How Alphabet’s DeepMind Tool is Revolutionizing Hurricane Prediction with Speed

When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the storm would become a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Dependence on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 AI ensemble members show Melissa reaching a most intense storm. Although I am not ready to forecast that strength at this time due to track uncertainty, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the storm moves slowly over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and now the first to outperform traditional meteorological experts at their specialty. Across all tropical systems this season, Google’s model is the best – even beating experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts recorded in nearly two centuries of data collection across the region. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

The Way The Model Works

Google’s model operates through spotting patterns that traditional lengthy scientific weather models may overlook.

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

“This season’s events has demonstrated in short order is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” he said.

Understanding AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a method that has been employed in data-heavy sciences like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes large datasets 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 decades that can take hours to run and require some of the biggest supercomputers in the world.

Expert Responses and Upcoming Developments

Still, the fact that the AI could exceed earlier top-tier traditional systems so quickly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former expert. “The data is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

He said that while the AI is outperforming all other models on forecasting the future path of storms globally this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It struggled with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin said he plans to talk with the company about how it can enhance the AI results even more helpful for forecasters by providing extra internal information they can use to evaluate exactly why it is coming up with its answers.

“A key concern that nags at me is that while these predictions seem to be really, really good, the results of the system is essentially a black box,” remarked Franklin.

Wider Sector Trends

Historically, no a commercial entity that has developed a high-performance weather model which allows researchers a peek into its methods – in contrast to nearly all systems which are provided free to the public in their full form by the authorities that created and operate them.

Google is not alone in starting to use artificial intelligence to address challenging weather forecasting problems. The US and European governments are developing their own AI weather models in the development phase – which have demonstrated improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve new firms tackling previously difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they have secured US government funding to pursue this. One company, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the US weather-observing network.