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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it would soon grow into a monster hurricane.

Serving as lead forecaster on duty, he predicted that in a single day the storm would become a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made such a bold forecast for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a most intense hurricane. While I am unprepared to forecast that strength yet given path variability, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the storm moves slowly over very warm ocean waters which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer AI model dedicated to tropical cyclones, and now the initial to beat standard weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is the best – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to prepare for the disaster, potentially preserving lives and property.

The Way The Model Functions

Google’s model operates through 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 more affordable and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in short order is that the recent artificial intelligence systems are on par with and, in some cases, superior than the less rapid traditional forecasting tools we’ve relied upon,” he said.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a method that has been employed in research fields like meteorology for years – and is distinct from generative AI like ChatGPT.

AI training processes large datasets and extracts trends from them in a manner that its model only takes a few minutes to generate an answer, and can do so on a desktop computer – in sharp difference to the flagship models that authorities have utilized for decades that can require many hours to process and need the largest supercomputers in the world.

Expert Reactions and Future Developments

Still, the fact that the AI could exceed previous gold-standard 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 former expert. “The data is sufficient that it’s evident this is not a case of beginner’s luck.”

He noted that while the AI is outperforming all other models on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions wrong. It struggled with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin said he intends to talk with Google about how it can make the DeepMind output more useful for forecasters by offering additional under-the-hood data they can utilize to evaluate exactly why it is coming up with its answers.

“A key concern that troubles me is that while these predictions seem to be highly accurate, the results of the model is kind of a black box,” said Franklin.

Wider Industry Developments

There has never been a private, for-profit company that has developed a top-level weather model which grants experts a peek into its techniques – unlike most systems which are provided at no cost to the public in their entirety by the governments that designed and maintain them.

The company is not the only one in adopting artificial intelligence to solve difficult weather forecasting problems. The authorities also have their own AI weather models in the development phase – which have also shown improved skill over previous traditional systems.

Future developments in AI weather forecasts appear to involve new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the national monitoring system.