The Way Alphabet’s AI Research Tool is Transforming Hurricane Prediction with Speed

When Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had ever issued such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’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.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense hurricane. While I am unprepared to predict that intensity yet due to path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification is expected as the system drifts over very warm ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the first AI model dedicated to tropical cyclones, and now the initial to outperform standard meteorological experts at their own game. Across all tropical systems this season, Google’s model is the best – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to prepare for the catastrophe, possibly saving lives and property.

The Way The System Functions

Google’s model operates through identifying trends that conventional time-intensive physics-based weather models may miss.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a former forecaster.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid traditional weather models we’ve relied upon,” Lowry said.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a method that has been employed in data-heavy sciences like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the primary systems that governments have used for decades that can require many hours to run and require the largest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the reality that Google’s model could exceed previous gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the world’s strongest storms.

“I’m impressed,” said James Franklin, a former forecaster. “The sample is sufficient that it’s pretty clear this is not just chance.”

Franklin noted that although Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, he stated he plans to talk with Google about how it can make the DeepMind output more useful for forecasters by offering additional under-the-hood data they can use to evaluate the reasons it is producing its answers.

“The one thing that troubles me is that although these predictions seem to be highly accurate, the output of the system is essentially a black box,” remarked Franklin.

Wider Industry Trends

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its methods – in contrast to nearly all other models which are provided at no cost to the general audience in their entirety by the governments that created and operate them.

The company is not alone in adopting artificial intelligence to address difficult weather forecasting problems. The US and European governments are developing their own artificial intelligence systems in the works – which have demonstrated improved skill over previous traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at 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 do so. A particular firm, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the national monitoring system.