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When Tropical Storm 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 a single day the storm would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made such a bold prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense hurricane. While I am not ready to predict that intensity at this time due to path variability, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the storm drifts over exceptionally hot sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the first to outperform standard meteorological experts at their specialty. Through all tropical systems so far this year, Google’s model is top-performing – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in nearly two centuries of data collection across the region. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving people and assets.

How The System Works

Google’s model works by identifying trends that traditional time-intensive physics-based prediction systems may miss.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” said Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the recent AI weather models are competitive with and, in some cases, more accurate than the slower physics-based weather models we’ve relied upon,” he added.

Clarifying Machine Learning

It’s important to note, the system is an example of machine learning – a technique that has been used in research fields like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a manner that its model only requires minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have utilized for decades that can take hours to run and require the largest high-performance systems in the world.

Professional Responses and Future Advances

Nevertheless, the reality that Google’s model could outperform earlier top-tier traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

“I’m impressed,” said James Franklin, a former expert. “The sample is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that while the AI is outperforming all competing systems on forecasting the trajectory of storms worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, he said he plans to discuss with Google about how it can make the DeepMind output more useful for experts by offering extra under-the-hood data they can use to evaluate the reasons it is producing its conclusions.

“A key concern that nags at me is that while these forecasts seem to be really, really good, the output of the system is essentially a black box,” said Franklin.

Broader Sector Developments

Historically, no a private, for-profit company that has produced a high-performance weather model which allows researchers a peek into its methods – unlike nearly all other models which are provided free to the public in their entirety by the governments that designed and maintain them.

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

Future developments in AI weather forecasts seem to be new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the national monitoring system.