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When Developing Cyclone 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 forecasted that in a single day the storm would become a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Growing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa becoming a Category 5 hurricane. Although I am not ready to predict that intensity yet given path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the storm moves slowly over very warm ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms this season, Google’s model is the best – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to prepare for the disaster, potentially preserving lives and property.

How The System Functions

Google’s model operates through identifying trends that conventional time-intensive physics-based prediction systems may miss.

“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 ex forecaster.

“This season’s events has proven in quick time is that the newcomer AI weather models are on par with and, in some cases, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

It’s important to note, the system is an example of AI training – a technique that has been used in data-heavy sciences like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the primary systems that governments have used for decades that can require many hours to process and need the largest supercomputers in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the fact that Google’s model could exceed previous gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” said James Franklin, a former expert. “The sample is now large enough that it’s evident this is not just beginner’s luck.”

He said that while Google DeepMind is outperforming all other models on forecasting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It struggled with another storm previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he said he plans to talk with Google about how it can make the DeepMind output more useful for experts by providing additional under-the-hood data they can utilize to assess exactly why it is coming up with its answers.

“The one thing that troubles me is that although these predictions seem to be really, really good, the results of the system is kind of a black box,” remarked Franklin.

Wider Sector Developments

There has never been a commercial entity that has produced a high-performance weather model which grants experts a view of its methods – unlike most other models 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 solve challenging weather forecasting problems. The US and European governments are developing their own AI weather models in the development phase – which have demonstrated improved skill over previous non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is also launching its own atmospheric sensors to fill the gaps in the national monitoring system.