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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Forecasters 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: “Roughly 40/50 AI ensemble members indicate Melissa reaching a Category 5 storm. While I am not ready to predict that strength at this time given track uncertainty, that remains a possibility.

“There is a high probability that a phase of rapid intensification is expected as the system moves slowly over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first AI model dedicated to tropical cyclones, and currently the first to beat standard meteorological experts at their own game. Across all 13 Atlantic storms this season, Google’s model is the best – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls recorded in almost 200 years of record-keeping across the Atlantic basin. The confident prediction probably provided residents additional preparation time to get ready for the disaster, potentially preserving lives and property.

The Way The System Works

Google’s model works by identifying trends that traditional lengthy physics-based weather models may miss.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve relied upon,” he said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been used in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to generate an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have utilized for years that can require many hours to process and need some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Developments

Still, the reality that Google’s model could outperform earlier top-tier traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest weather systems.

“It’s astonishing,” commented James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not a case of beginner’s luck.”

Franklin said that while the AI is outperforming all other models on forecasting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, Franklin stated he intends to talk with Google about how it can make the DeepMind output even more helpful for experts by providing additional internal information they can use to assess exactly why it is coming up with its conclusions.

“A key concern that nags at me is that although these forecasts appear really, really good, the results of the system is essentially a black box,” remarked Franklin.

Broader Sector Developments

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 most other models which are provided free to the public in their entirety by the governments that created and operate them.

Google is not alone in starting to use artificial intelligence to solve difficult meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have demonstrated improved skill over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms tackling previously difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the US weather-observing network.