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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had ever issued this confident forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa becoming a Category 5 storm. While I am unprepared to forecast that strength yet due to path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the storm drifts over very warm ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the first AI model focused on tropical cyclones, and now the first to beat standard meteorological experts at their own game. Through all 13 Atlantic storms this season, Google’s model is top-performing – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

How The System Functions

Google’s model works by identifying trends 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 time consuming,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the slower traditional forecasting tools we’ve relied upon,” Lowry said.

Understanding AI Technology

To be sure, the system is an instance of AI training – a method that has been used in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to generate an result, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have used for years that can require many hours to process and need the largest supercomputers in the world.

Professional Reactions and Future Advances

Still, the reality that Google’s model could exceed earlier gold-standard legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the most intense storms.

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

Franklin noted that although the AI is outperforming all competing systems on forecasting the future path of hurricanes globally this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It had difficulty with another storm previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

During the next break, he said 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 use to assess exactly why it is producing its answers.

“The one thing that nags at me is that while these predictions appear really, really good, the output of the system is kind of a opaque process,” said Franklin.

Wider Sector Developments

There has never been a private, for-profit company that has developed a top-level weather model which allows researchers a peek into its methods – in contrast to nearly all systems which are provided free to the general audience in their entirety by the authorities that designed and maintain them.

The company is not the only one in starting to use AI to solve challenging weather forecasting problems. The US and European governments are developing their own artificial intelligence systems in the development phase – which have also shown improved skill over previous traditional systems.

The next steps in AI weather forecasts seem to be startup companies taking swings at formerly tough-to-solve 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 deploying its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.