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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would intensify into a severe hurricane and start shifting towards the coast of Jamaica. Not a single expert had previously made such a bold prediction for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s new DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa becoming a most intense storm. Although I am not ready to predict that strength at this time given path variability, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the system moves slowly over very warm ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the pioneer AI model dedicated to hurricanes, and currently the initial to beat traditional meteorological experts at their specialty. Across all tropical systems this season, Google’s model is top-performing – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, 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 extra time to get ready for the disaster, possibly saving people and assets.

The Way The System Functions

Google’s model operates through spotting patterns that conventional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in quick time is that the newcomer AI weather models are on par with and, in some cases, more accurate than the slower traditional forecasting tools we’ve relied upon,” Lowry added.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of machine learning – a technique that has been used in data-heavy sciences like meteorology for years – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its model only requires minutes to come up with an answer, and can do so on a desktop computer – in sharp difference to the primary systems that authorities have utilized for decades that can require many hours to run and need the largest high-performance systems in the world.

Professional Responses and Future Advances

Still, the fact that the AI could exceed earlier top-tier legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” said James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not just beginner’s luck.”

He noted that although the AI is outperforming all other models on predicting the future path of storms globally this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, Franklin stated he plans to talk with the company about how it can make the DeepMind output even more helpful for forecasters by offering extra under-the-hood data they can utilize to evaluate the reasons it is producing its answers.

“The one thing that troubles me is that although these forecasts seem to be highly accurate, the results of the system is essentially a opaque process,” remarked Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has developed a high-performance weather model which allows researchers a view of its methods – in contrast to most other models which are offered at no cost to the general audience in their full form by the governments that created and operate them.

Google is not the only one in starting to use artificial intelligence to solve challenging weather forecasting problems. The authorities are developing their respective AI weather models in the development phase – which have demonstrated better performance over previous traditional systems.

The next steps in AI weather forecasts seem to be startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to do so. One company, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the US weather-observing network.