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As Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

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

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa becoming a Category 5 hurricane. Although I am not ready to forecast that strength yet given path variability, that remains a possibility.

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

Outperforming Conventional Models

The AI model is the pioneer AI model focused on hurricanes, and now the initial to beat standard meteorological experts at their specialty. Through all tropical systems so far this year, the AI is the best – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest landfalls recorded in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to prepare for the catastrophe, possibly saving lives and property.

How Google’s Model Works

Google’s model operates through spotting patterns that conventional time-intensive scientific weather models may miss.

“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the slower traditional weather models we’ve relied upon,” he added.

Understanding AI Technology

To be sure, the system is an instance of AI training – a method that has been employed in research fields like weather science for a long time – and is not generative AI like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a such a way that its system only requires minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the flagship models that governments have utilized for decades that can take hours to process and need some of the biggest supercomputers in the world.

Professional Responses and Upcoming Developments

Still, the fact that Google’s model could outperform previous top-tier legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense storms.

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

He noted that while Google DeepMind is beating all other models on forecasting the future path of hurricanes globally this year, like many AI models it sometimes errs on extreme strength predictions wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, Franklin said he intends to discuss with Google about how it can make the DeepMind output more useful for forecasters by offering extra internal information they can use to assess exactly why it is coming up with its conclusions.

“A key concern that troubles me is that although these predictions seem to be highly accurate, the results of the model is kind of a opaque process,” remarked Franklin.

Wider Industry Trends

There has never been a commercial entity that has produced a top-level weather model which allows researchers a view of its techniques – in contrast to most systems which are offered free to the general audience in their entirety by the governments that created and operate them.

Google is not the only one in starting to use AI to address difficult meteorological problems. The US and European governments also have their respective AI weather models in the works – which have also shown better performance over previous non-AI versions.

The next steps in AI weather forecasts seem to be new firms tackling previously difficult problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and sudden deluges – and they have secured federal support to do so. A particular firm, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the national monitoring system.