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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the storm would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense hurricane. While I am unprepared to forecast that intensity yet given track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification is expected as the storm moves slowly over very warm ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

The AI model is the pioneer AI model dedicated to tropical cyclones, and currently the initial to outperform traditional weather forecasters at their specialty. Across all 13 Atlantic storms this season, the AI is top-performing – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the region. The confident prediction likely gave people in Jamaica extra time to get ready for the catastrophe, potentially preserving people and assets.

How Google’s 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 physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated 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 weather models we’ve relied upon,” he added.

Understanding Machine Learning

It’s important to note, the system is an example of AI training – a technique that has been employed in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns 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 primary systems that authorities have utilized for years that can take hours to process and require some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Nevertheless, the reality that Google’s model could outperform earlier gold-standard traditional systems so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The data is now large enough that it’s pretty clear this is not just chance.”

He noted that while the AI is outperforming all other models on predicting the future path of storms worldwide this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It had difficulty with another storm previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he said he plans to discuss with Google about how it can enhance the AI results even more helpful for experts by providing additional internal information they can utilize to assess exactly why it is producing its answers.

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

Wider Industry Developments

Historically, no a commercial entity that has developed a high-performance weather model which allows researchers a peek into its methods – in contrast to most systems which are provided free to the general audience in their full form by the authorities that created and operate them.

The company is not alone in adopting artificial intelligence to address difficult meteorological problems. The authorities are developing their own artificial intelligence systems in the development phase – which have also shown improved skill over previous non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies taking swings at previously difficult problems such as long-range forecasts and improved advance warnings of severe weather and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.