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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

As the lead forecaster on duty, he predicted that in just 24 hours the storm would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 hurricane. While I am not ready to forecast 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 storm moves slowly over very warm sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and currently the first to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms this season, Google’s model is top-performing – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the region. The confident prediction probably provided residents extra time to prepare for the disaster, possibly saving lives and property.

How Google’s System Functions

Google’s model operates through identifying trends that traditional lengthy scientific weather models may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former meteorologist.

“What this hurricane season has proven in short order is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the less rapid traditional weather models we’ve relied upon,” he said.

Clarifying AI Technology

To be sure, Google DeepMind is an instance of machine learning – a method that has been used in research fields like meteorology for years – and is distinct from creative artificial intelligence 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 generate an result, and can operate on a desktop computer – in sharp difference to the primary systems that authorities have used for years that can require many hours to process and need some of the biggest supercomputers in the world.

Expert Responses and Future Advances

Still, the fact that the AI could exceed earlier gold-standard legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

He said that although the AI is beating all competing systems on predicting the trajectory of storms worldwide this year, like many AI models it sometimes errs on extreme strength forecasts wrong. It struggled with another storm previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he plans to talk with Google about how it can enhance the DeepMind output even more helpful for experts by providing extra internal information they can use to assess the reasons it is coming up with its conclusions.

“The one thing that troubles me is that while these predictions seem to be really, really good, the results of the model is essentially a black box,” said Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has produced a high-performance forecasting system which grants experts a view of its techniques – unlike nearly all other models which are offered at no cost to the general audience in their entirety by the authorities that created and operate them.

Google is not alone in adopting AI to address difficult weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the works – which have also shown improved skill over previous traditional systems.

The next steps in AI weather forecasts appear to involve new firms taking swings at formerly difficult problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is even deploying its own atmospheric sensors to fill the gaps in the national monitoring system.