🔗 Share this article

As Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

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

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Forecasters 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 key factor for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a most intense storm. Although I am unprepared to forecast that strength at this time given path variability, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the system moves slowly over exceptionally hot sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and now the first to beat standard meteorological experts at their specialty. Across all tropical systems so far this year, Google’s model is the best – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in almost 200 years of data collection across the region. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving people and assets.

How Google’s Model Functions

Google’s model works by spotting patterns that traditional lengthy scientific prediction systems may overlook.

“They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are on par with and, in certain instances, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Understanding 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 meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data 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 do so on a desktop computer – in strong contrast to the flagship models that governments have utilized for years that can take hours to run and need some of the biggest high-performance systems in the world.

Expert Responses and Future Developments

Still, the fact that the AI could exceed previous top-tier legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” said James Franklin, a former expert. “The sample is sufficient that it’s evident this is not a case of beginner’s luck.”

He noted that while the AI is outperforming all competing systems on predicting the trajectory of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he said he plans to discuss with the company about how it can enhance the AI results even more helpful for forecasters by providing extra internal information they can utilize to assess exactly why it is producing its answers.

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

Broader Industry Trends

There has never been a private, for-profit company that has produced a top-level forecasting system which grants experts a view of its techniques – unlike nearly all systems which are offered at no cost to the general audience in their full form by the governments that designed and maintain them.

The company is not the only one in adopting artificial intelligence to address challenging weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have also shown better performance over previous traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.