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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.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a most intense storm. While I am unprepared to forecast that intensity yet due to path variability, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the storm moves slowly over exceptionally hot ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the first AI model dedicated to tropical cyclones, and currently the initial to outperform traditional meteorological experts at their specialty. Through all 13 Atlantic storms so far this year, the AI is top-performing – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts recorded in almost 200 years of data collection across the region. Papin’s bold forecast likely gave residents extra time to prepare for the disaster, possibly saving lives and property.

How Google’s Model Works

Google’s model operates through spotting patterns that traditional time-intensive scientific prediction systems may miss.

“The AI performs much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has proven in quick time is that the newcomer AI weather models are competitive with and, in some cases, superior than the slower traditional weather models we’ve traditionally leaned on,” he said.

Clarifying AI Technology

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 not creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the primary systems that governments have utilized for decades that can require many hours to process and need some of the biggest supercomputers in the world.

Expert Responses and Upcoming Developments

Still, the fact that Google’s model could exceed previous gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense storms.

“I’m impressed,” said James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not a case of beginner’s luck.”

He said that while Google DeepMind is outperforming all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he intends to talk with the company about how it can enhance the DeepMind output even more helpful for experts by offering additional under-the-hood data they can use to assess exactly why it is coming up with its answers.

“A key concern that nags at me is that although these predictions seem to be really, really good, the output of the model is essentially a opaque process,” remarked Franklin.

Broader Sector Developments

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its techniques – in contrast to nearly all other models which are offered free to the general audience in their entirety by the governments that designed and maintain them.

Google is not alone in starting to use artificial intelligence to address challenging meteorological problems. The authorities also have their own AI weather models in the development phase – which have demonstrated better performance over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve new firms tackling formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they have secured federal support to do so. A particular firm, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.