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When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had ever issued such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his confidence: “Roughly 40/50 AI ensemble members show Melissa reaching a Category 5 hurricane. Although I am unprepared to predict that intensity yet given track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the storm drifts over exceptionally hot ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first AI model focused on tropical cyclones, and currently the first to outperform standard weather forecasters at their own game. Through all 13 Atlantic storms so far this year, Google’s model is the best – even beating experts on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful landfalls recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction likely gave residents additional preparation time to get ready for the disaster, possibly saving lives and property.

The Way Google’s System Functions

Google’s model works by spotting patterns that traditional lengthy physics-based weather models may miss.

“They do it far faster than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former forecaster.

“This season’s events has proven in quick time is that the newcomer artificial intelligence systems are on par with and, in some cases, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry added.

Understanding AI Technology

To be sure, Google DeepMind is an example of AI training – a technique that has been used in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its system only takes a few minutes to generate an result, and can operate on a standard PC – in sharp difference to the primary systems that authorities have utilized for years that can require many hours to run and require some of the biggest supercomputers in the world.

Expert Responses and Upcoming Advances

Nevertheless, the fact that the AI could exceed earlier gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not just beginner’s luck.”

Franklin noted that although Google DeepMind is beating all competing systems on predicting the future path of storms globally this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, Franklin stated he intends to talk with the company about how it can make the DeepMind output even more helpful for forecasters by offering extra internal information they can utilize to assess the reasons it is producing its answers.

“The one thing that troubles me is that although these forecasts appear really, really good, the results of the model is essentially a opaque process,” said Franklin.

Wider Sector Trends

Historically, no a private, for-profit company that has developed a high-performance weather model which grants experts a view of its techniques – in contrast to most systems which are provided at no cost to the general audience in their full form by the governments that created and operate them.

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

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 early alerts of severe weather and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the US weather-observing network.