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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the weather system would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had ever issued this confident prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 hurricane. Although I am unprepared to predict that strength yet given track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification is expected as the system drifts over very warm sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer AI model focused on hurricanes, and currently the initial to outperform standard weather forecasters at their own game. Through all 13 Atlantic storms this season, the AI is the best – even beating human forecasters on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls recorded in almost 200 years of data collection across the region. The confident prediction probably provided residents additional preparation time to prepare for the disaster, potentially preserving people and assets.

How Google’s Model Functions

The AI system works by identifying trends that conventional time-intensive physics-based prediction systems may overlook.

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

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

To be sure, the system is an instance of AI training – a method that has been used in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to generate an result, and can operate on a desktop computer – in sharp difference to the primary systems that authorities have utilized for years that can take hours to process and require the largest supercomputers in the world.

Professional Reactions and Upcoming Developments

Still, the reality that the AI could outperform earlier top-tier traditional systems 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 data is now large enough that it’s evident this is not just beginner’s luck.”

He said that while Google DeepMind is outperforming all other models on predicting the trajectory of storms worldwide this year, like many AI models it occasionally gets extreme strength predictions wrong. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, he said he plans to talk with the company about how it can enhance the DeepMind output even more helpful for experts by offering additional internal information they can utilize to assess exactly why it is coming up with its answers.

“The one thing that nags at me is that while these predictions seem to be highly accurate, the results of the system is kind of a opaque process,” remarked Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has produced a top-level forecasting system which grants experts a peek into its methods – in contrast to most systems which are offered at no cost to the public in their entirety by the authorities that designed and maintain them.

The company is not the only one in adopting AI to solve difficult weather forecasting problems. The US and European governments also have their respective AI weather models in the works – which have demonstrated improved skill over earlier traditional systems.

The next steps in AI weather forecasts seem to be startup companies taking swings at previously difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.