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

As Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued this confident prediction for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Growing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 storm. Although I am unprepared to predict that strength yet due to track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the storm moves slowly over very warm ocean waters which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and currently the first to beat standard meteorological experts at their own game. Through all tropical systems this season, the AI is the best – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the disaster, potentially preserving people and assets.

The Way The Model Functions

Google’s model operates through identifying trends that traditional lengthy physics-based weather models may overlook.

“They do it much more quickly than their physics-based cousins, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in quick time 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 relied upon,” Lowry said.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a method that has been used in data-heavy sciences like weather science for years – and is distinct from 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 generate an result, and can operate on a desktop computer – in sharp difference to the primary systems that authorities have used for years that can require many hours to run and need the largest supercomputers in the world.

Expert Reactions and Upcoming Advances

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

“I’m impressed,” commented James Franklin, a retired expert. “The data is sufficient that it’s evident this is not a case of chance.”

He said that although the AI is beating all other models on predicting the future path of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

In the coming offseason, Franklin said he plans to talk with the company about how it can enhance the AI results more useful for forecasters by offering additional under-the-hood data they can utilize to assess the reasons it is producing its answers.

“The one thing that troubles me is that while these forecasts seem to be really, really good, the results of the model is essentially a black box,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a view of its methods – unlike 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.

Google is not alone in starting to use AI to address difficult weather forecasting problems. The authorities also have their respective artificial intelligence systems in the development phase – which have demonstrated better performance over previous traditional systems.

The next steps in AI weather forecasts appear to involve new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.