🔗 Share this article

As Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system.

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would become a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made 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 – launched for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a most intense storm. Although I am not ready to predict that intensity yet given track uncertainty, that remains a possibility.

“There is a high probability that a phase of rapid intensification is expected as the storm drifts over very warm sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to hurricanes, and now the initial to beat standard weather forecasters at their own game. Through all 13 Atlantic storms this season, the AI is the best – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction probably provided residents extra time to get ready for the disaster, possibly saving lives and property.

How Google’s System Functions

The AI system works by spotting patterns that conventional time-intensive physics-based weather models may miss.

“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a former forecaster.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in certain instances, superior than the slower physics-based forecasting tools we’ve relied upon,” Lowry said.

Clarifying AI Technology

To be sure, Google DeepMind is an example of machine learning – a technique that has been employed in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a such a way that its system only takes a few minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the primary systems 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

Nevertheless, the reality that the AI could exceed earlier gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a former expert. “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 worldwide this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin said he intends to discuss with the company about how it can enhance the AI results even more helpful for experts by offering additional under-the-hood data they can utilize to evaluate exactly why it is producing its conclusions.

“The one thing that troubles me is that although these predictions seem to be really, really good, the results of the system is kind of a opaque process,” said Franklin.

Broader Industry Developments

Historically, no a commercial entity that has developed 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 entirety by the governments that created and operate them.

The company is not alone in adopting artificial intelligence to solve difficult weather forecasting problems. The US and European governments also have their own AI weather models in the works – which have also shown improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.