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When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

As the lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn towards the Jamaican shoreline. 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 cyclone prediction system – released for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa becoming a most intense hurricane. Although I am not ready to predict that intensity yet given path variability, that remains a possibility.

“There is a high probability that a phase of quick strengthening will occur as the system moves slowly over very warm sea temperatures which is the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and currently the first to outperform traditional weather forecasters at their specialty. Across all tropical systems this season, Google’s model is the best – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the disaster, possibly saving people and assets.

How The System Works

Google’s model operates through spotting patterns that conventional time-intensive scientific weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

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

Machine learning takes large datasets and extracts trends 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 decades that can take hours to process and require the largest supercomputers in the world.

Expert Responses and Future Advances

Nevertheless, the reality that Google’s model could exceed previous gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a former expert. “The sample 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 storms globally this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It struggled with another storm previously, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he said he intends to discuss with Google about how it can make the AI results even more helpful for experts by providing extra under-the-hood data they can utilize to evaluate exactly why it is coming up with its conclusions.

“The one thing that nags at me is that although these predictions appear highly accurate, the results of the system is kind of a opaque process,” remarked Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has developed a top-level weather model which allows researchers a peek into its methods – in contrast to most systems which are provided free to the public in their full form by the authorities that created and operate them.

Google is not alone in adopting artificial intelligence to solve challenging meteorological problems. The authorities are developing their own artificial intelligence systems in the development phase – which have also shown improved skill over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies tackling formerly tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.