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When Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the storm would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had ever issued this confident prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa did become a system of remarkable power that tore through Jamaica.

Increasing Dependence on AI 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 primary reason for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa becoming a Category 5 hurricane. While I am unprepared to forecast that intensity at this time due to path variability, that remains a possibility.

“There is a high probability that a phase of rapid intensification will occur as the system moves slowly over exceptionally hot ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to outperform traditional meteorological experts at their own game. Through all 13 Atlantic storms this season, Google’s model is the best – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the disaster, possibly saving lives and property.

How Google’s Model Functions

The AI system works by spotting patterns that traditional lengthy physics-based prediction systems may miss.

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

“What this hurricane season has proven in quick time is that the newcomer AI weather models are on par with and, in some cases, superior than the less rapid traditional weather models we’ve relied upon,” he added.

Understanding AI Technology

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

Machine learning processes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to generate an result, and can operate on a standard PC – in strong contrast to the flagship models that authorities have used for years that can take hours to process and need some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the fact that the AI could exceed earlier gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense storms.

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

He noted that while the AI is outperforming all competing systems on forecasting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he intends to talk with the company about how it can make the DeepMind output even more helpful for forecasters by offering extra 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 while these forecasts seem to be highly accurate, the results of the system is kind of a opaque process,” remarked Franklin.

Wider Sector Developments

Historically, no a commercial entity that has developed a top-level weather model which grants experts a view of its methods – in contrast to most other models which are provided at no cost to the public in their entirety by the authorities that created and operate them.

The company is not alone in starting to use AI to solve difficult weather forecasting problems. The authorities are developing their respective AI weather models in the works – which have demonstrated improved skill over previous traditional systems.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly difficult problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the US weather-observing network.