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As Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued this confident forecast for quick intensification.

However, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa reaching a Category 5 hurricane. Although I am unprepared to predict that intensity at this time given path variability, that remains a possibility.

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

Surpassing Traditional Systems

The AI model is the pioneer AI model dedicated to tropical cyclones, and now the initial to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms this season, the AI is the best – even beating human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the catastrophe, potentially preserving people and assets.

How The Model Works

Google’s model works by identifying trends that conventional lengthy physics-based prediction systems may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, Google DeepMind is an example of AI training – a method that has been used in research fields like weather science for years – 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 come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have utilized for years that can require many hours to run and need the largest high-performance systems in the world.

Expert Reactions and Upcoming Developments

Nevertheless, the fact that Google’s model could exceed earlier gold-standard traditional systems so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the most intense storms.

“I’m impressed,” said James Franklin, a retired expert. “The data is now large enough that it’s evident this is not just chance.”

He noted that although the AI is outperforming all other models on predicting the trajectory of hurricanes globally this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

In the coming offseason, he stated he intends to talk with the company about how it can make the DeepMind output more useful for forecasters by providing additional under-the-hood data they can utilize to evaluate exactly why it is producing its answers.

“The one thing that nags at me is that while these forecasts appear really, really good, the results of the model is kind of a black box,” remarked Franklin.

Wider Industry Developments

Historically, no a private, for-profit company that has produced a high-performance forecasting system which allows researchers a peek into its techniques – in contrast to nearly all systems which are provided free to the public in their full form by the governments that created and operate them.

The company is not the only one in adopting artificial intelligence to address difficult meteorological problems. The authorities are developing their own artificial intelligence systems in the works – which have demonstrated improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the US weather-observing network.