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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as primary meteorologist on duty, he predicted that in a single day the weather system would become a severe hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had previously made this confident prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members show Melissa becoming a Category 5 hurricane. While I am not ready to predict that strength yet given track uncertainty, that is still plausible.

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

Outperforming Traditional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to hurricanes, and now the first to beat traditional weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, the AI is top-performing – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum strength, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction probably provided residents additional preparation time to prepare for the catastrophe, possibly saving lives and property.

How Google’s System Works

The AI system operates through identifying trends that traditional time-intensive scientific prediction systems may overlook.

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

“This season’s events has demonstrated in quick time is that the recent AI weather models are on par with and, in some cases, more accurate than the slower traditional weather models we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

It’s important to note, the system is an example of AI training – a method that has been used in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to come up with an answer, and can do so on a standard PC – in sharp difference to the primary systems that authorities have used for decades that can require many hours to run and need some of the biggest high-performance systems in the world.

Professional Responses and Future Advances

Nevertheless, the reality that the AI could exceed previous gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin noted that while the AI is outperforming all competing systems on forecasting the future path of storms worldwide this year, like many AI models it sometimes errs on extreme strength predictions inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

In the coming offseason, Franklin said he plans to discuss with Google about how it can make the DeepMind output even more helpful for experts by providing additional internal information they can use to evaluate exactly why it is producing its answers.

“A key concern that nags at me is that although these predictions seem to be really, really good, the output of the model is kind of a black box,” remarked Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has produced a high-performance forecasting system which allows researchers a peek into its techniques – unlike nearly all other models which are offered free to the public in their full form by the authorities that designed and maintain them.

The company is not the only one in starting to use AI to address challenging meteorological problems. The authorities also have their respective artificial intelligence systems in the development phase – which have demonstrated better performance over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve new firms taking swings at formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.