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As Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made this confident prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the guise 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 ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa reaching a Category 5 hurricane. Although I am unprepared to forecast that strength yet given path variability, that is still plausible.

“It appears likely that a period of quick strengthening will occur as the system drifts over exceptionally hot sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and currently the initial to beat standard meteorological experts at their own game. Through all 13 Atlantic storms so far this year, the AI is the best – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the catastrophe, potentially preserving lives and property.

How The Model Functions

Google’s model works by spotting patterns that traditional time-intensive scientific prediction systems may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are competitive with and, in some cases, superior than the slower physics-based weather models we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a method that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the primary systems that authorities have used for decades that can take hours to process and need some of the biggest supercomputers in the world.

Professional Responses and Future Developments

Still, the fact that the AI could exceed earlier gold-standard traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the most intense storms.

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

He said that although the AI is outperforming all competing systems on forecasting the trajectory of storms worldwide this year, like many AI models it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, he said he plans to talk with the company about how it can make the AI results more useful for forecasters by providing extra under-the-hood data they can utilize to assess exactly why it is producing its conclusions.

“The one thing that nags at me is that while these forecasts seem to be highly accurate, the output of the system is kind of a opaque process,” remarked Franklin.

Broader Sector Developments

Historically, no a commercial entity that has produced a high-performance weather model which grants experts a peek into its techniques – in contrast to nearly all systems which are provided at no cost to the public in their full form by the governments that designed and maintain them.

The company is not alone in adopting AI to address challenging weather forecasting problems. The authorities are developing 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 long-range forecasts and better advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.