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As Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 hurricane. Although I am not ready to forecast that intensity yet due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the system drifts over exceptionally hot ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer AI model dedicated to hurricanes, and now the initial to beat standard weather forecasters at their own game. Through all tropical systems this season, the AI is top-performing – even beating experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the disaster, possibly saving lives and property.

The Way The Model Works

Google’s model works by identifying trends that traditional 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 time consuming,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he said.

Understanding AI Technology

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

AI training takes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the flagship models that governments have used for decades that can take hours to run and require some of the biggest supercomputers in the world.

Professional Reactions and Future Developments

Nevertheless, the reality that Google’s model could exceed earlier top-tier traditional systems so quickly is nothing short of amazing to meteorologists who have spent their careers trying to predict the most intense weather systems.

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

He noted that while the AI is beating all other models on forecasting the trajectory of storms worldwide this year, like many AI models it occasionally gets extreme strength predictions wrong. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

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

“A key concern that troubles me is that while these forecasts appear really, really good, the output of the system is kind of a black box,” remarked Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has developed a top-level forecasting system which allows researchers a view of its techniques – unlike most systems which are provided free to the public in their full form by the authorities that created and operate them.

The company is not the only one in adopting artificial intelligence to solve challenging weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks 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.