How Google’s DeepMind System is Transforming Tropical Cyclone Forecasting with Rapid Pace

As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa reaching a most intense storm. While I am not ready to predict that intensity at this time due to track uncertainty, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the storm moves slowly over very warm ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and currently the first to outperform standard weather forecasters at their specialty. Across all tropical systems so far this year, the AI is top-performing – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful landfalls recorded in almost 200 years of record-keeping across the region. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the disaster, potentially preserving lives and property.

How Google’s Model Functions

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

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in short order is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid traditional weather models we’ve relied upon,” he said.

Understanding Machine Learning

To be sure, the system is an example of machine learning – a technique that has been employed in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a manner that its model 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 years that can require many hours to process and require the largest supercomputers in the world.

Professional Responses and Future Advances

Still, the fact that the AI could exceed previous gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest storms.

“I’m impressed,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s evident this is not a case of chance.”

Franklin said that although Google DeepMind is outperforming all competing systems on forecasting the trajectory of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, he said he intends to discuss with the company about how it can make the AI results even more helpful for experts by offering additional internal information they can use to assess the reasons it is producing its conclusions.

“The one thing that troubles me is that although these predictions appear really, really good, the results of the model is essentially a opaque process,” said Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has developed a top-level forecasting system which grants experts a view of its techniques – unlike nearly all other models which are provided at no cost to the general audience in their entirety by the authorities that created and operate them.

The company is not alone in starting to use AI to solve difficult weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the development phase – which have also shown improved skill over earlier non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies tackling previously tough-to-solve problems such as long-range forecasts and improved advance warnings of severe weather and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the national monitoring system.