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As Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a monster hurricane.

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had previously made this confident prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

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 most intense hurricane. While I am unprepared to predict that strength at this time given track uncertainty, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the system moves slowly over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer AI model dedicated to hurricanes, and now the initial to outperform standard weather forecasters at their own game. Through all tropical systems this season, Google’s model is top-performing – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, among the most powerful landfalls ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave residents extra time to get ready for the catastrophe, possibly saving lives and property.

The Way The Model Functions

The AI system works by spotting patterns that conventional time-intensive physics-based weather models may miss.

“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the slower traditional forecasting tools we’ve relied upon,” Lowry said.

Understanding Machine Learning

It’s important to note, the system is an instance of AI training – a technique that has been used in research fields like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training processes mounds of data and extracts trends from them in a such a way that its model 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 decades that can take hours to run and require some of the biggest supercomputers in the world.

Professional Responses and Upcoming Developments

Nevertheless, the fact that the AI could outperform previous gold-standard legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense storms.

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

He said that although the AI is outperforming all competing systems on forecasting the future path of storms globally this year, like many AI models it occasionally gets extreme strength forecasts wrong. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he intends to talk with the company about how it can make the DeepMind output even more helpful for experts by providing extra under-the-hood data they can use to assess exactly why it is coming up with its conclusions.

“The one thing that troubles me is that while these forecasts appear really, really good, the output of the model is kind of a opaque process,” said Franklin.

Wider Industry Trends

There has never been a private, for-profit company that has produced a high-performance weather model which allows researchers a view of its methods – in contrast to most other models which are provided at no cost to the general audience in their entirety by the governments that designed and maintain them.

The company is not alone in starting to use artificial intelligence to address challenging weather forecasting problems. The authorities also have their own AI weather models in the works – which have also shown improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.