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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and start shifting towards the coast of Jamaica. Not a single expert had previously made this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Forecasters are heavily relying upon 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 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. Although I am not ready to predict that strength at this time due to path variability, that is still plausible.

“It appears likely that a period of quick strengthening will occur as the system moves slowly over very warm sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer AI model focused on tropical cyclones, and now the first to outperform traditional meteorological experts at their own game. Across all 13 Atlantic storms this season, the AI is the best – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to prepare for the catastrophe, potentially preserving lives and property.

The Way The System Functions

Google’s model works by spotting patterns that conventional lengthy scientific weather models may miss.

“They do it far faster than their physics-based cousins, and the computing power is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“This season’s events has proven in short order is that the newcomer AI weather models are competitive with and, in certain instances, superior than the slower physics-based forecasting tools we’ve traditionally leaned on,” he added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for years – and is not 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 come up with an result, and can operate on a desktop computer – in strong contrast to the primary systems that authorities have used for decades that can require many hours to process and require the largest supercomputers in the world.

Professional Responses and Future Advances

Nevertheless, the fact that the AI could outperform earlier top-tier legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest weather systems.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

He said that although the AI is outperforming all competing systems on predicting the trajectory of storms globally this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

During the next break, he stated he plans to discuss with the company about how it can make the AI results more useful for forecasters by offering additional internal information they can utilize to assess the reasons it is producing its answers.

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

Broader Industry Developments

There has never been a commercial entity that has produced a high-performance forecasting system which allows researchers a peek into its methods – unlike nearly all other models which are offered at no cost to the general audience in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use artificial intelligence to address challenging meteorological problems. The US and European governments are developing their respective AI weather models in the development phase – which have also shown better performance over earlier traditional systems.

Future developments in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.