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When Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would become a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense hurricane. Although I am unprepared to forecast that intensity at this time due to track uncertainty, that is still plausible.

“There is a high probability that a period 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 Traditional Systems

Google DeepMind is the pioneer AI model focused on hurricanes, and now the first to beat traditional weather forecasters at their specialty. Through all 13 Atlantic storms this season, the AI is the best – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in nearly two centuries of data collection across the region. The confident prediction likely gave residents additional preparation time to prepare for the disaster, possibly saving lives and property.

The Way Google’s System Works

Google’s model operates through identifying trends that conventional lengthy physics-based prediction systems may miss.

“The AI performs far faster than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the recent AI weather models are on par with and, in some cases, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

It’s important to note, the system is an example of machine learning – a method that has been employed in data-heavy sciences like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a such a way that its system 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 authorities have used for years that can require many hours to process and need some of the biggest supercomputers in the world.

Professional Responses and Future Advances

Still, the fact that the AI could exceed earlier top-tier legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the world’s strongest weather systems.

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

He said that although the AI is outperforming all other models on predicting the trajectory of storms worldwide this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It had difficulty with another storm previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin said he plans to talk with Google about how it can enhance the AI results more useful for experts by providing extra under-the-hood data they can utilize to evaluate exactly why it is producing its conclusions.

“A key concern that nags at me is that while these forecasts appear really, really good, the output of the model is essentially a black box,” remarked Franklin.

Wider Industry Trends

Historically, no a commercial entity that has produced a top-level forecasting system which grants experts a peek into its methods – in contrast to most other models which are offered at no cost to the public in their entirety by the governments that designed and maintain them.

The company is not the only one in adopting artificial intelligence to solve difficult weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the development phase – which have also shown improved skill over earlier traditional systems.

The next steps in AI weather forecasts appear to involve startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.