🔗 Share this article

As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. While I am not ready to forecast that strength yet given path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification is expected as the storm drifts over exceptionally hot sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the first AI model dedicated to hurricanes, and now the initial to beat traditional meteorological experts at their own game. Across all 13 Atlantic storms so far this year, the AI is top-performing – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving lives and property.

How Google’s System Works

Google’s model works by spotting patterns that traditional lengthy physics-based weather models may overlook.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid physics-based weather models we’ve relied upon,” Lowry added.

Understanding AI Technology

To be sure, the system is an instance of AI training – a technique that has been used in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a such a way that its model only requires minutes to generate an answer, and can operate on a desktop computer – in sharp difference to the flagship models that governments have utilized for years that can take hours to process and require the largest supercomputers in the world.

Professional Responses and Upcoming Developments

Nevertheless, the reality that the AI could outperform earlier gold-standard traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s evident this is not a case of chance.”

Franklin noted that although the AI is outperforming all other models on forecasting the future path of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he plans to talk with Google about how it can enhance the DeepMind output even more helpful for experts by offering additional 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 predictions appear really, really good, the results of the model is kind of a opaque process,” said Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has developed a top-level forecasting system which allows researchers a peek into its methods – unlike nearly all systems which are provided at no cost to the public in their entirety by the authorities that designed and maintain them.

Google is not the only one in adopting AI to address difficult meteorological problems. The authorities also have their respective AI weather models in the works – which have demonstrated better performance over earlier traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies tackling previously difficult problems such as long-range forecasts 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 launching its own weather balloons to address deficiencies in the national monitoring system.