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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would become a severe hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa becoming a most intense storm. While I am unprepared to predict that strength at this time due to path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the system moves slowly over exceptionally hot ocean waters which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Models

The AI model is the first artificial intelligence system dedicated to hurricanes, and currently the first to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is the best – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls ever documented in nearly two centuries of data collection across the region. The confident prediction probably provided residents extra time to prepare for the disaster, potentially preserving people and assets.

How The System Functions

The AI system operates through identifying trends that traditional time-intensive scientific prediction systems may miss.

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

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are on par with and, in some cases, more accurate than the less rapid physics-based weather models we’ve relied upon,” Lowry added.

Understanding AI Technology

To be sure, Google DeepMind is an instance 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.

Machine learning processes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to generate an answer, and can operate on a desktop computer – in sharp difference to the primary systems that authorities have used for decades that can take hours to run and require the largest high-performance systems in the world.

Expert Reactions and Future Developments

Still, the reality that Google’s model could exceed earlier top-tier traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense storms.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is sufficient that it’s evident this is not a case of chance.”

He said that while Google DeepMind is beating all competing systems on predicting the future path of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he stated he plans to discuss with the company about how it can make the DeepMind output even more helpful for experts by providing extra internal information they can utilize to evaluate the reasons it is producing its conclusions.

“A key concern that nags at me is that while these forecasts appear highly accurate, the results of the system is essentially a black box,” said Franklin.

Wider Sector Developments

There has never been a commercial entity that has produced a high-performance forecasting system which grants experts a peek into its methods – in contrast to nearly all systems which are offered free to the general audience in their entirety by the governments that created and operate them.

The company is not the only one in starting to use artificial intelligence to solve difficult meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have demonstrated improved skill over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve new firms tackling previously difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the national monitoring system.