The Way Google’s DeepMind Tool is Revolutionizing Tropical Cyclone Forecasting with Rapid Pace

As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a major tropical system.

As the primary meteorologist on duty, he forecasted that in a single day the storm would become a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made such a bold prediction for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his confidence: “Approximately 40/50 AI simulation runs show Melissa becoming a Category 5 storm. While I am unprepared to predict that intensity at this time due to track uncertainty, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the storm drifts over exceptionally hot sea temperatures which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first artificial intelligence system focused on tropical cyclones, and currently the first to beat standard weather forecasters at their own game. Across all 13 Atlantic storms this season, the AI is top-performing – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum strength, one of the strongest landfalls recorded in nearly two centuries of data collection across the region. Papin’s bold forecast probably provided residents extra time to prepare for the catastrophe, possibly saving people and assets.

How Google’s System Works

The AI system works by spotting patterns that traditional lengthy physics-based prediction systems may overlook.

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and demanding,” said Michael Lowry, a ex meteorologist.

“This season’s events has proven in quick time is that the newcomer artificial intelligence systems are on par with and, in certain instances, superior than the slower physics-based weather models we’ve relied upon,” he added.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a technique 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 manner that its model only takes a few minutes to come up with an result, and can do so on a desktop computer – in sharp difference to the primary systems that governments have used for decades that can require many hours to process and need the largest high-performance systems in the world.

Expert Reactions and Future Developments

Nevertheless, the reality that the AI could exceed previous gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest storms.

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

He noted that although the AI is beating all other models on forecasting the trajectory of storms worldwide this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, Franklin said he plans to discuss with Google about how it can make the AI results even more helpful for experts by providing additional under-the-hood data they can use to evaluate exactly why it is coming up with its answers.

“A key concern that troubles me is that although these forecasts appear highly accurate, the output of the model is essentially a black box,” remarked Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has developed a top-level weather model which grants experts a peek into its techniques – in contrast to nearly all systems which are provided free to the public in their entirety by the authorities that designed and maintain them.

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

Future developments in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the US weather-observing network.