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As Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made this confident prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI simulation runs show Melissa reaching a Category 5 storm. While I am not ready to predict that strength at this time due to track uncertainty, that remains a possibility.

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

Surpassing Conventional Systems

The AI model is the first AI model dedicated to hurricanes, and now the initial to outperform traditional weather forecasters at their own game. Across all 13 Atlantic storms so far this year, the AI is top-performing – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction likely gave residents additional preparation time to prepare for the disaster, potentially preserving lives and property.

The Way Google’s System Works

Google’s model operates through identifying trends that conventional lengthy physics-based weather models may overlook.

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

“This season’s events has proven in quick time is that the recent AI weather models are competitive with and, in certain instances, more accurate than the less rapid traditional weather models we’ve relied upon,” Lowry added.

Understanding Machine Learning

To be sure, Google DeepMind is an example of AI training – a method that has been used in research fields like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the primary systems that authorities have utilized for decades that can take hours to process and need some of the biggest supercomputers in the world.

Expert Reactions and Upcoming Developments

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

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

He said that while Google DeepMind is beating all other models on predicting the trajectory of storms globally this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, he said he intends to talk with the company 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.

“The one thing that nags at me is that while these forecasts seem to be really, really good, the output of the system is kind of a opaque process,” remarked Franklin.

Wider Sector Developments

Historically, no a commercial entity that has developed a top-level forecasting system which allows researchers a peek into its methods – in contrast to nearly all systems which are offered free to the public in their full form by the governments that created and operate them.

The company is not alone in adopting AI to address challenging weather forecasting problems. The US and European governments also have their respective AI weather models in the development phase – which have demonstrated better performance over previous traditional systems.

The next steps in AI weather forecasts seem to be startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.