🔗 Share this article

When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the guise 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 astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa becoming a most intense hurricane. Although I am not ready to forecast that intensity at this time given path variability, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the system drifts over exceptionally hot ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Models

The AI model is the first AI model focused on hurricanes, and currently the initial to outperform standard weather forecasters at their own game. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls ever documented in nearly two centuries of data collection across the region. The confident prediction likely gave residents additional preparation time to get ready for the disaster, potentially preserving people and assets.

The Way Google’s System Works

Google’s model operates through identifying trends that conventional time-intensive scientific weather models may overlook.

“The AI performs far faster than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the newcomer AI weather models are on par with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

To be sure, the system 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.

Machine learning processes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to come up with an result, and can operate on a standard PC – in strong contrast to the flagship models that authorities have utilized for decades that can require many hours to process and require the largest high-performance systems in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that the AI could outperform earlier 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 former expert. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

Franklin noted that while Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions wrong. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to category 5 above the Caribbean.

During the next break, he said he plans to discuss with Google about how it can make the DeepMind output even more helpful for experts by providing extra under-the-hood data they can use to assess exactly why it is producing its conclusions.

“The one thing that nags at me is that although these predictions appear really, really good, the results of the system is kind of a opaque process,” remarked Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has developed a high-performance forecasting system which grants experts a peek into its techniques – unlike nearly all other models which are provided at no cost to the general audience in their entirety by the governments that created and operate them.

The company is not the only one in adopting artificial intelligence to solve difficult weather forecasting problems. The authorities also have their own artificial intelligence systems in the works – which have also shown improved skill over earlier traditional systems.

Future developments in AI weather forecasts seem to be new firms tackling previously difficult problems such as long-range forecasts and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even deploying its own weather balloons to fill the gaps in the national monitoring system.