🔗 Share this article

When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the weather system would intensify into 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.

However, Papin possessed a secret advantage: AI technology in the form of the tech giant’s recently introduced DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members show Melissa reaching a most intense hurricane. Although I am not ready to forecast that strength at this time given path variability, that remains a possibility.

“There is a high probability that a phase of quick strengthening is expected as the system drifts over very warm sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the first AI model dedicated to tropical cyclones, and now the initial to outperform traditional weather forecasters at their specialty. Across all 13 Atlantic storms this season, the AI is the best – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the region. The confident prediction likely gave people in Jamaica extra time to get ready for the catastrophe, potentially preserving people and assets.

How Google’s Model Works

Google’s model operates through identifying trends that conventional lengthy scientific weather models may overlook.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and demanding,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve relied upon,” he said.

Understanding AI Technology

To be sure, Google DeepMind is an example of AI training – a method that has been used in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a manner that its system only requires minutes to generate an answer, and can operate on a desktop computer – in sharp difference to the primary systems that governments have utilized for years that can require many hours to run and need the largest supercomputers in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that the AI could outperform previous gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired forecaster. “The data is now large enough that it’s evident this is not a case of beginner’s luck.”

He said that although Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. 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, he said he plans to discuss with the company about how it can enhance the AI results even more helpful for forecasters by providing extra internal information they can utilize to assess exactly why it is coming up with its answers.

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

Broader Sector Trends

Historically, no a commercial entity that has developed a top-level weather model which grants experts a view of its methods – in contrast to nearly all systems which are offered at no cost to the general audience in their entirety by the authorities that created and operate them.

Google is not alone in adopting artificial intelligence to address challenging weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the works – which have demonstrated improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the national monitoring system.