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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the primary meteorologist on duty, he predicted that in a single day the storm would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s recently introduced DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa becoming a most intense storm. While I am not ready to predict that intensity at this time given path variability, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the system drifts over very warm ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first AI model focused on tropical cyclones, and currently the first to beat standard weather forecasters at their own game. Through all tropical systems this season, the AI is top-performing – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets.

How The System Works

The AI system works by identifying trends that traditional time-intensive scientific prediction systems may overlook.

“They do it much more quickly 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 proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, more accurate than the slower physics-based weather models we’ve relied upon,” he added.

Understanding Machine Learning

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

Machine learning processes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to generate an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have used for decades that can take hours to process and need some of the biggest supercomputers in the world.

Professional Responses and Upcoming Advances

Still, the fact that Google’s model could exceed earlier top-tier legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired forecaster. “The sample is sufficient that it’s evident this is not just chance.”

Franklin said that although the AI is outperforming all competing systems on predicting the trajectory of storms globally this year, like many AI models it sometimes errs on 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.

During the next break, he stated he plans to discuss with Google about how it can enhance the DeepMind output even more helpful for forecasters by offering additional internal information they can utilize to assess the reasons it is coming up with its conclusions.

“A key concern that nags at me is that although these predictions appear highly accurate, the output of the system is essentially a black box,” remarked Franklin.

Broader Industry Developments

There has never been a commercial entity that has produced a high-performance forecasting system which grants experts a peek into its techniques – unlike nearly all systems which are offered free to the general audience in their entirety by the governments that created and operate them.

The company is not alone in adopting AI to address difficult weather forecasting problems. The authorities also have their own artificial intelligence systems in the works – which have also shown improved skill 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 have secured federal support to do so. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.