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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence 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 Google DeepMind ensemble members show Melissa becoming a Category 5 storm. While I am unprepared to predict that intensity yet due to path variability, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the storm drifts over exceptionally hot ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Models

The AI model is the first AI model focused on tropical cyclones, and currently the initial to outperform traditional meteorological experts at their specialty. Across all tropical systems so far this year, the AI is top-performing – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in almost 200 years of data collection across the region. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets.

The Way The Model Functions

The AI system works by spotting patterns that conventional lengthy scientific weather models may miss.

“They do it much more quickly than their physics-based cousins, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, more accurate than the slower physics-based weather models we’ve relied upon,” he said.

Understanding AI Technology

To be sure, Google DeepMind is an instance of AI training – a method that has been used in data-heavy sciences like meteorology for years – and is not creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to generate an result, and can do so on a standard PC – in strong contrast to the primary systems that governments have used for years that can take hours to run and require some of the biggest supercomputers in the world.

Professional Reactions and Future Developments

Still, the fact that Google’s model could outperform previous top-tier legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” said James Franklin, a former expert. “The sample is now large enough that it’s pretty clear this is not just beginner’s luck.”

Franklin said that although the AI is beating all competing systems on forecasting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It had difficulty 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 the company about how it can enhance the DeepMind output more useful for forecasters by providing additional under-the-hood data they can utilize to assess the reasons it is producing its conclusions.

“A key concern that nags at me is that while these predictions seem to be really, really good, the output of the system is kind of a black box,” remarked Franklin.

Broader Sector Developments

Historically, no a commercial entity that has developed a high-performance forecasting system which grants experts a view of its methods – unlike most other models which are provided at no cost to the general audience in their full form by the authorities that designed and maintain them.

The company is not the only one in adopting artificial intelligence to address difficult meteorological problems. The authorities also have their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over earlier traditional systems.

The next steps in AI weather forecasts seem to be startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the national monitoring system.