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As Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in just 24 hours the storm would become a category 4 hurricane and start shifting towards the coast of Jamaica. Not a single expert had ever issued such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa becoming a Category 5 storm. While I am unprepared to forecast that strength at this time given track uncertainty, that is still plausible.

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

Surpassing Traditional Models

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and now the initial to outperform standard meteorological experts at their own game. Through all tropical systems this season, the AI is top-performing – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in almost 200 years of data collection across the region. The confident prediction likely gave people in Jamaica extra time to get ready for the disaster, potentially preserving lives and property.

The Way The Model Functions

Google’s model works by spotting patterns that conventional time-intensive physics-based weather models may overlook.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the slower physics-based forecasting tools we’ve traditionally leaned on,” he said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have used for years that can require many hours to process and need the largest supercomputers in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the reality that Google’s model could exceed earlier top-tier legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a retired forecaster. “The data is sufficient that it’s evident this is not a case of chance.”

He noted that although the AI is outperforming all other models on forecasting the future path of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity predictions wrong. It struggled with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he stated he intends to discuss with the company about how it can enhance the DeepMind output more useful for experts by offering extra under-the-hood data they can use to evaluate the reasons it is coming up with its answers.

“A key concern that nags at me is that although these predictions appear really, really good, the output of the model is essentially a black box,” said Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has developed a top-level forecasting system which allows researchers a peek into its techniques – unlike most other models which are provided free to the general audience in their full form by the governments that created and operate them.

The company is not the only one in starting to use AI to solve challenging weather forecasting problems. The authorities also have their respective artificial intelligence systems in the development phase – which have demonstrated better performance over previous 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 better early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.