🔗 Share this article

When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in a single day the storm would become a severe hurricane and begin a turn towards the coast of Jamaica. No forecaster had ever issued such a bold forecast for quick intensification.

However, Papin possessed a secret advantage: AI technology 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 evolved into a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon 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 AI simulation runs indicate Melissa reaching a most intense storm. Although I am unprepared to forecast that intensity yet 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 sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer AI model focused on hurricanes, and now the initial to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is the best – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to get ready for the disaster, potentially preserving lives and property.

The Way Google’s Model Works

The AI system works by identifying trends that traditional time-intensive scientific prediction systems may miss.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the slower physics-based forecasting tools we’ve traditionally leaned on,” he said.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a method that has been used in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to generate an result, and can operate on a desktop computer – in strong contrast to the primary systems that governments have used for years that can require many hours to run and require the largest supercomputers in the world.

Professional Reactions and Upcoming Advances

Nevertheless, the fact that Google’s model could outperform previous gold-standard legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

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

He said that while Google DeepMind is outperforming all other models on forecasting the trajectory of storms worldwide this year, like many AI models it occasionally gets extreme strength predictions wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, he said he plans to discuss with Google about how it can enhance the DeepMind output even more helpful for forecasters by providing extra internal information they can utilize to assess the reasons it is coming up with its conclusions.

“A key concern that troubles me is that while these forecasts appear really, really good, the results of the system is kind of a opaque process,” remarked Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has developed a high-performance forecasting system which allows researchers a view of its techniques – in contrast to most systems which are provided free to the general audience in their entirety by the authorities that designed and maintain them.

The company is not alone in starting to use artificial intelligence to address challenging meteorological problems. The US and European governments also have their respective AI weather models in the development phase – which have demonstrated better performance over previous non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even deploying its own weather balloons to fill the gaps in the national monitoring system.