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

As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made this confident prediction for quick intensification.

But, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Growing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified 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 Category 5 storm. While I am unprepared to predict that strength at this time due to path variability, that is still plausible.

“There is a high probability that a phase of quick strengthening will occur as the system moves slowly over very warm sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and now the first to beat traditional weather forecasters at their own game. Across all 13 Atlantic storms this season, Google’s model is top-performing – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful landfalls recorded in almost 200 years of data collection across the region. The confident prediction likely gave residents additional preparation time to prepare for the disaster, possibly saving lives and property.

How Google’s Model Works

The AI system operates through identifying trends that conventional time-intensive scientific weather models may overlook.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” stated Michael Lowry, a former meteorologist.

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

Understanding Machine Learning

It’s important to note, the system is an instance of machine learning – a technique 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 mounds of data and extracts trends from them in a such a way that its system only takes a few minutes to generate an answer, and can do so on a standard PC – in strong contrast to the flagship models that authorities have utilized for decades that can require many hours to run and need some of the biggest supercomputers in the world.

Expert Responses and Future Advances

Still, the reality that the AI could outperform previous gold-standard traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

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

Franklin noted that while the AI is outperforming all other models on predicting the trajectory of hurricanes 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 also undergoing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin said he plans to talk with Google about how it can enhance the AI results more useful for experts by providing additional under-the-hood data they can utilize to evaluate the reasons it is coming up with its answers.

“The one thing that nags at me is that while these predictions appear highly accurate, the results of the model is kind of a opaque process,” said Franklin.

Wider Industry Trends

There has never been a private, for-profit company that has developed a top-level weather model which grants experts a view of its methods – unlike nearly all other models which are provided free to the public in their entirety by the governments that designed and maintain them.

Google is not alone in adopting artificial intelligence to solve difficult meteorological problems. The US and European governments are developing their own AI weather models in the works – which have also shown improved skill over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.