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When Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. No forecaster had ever issued such a bold prediction for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense storm. While I am unprepared to predict that intensity at this time due to path variability, that is still plausible.

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

Surpassing Conventional Systems

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and currently the initial to outperform traditional weather forecasters at their own game. Across all 13 Atlantic storms this season, the AI is top-performing – even beating experts on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving lives and property.

The Way Google’s System Works

The AI system works by spotting patterns that conventional time-intensive physics-based weather models may miss.

“The AI performs far faster than their traditional counterparts, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in some cases, superior than the slower physics-based weather models we’ve relied upon,” he said.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a method that has been employed in research fields like meteorology for years – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a such a way that its system only requires minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the primary systems that governments have used for years that can require many hours to run and require the largest supercomputers in the world.

Professional Responses and Future Developments

Still, the reality that the AI could outperform earlier gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense storms.

“I’m impressed,” said 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 storms worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

During the next break, he stated he plans to talk with the company about how it can enhance the AI results more useful for experts by offering extra under-the-hood data they can utilize to assess the reasons it is coming up with its conclusions.

“The one thing that nags at me is that while these predictions appear really, really good, the output of the model is kind of a black box,” remarked Franklin.

Wider Industry Trends

Historically, no a commercial entity that has produced a high-performance weather model which allows researchers a peek into its methods – in contrast to most other models which are provided at no cost to the general audience in their entirety by the authorities that designed and maintain them.

The company is not alone in adopting artificial intelligence to address challenging meteorological problems. The authorities also have their respective artificial intelligence systems in the works – which have demonstrated better performance over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies tackling formerly difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the national monitoring system.