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As Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made this confident forecast for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense storm. Although I am not ready to predict that strength at this time given path variability, that remains a possibility.

“There is a high probability that a phase of quick strengthening 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.”

Outperforming Conventional Systems

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and now the initial to beat traditional weather forecasters at their own game. Through all tropical systems so far this year, the AI is the best – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at maximum strength, one of the strongest landfalls ever documented in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to prepare for the disaster, possibly saving lives and property.

The Way Google’s System Works

The AI system operates through spotting patterns that traditional lengthy scientific weather models may miss.

“They do it far faster than their traditional counterparts, and the computing power is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

“This season’s events has demonstrated in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the less rapid physics-based weather models we’ve relied upon,” Lowry said.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of machine learning – a method that has been employed in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a such a way that its system only requires minutes to come up with an result, and can operate on a desktop computer – in sharp difference to the flagship models that authorities have used for decades that can take hours to run and need some of the biggest supercomputers in the world.

Professional Reactions and Future Developments

Still, the fact that Google’s model could exceed earlier gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former expert. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

Franklin noted that although the AI is outperforming all competing systems on forecasting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength predictions wrong. It struggled with another storm previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he intends to discuss with Google about how it can enhance the AI results more useful for forecasters by offering extra under-the-hood data they can use to assess exactly why it is producing its conclusions.

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

Wider Sector Developments

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its methods – in contrast to most 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 starting to use AI to address difficult meteorological problems. The US and European governments also have their own AI weather models in the development phase – which have demonstrated improved skill over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve new firms taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they have secured federal support to do so. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.