How Alphabet’s DeepMind Tool is Transforming Hurricane Forecasting with Rapid Pace

As Tropical Storm Melissa was churning 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 just 24 hours the weather system would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made such a bold prediction for rapid strengthening.

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

Increasing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 AI ensemble members show Melissa becoming a most intense hurricane. While I am unprepared to predict that strength at this time 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 exceptionally hot ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and currently the first to beat standard weather forecasters at their specialty. Across all tropical systems this season, Google’s model is top-performing – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls recorded in almost 200 years of data collection across the region. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the disaster, potentially preserving people and assets.

The Way Google’s Model Works

Google’s model works by identifying trends that conventional lengthy scientific weather models may overlook.

“The AI performs far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are on par with and, in some cases, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

To be sure, the system is an example of machine learning – a method that has been employed in data-heavy sciences like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to come up with an result, and can operate on a standard PC – in sharp difference to the primary systems that governments have utilized for years that can require many hours to run and need the largest supercomputers in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the reality that Google’s model could exceed earlier gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the most intense weather systems.

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

Franklin said that although the AI is outperforming all other models on forecasting the future path of storms worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

During the next break, he said he intends to discuss with Google about how it can make the DeepMind output even more helpful for forecasters by providing additional internal information they can use to assess exactly why it is producing its conclusions.

“The one thing that troubles me is that although these forecasts appear highly accurate, the output of the system is kind of a black box,” remarked Franklin.

Broader Sector Developments

Historically, no a commercial entity that has developed a top-level weather model which grants experts a peek into its methods – in contrast to nearly all systems which are offered free to the public in their full form by the governments that designed and maintain them.

The company is not alone in starting to use artificial intelligence to address challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have demonstrated better performance over previous non-AI versions.

The next steps in AI weather forecasts appear to involve new firms tackling previously difficult problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.