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When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had ever issued this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 hurricane. Although I am unprepared to forecast that strength at this time given path variability, that is still plausible.

“It appears likely that a period of quick strengthening will occur as the storm moves slowly over exceptionally hot sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer AI model dedicated to tropical cyclones, and currently the first to outperform standard weather forecasters at their specialty. Through all tropical systems so far this year, Google’s model is top-performing – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the region. The confident prediction likely gave people in Jamaica extra time to get ready for the catastrophe, potentially preserving lives and property.

The Way Google’s System Works

Google’s model operates through identifying trends that traditional lengthy scientific prediction systems may miss.

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are on par with and, in some cases, superior than the slower physics-based weather models we’ve relied upon,” Lowry added.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of AI training – a method that has been used in research fields like weather science for years – and is not generative AI like ChatGPT.

AI training takes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the flagship models that governments have used for years that can require many hours to process and require some of the biggest supercomputers in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the reality that Google’s model could exceed previous gold-standard traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” said James Franklin, a retired forecaster. “The data is sufficient that it’s pretty clear this is not a case of chance.”

Franklin said that while Google DeepMind is outperforming all other models on predicting the trajectory of storms globally this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It struggled with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, he stated he plans to discuss with Google about how it can enhance the DeepMind output even more helpful for forecasters by providing additional internal information they can utilize to assess exactly why it is coming up with its answers.

“The one thing that troubles me is that while these forecasts appear really, really good, the results of the system is essentially a black box,” remarked Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has produced a high-performance weather model which grants experts a view of its techniques – unlike nearly all systems which are provided free to the general audience in their full form by the authorities that created and operate them.

The company is not alone in adopting artificial intelligence to address difficult weather forecasting problems. The authorities are developing their own artificial intelligence systems in the development phase – which have also shown improved skill over previous traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies tackling formerly difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the national monitoring system.