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As Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he predicted that in a single day the storm would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. Although I am not ready to predict that strength yet due to track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the storm moves slowly over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer AI model dedicated to hurricanes, and now the initial to beat standard weather forecasters at their own game. Across all 13 Atlantic storms this season, Google’s model is the best – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the catastrophe, possibly saving people and assets.

How The System Functions

Google’s model works by identifying trends that traditional time-intensive physics-based prediction systems may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a ex forecaster.

“This season’s events has proven in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

To be sure, Google DeepMind is an instance of machine learning – a technique that has been used in data-heavy sciences 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 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 utilized for years that can require many hours to process and need some of the biggest supercomputers in the world.

Expert Reactions and Future Advances

Nevertheless, the fact that Google’s model could outperform previous gold-standard traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest storms.

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

Franklin noted that while Google DeepMind is beating all competing systems on predicting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It had difficulty with another storm previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, he stated he plans to talk with Google about how it can enhance the AI results even more helpful for experts by providing additional under-the-hood data they can utilize to assess exactly why it is coming up with its conclusions.

“A key concern that troubles me is that although these predictions seem to be highly accurate, the results of the system is essentially a opaque process,” remarked Franklin.

Broader Sector Trends

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

The company is not the only one in adopting artificial intelligence to address challenging weather forecasting problems. The authorities are developing their own artificial intelligence systems in the works – which have demonstrated better performance over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies tackling formerly difficult problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the US weather-observing network.