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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and start shifting towards the Jamaican shoreline. No forecaster had ever issued this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise 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 remarkable power that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa reaching a Category 5 storm. Although I am unprepared to forecast that intensity yet due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the storm moves slowly over very warm ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the first AI model focused on tropical cyclones, and currently the initial to beat standard weather forecasters at their own game. Across all tropical systems this season, the AI is top-performing – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest landfalls recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to get ready for the catastrophe, possibly saving lives and property.

The Way Google’s System Functions

The AI system works by identifying trends that conventional lengthy physics-based prediction systems may miss.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve relied upon,” he added.

Understanding Machine Learning

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

Machine learning processes large datasets and pulls out patterns from them in a manner that its system only requires minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the flagship models that authorities have used for decades that can require many hours to process and need some of the biggest supercomputers in the world.

Professional Reactions and Future Developments

Nevertheless, the reality that Google’s model could outperform earlier top-tier traditional systems so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a former forecaster. “The sample is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that while Google DeepMind is outperforming all other models on forecasting the future path of hurricanes globally this year, similar to other systems it occasionally gets extreme strength predictions wrong. It struggled with another storm previously, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, Franklin said he intends to talk with Google about how it can make the AI results more useful for experts by offering additional under-the-hood data they can utilize to evaluate the reasons it is coming up with its answers.

“A key concern that nags at me is that although these forecasts appear really, really good, the output of the model is essentially a opaque process,” remarked Franklin.

Wider Sector Trends

There has never been a commercial entity that has developed a top-level forecasting system which grants experts a peek into its methods – unlike nearly all systems which are offered at no cost to the public in their entirety by the authorities that designed and maintain them.

Google is not alone in adopting artificial intelligence to address challenging meteorological problems. The authorities also have their respective AI weather models in the development phase – which have demonstrated improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be new firms taking swings at previously tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is also deploying its proprietary weather balloons to fill the gaps in the national monitoring system.