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When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica.

Increasing 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 Google’s model was a primary reason for his confidence: “Approximately 40/50 AI ensemble members show Melissa reaching a Category 5 hurricane. Although I am not ready to predict that intensity yet given track uncertainty, that is still plausible.

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

Outperforming Traditional Models

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the initial to beat standard meteorological experts at their own game. Through all 13 Atlantic storms this season, the AI is top-performing – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest 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 disaster, possibly saving lives and property.

The Way The System Functions

The AI system works by identifying trends that conventional time-intensive scientific weather models may miss.

“The AI performs far faster than their traditional counterparts, and the computing power is more affordable and time consuming,” stated Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a technique that has been employed in research fields like meteorology for years – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets 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 standard PC – in strong contrast to the primary systems that authorities have utilized for years that can require many hours to process and need some of the biggest high-performance systems in the world.

Expert Responses and Future Developments

Nevertheless, the fact that Google’s model could outperform previous top-tier legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” said James Franklin, a retired expert. “The data is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that although Google DeepMind is outperforming all other models on forecasting the future path of storms globally this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It struggled with another storm previously, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

In the coming offseason, he stated he intends to talk with Google about how it can make the AI results more useful for experts by providing additional under-the-hood data they can utilize to evaluate exactly why it is coming up with its answers.

“A key concern that troubles me is that while these predictions seem to be really, really good, the output of the model is essentially a black box,” said Franklin.

Broader Industry Trends

There has never been a commercial entity that has produced a high-performance weather model which allows researchers a peek into its methods – in contrast to nearly all systems which are provided free to the public in their full form by the governments that designed and maintain them.

The company is not the only one in adopting AI to solve challenging meteorological problems. The authorities are developing their own artificial intelligence systems in the development phase – which have demonstrated better performance over previous traditional systems.

Future developments in AI weather forecasts appear to involve new firms taking swings at previously difficult problems such as long-range forecasts and improved early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the national monitoring system.