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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the weather system would become a category 4 hurricane and begin a turn towards the coast of Jamaica. No forecaster had ever issued this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – 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 AI Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa becoming a Category 5 storm. While I am not ready to predict that strength at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a phase of rapid intensification will occur as the system moves slowly over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the first AI model dedicated to hurricanes, and currently the initial to outperform traditional weather forecasters at their own game. Through all tropical systems this season, the AI is top-performing – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the region. The confident prediction likely gave people in Jamaica extra time to prepare for the disaster, potentially preserving people and assets.

The Way Google’s Model Works

Google’s model works by identifying trends that traditional lengthy physics-based weather models may miss.

“They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in some cases, 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 example of AI training – a method that has been employed in research fields like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a such a way that its model only requires minutes to generate an answer, and can operate on a standard PC – in sharp difference to the primary systems that governments have utilized for years that can take hours to run and require some of the biggest high-performance systems in the world.

Expert Reactions and Upcoming Developments

Still, the reality 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 predict the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not a case of beginner’s luck.”

Franklin noted that while Google DeepMind is beating all other models on forecasting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin said he intends to discuss with Google about how it can enhance the DeepMind output even more helpful for experts by offering additional under-the-hood data they can utilize to assess the reasons it is producing its conclusions.

“The one thing that troubles me is that while these predictions seem to be really, really good, the results of the model is essentially a black box,” said Franklin.

Wider Industry Developments

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its techniques – unlike nearly all other models which are provided at no cost to the general audience in their entirety by the authorities that created and operate them.

The company is not the only one in starting to use AI to address difficult meteorological problems. The authorities are developing their own AI weather models in the works – which have demonstrated improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.