tier traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired forecaster. “The sample is sufficient that it’s evident this is not just chance.”

He said that while the AI is beating all competing systems on forecasting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength predictions inaccurate. It had difficulty with another storm previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

In the coming offseason, he stated he plans to talk with Google about how it can make the AI results even more helpful for experts by providing extra internal information they can utilize to assess the reasons it is producing its conclusions.

“The one thing that troubles me is that while these forecasts seem to be highly accurate, the output of the model is kind of a opaque process,” said Franklin.

Broader Industry Trends

Historically, no a commercial entity that has developed a top-level weather model which allows researchers a peek into its methods – unlike most systems which are offered free to the general audience in their full form by the authorities that created and operate them.

The company is not the only one in starting to use AI to address difficult weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have also shown improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is also launching its proprietary weather balloons to fill the gaps in the US weather-observing network.