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When Tropical Storm Melissa was churning south of Haiti, weather expert 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 storm would become a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 AI simulation runs show Melissa becoming a Category 5 storm. While I am not ready to predict that intensity at this time given path variability, that is still plausible.

“It appears likely that a period of quick strengthening will occur as the storm moves slowly over very warm ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the first AI model focused on hurricanes, and now the initial to beat traditional weather forecasters at their specialty. Across all tropical systems this season, Google’s model is the best – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful landfalls ever documented in almost 200 years of data collection across the region. The confident prediction likely gave people in Jamaica extra time to prepare for the disaster, possibly saving lives and property.

How The Model Functions

Google’s model operates through spotting patterns that traditional lengthy physics-based prediction systems may miss.

“They do it far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are on par with and, in some cases, superior than the slower traditional weather models we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a technique that has been employed in research fields like meteorology 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 system only requires minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the flagship models that governments have used for years that can require many hours to process and need some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that Google’s model could exceed earlier top-tier legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense storms.

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

He said that although the AI is outperforming all other models on forecasting the future path of storms globally this year, like many AI models it occasionally gets high-end intensity predictions wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, he said he plans to talk with Google about how it can make the AI results more useful for experts by offering additional internal information they can utilize to evaluate exactly why it is coming up with its answers.

“A key concern that troubles me is that although these predictions appear highly accurate, the output of the system is essentially a opaque process,” remarked Franklin.

Wider Sector Trends

Historically, no a private, for-profit company that has developed a high-performance weather model which allows researchers a view of its techniques – in contrast to most systems which are provided free to the public in their full form by the authorities that created and operate them.

Google is not the only one in adopting artificial intelligence to solve challenging meteorological problems. The US and European governments also have their own artificial intelligence systems in the works – which have demonstrated improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling formerly difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the US weather-observing network.