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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had ever issued this confident forecast for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Growing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa becoming a most intense hurricane. Although I am unprepared to forecast that strength yet given path variability, that remains a possibility.

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

Surpassing Traditional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to hurricanes, and currently the initial to outperform standard meteorological experts at their own game. Across all tropical systems so far this year, the AI is the best – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave residents additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

How The Model Works

Google’s model operates through identifying trends that conventional time-intensive physics-based weather models may overlook.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid traditional forecasting tools 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 a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a manner that its system only takes a few minutes to generate an answer, and can do so on a desktop computer – in strong contrast 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.

Expert Responses and Upcoming Advances

Still, the fact that the AI could outperform earlier gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest storms.

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

Franklin noted that although the AI is beating all competing systems on predicting the trajectory of storms worldwide this year, like many AI models it sometimes errs on extreme strength predictions wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he intends to discuss with the company about how it can make the DeepMind output even more helpful for forecasters by providing additional under-the-hood data they can use to assess the reasons it is producing its answers.

“The one thing that troubles me is that while these predictions appear highly accurate, the results of the model is kind of a black box,” remarked Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has developed a top-level weather model which allows researchers a peek into its techniques – in contrast to most systems which are provided at no cost to the public in their full form by the governments that created and operate them.

Google is not alone in starting to use AI to address challenging meteorological problems. The authorities are developing their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over earlier non-AI versions.

The next steps in AI weather forecasts seem to be startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the national monitoring system.