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When Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made this confident prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa becoming a most intense hurricane. While I am unprepared to forecast that strength yet given path variability, that is still plausible.

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

Outperforming Conventional Models

The AI model is the pioneer AI model focused on tropical cyclones, and currently the first to beat standard weather forecasters at their own game. Through all tropical systems so far this year, the AI is top-performing – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the region. The confident prediction likely gave residents additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

The Way The System Functions

The AI system operates through identifying trends that conventional lengthy physics-based prediction systems may miss.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the newcomer AI weather models are competitive with and, in some cases, superior than the slower physics-based forecasting tools we’ve traditionally leaned on,” he said.

Understanding Machine Learning

To be sure, Google DeepMind is an example of machine learning – a method that has been employed in data-heavy sciences like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an answer, and can do so on a standard PC – in sharp difference to the flagship models that authorities have utilized for decades that can take hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Future Advances

Still, the fact that Google’s model could exceed earlier gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest storms.

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

Franklin said that while Google DeepMind is beating all competing systems on predicting the future path of storms globally this year, like many AI models it sometimes errs on extreme strength forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin stated he plans to discuss with the company about how it can enhance the DeepMind output more useful for experts by providing extra internal information they can utilize to assess the reasons it is producing its answers.

“A key concern that nags at me is that while these forecasts seem to be highly accurate, the results of the system is kind of a opaque process,” said Franklin.

Broader Sector Trends

Historically, no a commercial entity that has developed a high-performance weather model which grants experts a peek into its methods – unlike most other models which are provided at no cost to the general audience in their entirety by the authorities that created and operate them.

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

The next steps 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 are receiving federal support to do so. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.