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When Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the storm would become a category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made this confident forecast for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists 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 certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 storm. While I am not ready to predict that intensity yet given path variability, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the system moves slowly over exceptionally hot ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to beat traditional meteorological experts at their own game. Through all 13 Atlantic storms so far this year, the AI is the best – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the region. The confident prediction likely gave residents extra time to prepare for the catastrophe, possibly saving people and assets.

The Way Google’s System Functions

Google’s model works by spotting patterns that conventional lengthy physics-based weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has proven in short order is that the recent AI weather models are on par with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

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

AI training processes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to generate an answer, and can operate on a standard PC – in strong contrast to the flagship models that governments have used for decades that can require many hours to run and need some of the biggest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the reality that Google’s model could outperform previous top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former expert. “The data is now large enough that it’s pretty clear this is not a case of chance.”

Franklin said that although the AI is beating all other models on predicting the trajectory of storms worldwide this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It had difficulty with another storm previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he stated he intends to discuss with the company about how it can enhance the DeepMind output even more helpful for experts by offering additional internal information they can use to evaluate the reasons it is coming up with its conclusions.

“The one thing that troubles me is that while these predictions seem to be really, really good, the output of the system is essentially a opaque process,” remarked Franklin.

Wider Industry Developments

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

The company is not the only one in adopting AI to solve difficult meteorological problems. The US and European governments also have their own AI weather models in the development phase – which have also shown improved skill over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve startup companies tackling previously tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the US weather-observing network.