🔗 Share this article

As Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa becoming a Category 5 storm. While I am unprepared to predict that intensity at this time given track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the system moves slowly over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and now the first to beat standard weather forecasters at their own game. Through all 13 Atlantic storms this season, the AI is the best – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to prepare for the disaster, potentially preserving people and assets.

The Way Google’s System Works

The AI system operates through identifying trends that conventional time-intensive scientific weather models may miss.

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

“What this hurricane season has demonstrated in quick time is that the recent AI weather models are competitive with and, in some cases, more accurate than the less rapid traditional weather models we’ve relied upon,” he added.

Understanding Machine Learning

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

AI training processes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to generate an result, and can do so on a desktop computer – in sharp difference to the flagship models that authorities have used for decades that can take hours to run and need the largest supercomputers in the world.

Professional Responses and Upcoming Developments

Still, the fact that the AI could exceed previous top-tier legacy models so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense storms.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is sufficient that it’s evident this is not just chance.”

Franklin said that while the AI is outperforming all competing systems on predicting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he plans to talk with Google about how it can make the AI results more useful for experts by providing extra internal information they can utilize to assess the reasons it is coming up with its answers.

“The one thing that troubles me is that while these forecasts appear really, really good, the output of the model is kind of a opaque process,” remarked Franklin.

Broader Industry Developments

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a view of its methods – in contrast to most other models which are offered free to the general audience in their entirety by the authorities that designed and maintain them.

Google is not alone in starting to use AI to solve challenging weather forecasting problems. The authorities are developing their own artificial intelligence systems in the works – which have also shown improved skill over previous traditional systems.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is even deploying its own weather balloons to fill the gaps in the US weather-observing network.