🔗 Share this article

When Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and begin a turn towards the Jamaican shoreline. No forecaster had ever issued such a bold prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind hurricane model – released 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 Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his confidence: “Approximately 40/50 AI ensemble members show Melissa becoming a most intense storm. Although I am not ready to forecast that strength yet due to track uncertainty, that is still plausible.

“There is a high probability that a period of quick strengthening will occur as the storm drifts over exceptionally hot ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer AI model focused on tropical cyclones, and now the initial to beat standard weather forecasters at their specialty. Through all tropical systems so far this year, the AI is the best – even beating experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the region. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the disaster, potentially preserving people and assets.

The Way The System Works

The AI system operates through spotting patterns that traditional time-intensive physics-based weather models may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid traditional forecasting tools we’ve relied upon,” Lowry added.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a method that has been used in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a such a way that its model only requires minutes to generate an answer, and can operate on a standard PC – in sharp difference to the primary systems that authorities have utilized for decades that can take hours to run and need the largest high-performance systems in the world.

Professional Responses and Future Developments

Still, the fact that Google’s model could outperform earlier top-tier legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense storms.

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

He noted that while Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It struggled with another storm 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 Google about how it can make the AI results more useful for forecasters by providing additional internal information they can utilize to assess exactly why it is coming up with its answers.

“A key concern that troubles me is that while these predictions seem to be really, really good, the output of the system is essentially a black box,” said Franklin.

Broader Sector Trends

Historically, no a private, for-profit company that has produced a high-performance forecasting system which allows researchers a view of its techniques – unlike most systems which are offered at no cost 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 challenging weather forecasting problems. The US and European governments also have their own AI weather models in the works – which have demonstrated better performance over earlier traditional systems.

The next steps in AI weather forecasts seem to be startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the national monitoring system.