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

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would intensify into a severe hurricane and start shifting towards the coast of Jamaica. Not a single expert had ever issued such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa reaching a Category 5 hurricane. Although I am unprepared to predict that strength yet due to track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the system moves slowly over very warm sea temperatures which is the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and currently the first to beat standard weather forecasters at their own game. Through all 13 Atlantic storms this season, Google’s model is the best – surpassing human forecasters 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 likely gave people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving lives and property.

The Way The System Works

Google’s model operates through identifying trends that conventional time-intensive scientific prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a former forecaster.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve relied upon,” he said.

Understanding AI Technology

To be sure, the system is an example of machine learning – a method that has been employed in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a manner that its system only requires minutes to generate an result, and can operate on a desktop computer – in sharp difference to the primary systems that governments have used for decades that can take hours to process and need some of the biggest supercomputers in the world.

Expert Reactions and Upcoming Developments

Still, the reality that the AI could exceed previous top-tier traditional systems so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest storms.

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

He said that although the AI is beating all competing systems on predicting the future path of hurricanes globally this year, like many AI models it sometimes errs on extreme strength predictions inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he intends to discuss with Google about how it can make the DeepMind output more useful for forecasters by providing additional internal information they can use to evaluate exactly why it is coming up with its answers.

“A key concern that nags at me is that while these predictions appear really, really good, the results of the system is kind of a black box,” remarked Franklin.

Broader Industry Trends

Historically, no a commercial entity that has developed a high-performance weather model which grants experts a view of its techniques – in contrast to most other models which are offered free to the general audience in their entirety by the governments that designed and maintain them.

The company is not alone in adopting AI to address challenging weather forecasting problems. The US and European governments are developing their own AI weather models in the development phase – which have demonstrated improved skill over previous traditional systems.

The next steps in AI weather forecasts seem to be new firms taking swings at previously tough-to-solve problems such as long-range forecasts and improved early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the US weather-observing network.