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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system.

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had ever issued such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 AI ensemble members show Melissa becoming a Category 5 hurricane. While I am unprepared to forecast that intensity at this time given track uncertainty, that is still plausible.

“There is a high probability that a phase of rapid intensification is expected as the storm drifts over very warm ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the first artificial intelligence system focused on hurricanes, and now the initial to beat traditional weather forecasters at their specialty. Through all tropical systems so far this year, the AI is top-performing – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the region. Papin’s bold forecast likely gave residents extra time to get ready for the catastrophe, possibly saving lives and property.

How The System Works

The AI system operates through spotting patterns that traditional lengthy scientific prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the slower traditional weather models we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a technique that has been used in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its model only requires minutes to come up with an result, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have used for decades that can require many hours to run and need the largest supercomputers in the world.

Professional Responses and Upcoming Advances

Still, the fact that Google’s model could outperform earlier top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not a case of chance.”

Franklin said that although Google DeepMind is outperforming all other models on predicting the future path of hurricanes globally this year, like many AI models it occasionally gets extreme strength forecasts wrong. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he stated he plans to discuss with Google about how it can enhance the DeepMind output more useful for forecasters by offering additional internal information they can use to evaluate exactly why it is producing its answers.

“A key concern that nags at me is that although these forecasts seem to be highly accurate, the results of the model is essentially a black box,” said Franklin.

Wider Industry Developments

Historically, no a commercial entity that has developed a top-level forecasting system which allows researchers a view of its methods – unlike most systems which are provided free to the general audience in their entirety by the authorities that designed and maintain them.

The company is not alone in starting to use AI to solve difficult meteorological problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.