🔗 Share this article

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

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had ever issued this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence 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 did become a system of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members show Melissa reaching a most intense hurricane. While I am unprepared to forecast that intensity yet given path variability, that remains a possibility.

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

Surpassing Conventional Systems

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and now the initial to outperform traditional meteorological experts at their own game. Through all tropical systems so far this year, Google’s model is top-performing – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave people in Jamaica extra time to prepare for the catastrophe, potentially preserving lives and property.

The Way The System Functions

Google’s model operates through identifying trends that conventional lengthy scientific weather models may miss.

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are competitive with and, in some cases, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been employed in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a such a way that its model only takes a few minutes to come up with an result, and can operate on a desktop computer – in sharp difference to the flagship models that governments have utilized for years that can require many hours to process and need the largest supercomputers in the world.

Expert Responses and Future Developments

Still, the fact that Google’s model could exceed previous top-tier traditional systems so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense storms.

“It’s astonishing,” said James Franklin, a former expert. “The data is sufficient that it’s evident this is not a case of chance.”

Franklin noted that while the AI is beating all other models on predicting the trajectory of hurricanes globally this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he intends to discuss with Google about how it can make the AI results more useful for experts by offering additional under-the-hood data they can use to assess exactly why it is coming up with its conclusions.

“The one thing that nags at me is that while these predictions seem to be highly accurate, the results of the system is kind of a black box,” remarked Franklin.

Wider Sector Developments

There has never been a commercial entity that has developed a high-performance 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 full form by the authorities that created and operate them.

Google is not alone in starting to use artificial intelligence to address difficult meteorological problems. The US and European governments also have their own AI weather models in the development phase – which have also shown better performance over previous traditional systems.

The next steps in artificial intelligence predictions seem to be new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.