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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had ever issued such a bold forecast for rapid strengthening.

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

Growing Reliance on AI Forecasting

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense storm. Although I am unprepared to forecast that intensity yet given path variability, that is still plausible.

“There is a high probability that a period of quick strengthening will occur as the storm moves slowly over exceptionally hot ocean waters which is the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the first AI model focused on hurricanes, and now the first to outperform standard meteorological experts at their own game. Across all tropical systems this season, Google’s model is top-performing – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the disaster, possibly saving lives and property.

The Way The System Works

The AI system works by spotting patterns that traditional lengthy physics-based prediction systems may miss.

“They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a former forecaster.

“What this hurricane season has proven in quick time is that the newcomer AI weather models are on par with and, in some cases, more accurate than the slower traditional weather models we’ve relied upon,” Lowry said.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of machine learning – a technique that has been used in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an result, and can operate on a standard PC – in strong contrast to the flagship models that authorities have used for decades that can take hours to run and need the largest supercomputers in the world.

Expert Responses and Future Advances

Still, the fact that Google’s model could outperform previous top-tier legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The data is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that while the AI is beating all competing systems on predicting the future path of storms worldwide this year, like many AI models it occasionally gets extreme strength forecasts wrong. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

During the next break, he stated he plans to talk with Google about how it can make the AI results more useful for forecasters by providing extra under-the-hood data they can use to assess the reasons it is coming up with its answers.

“The one thing that troubles me is that while these predictions seem to be really, really good, the results of the system is kind of a opaque process,” said Franklin.

Broader Sector Trends

There has never been a private, for-profit company that has produced a high-performance weather model which grants experts a peek into its methods – in contrast to most other models which are offered free to the public in their entirety by the authorities that created and operate them.

The company is not alone in adopting artificial intelligence to address challenging weather forecasting problems. The authorities also have their own artificial intelligence systems in the development phase – which have also shown improved skill over previous traditional systems.

The next steps in artificial intelligence predictions seem to be new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and better 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 proprietary atmospheric sensors to address deficiencies in the US weather-observing network.