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When Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would become a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 storm. Although I am unprepared to forecast that intensity yet given path variability, that remains a possibility.

“There is a high probability that a phase of rapid intensification is expected as the storm moves slowly over very warm sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the first artificial intelligence system dedicated to hurricanes, and currently the initial to beat standard weather forecasters at their specialty. Across all tropical systems this season, the AI is the best – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to prepare for the catastrophe, potentially preserving lives and property.

How Google’s System Works

Google’s model works by identifying trends that conventional lengthy physics-based prediction systems may miss.

“They do it far faster than their physics-based cousins, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“This season’s events has proven in short order is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the slower physics-based forecasting tools we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, the system is an example of machine learning – 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 manner that its model only takes a few minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have used for years that can require many hours to process and need the largest supercomputers in the world.

Expert Responses and Future Advances

Nevertheless, the fact that Google’s model could outperform previous top-tier legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” commented James Franklin, a former forecaster. “The data is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

He noted that although the AI is beating all competing systems on forecasting the future path of hurricanes globally this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

During the next break, Franklin said he intends to discuss with the company about how it can make the DeepMind output more useful for forecasters by providing additional under-the-hood data they can utilize to assess exactly why it is producing its conclusions.

“A key concern that nags at me is that although these predictions appear highly accurate, the results of the system is essentially a black box,” said Franklin.

Broader Sector Trends

Historically, no a commercial entity that has produced a top-level weather model which grants experts a view of its methods – in contrast to most other models which are provided free to the public in their full form by the governments that designed and maintain them.

Google is not the only one in adopting AI to address difficult weather forecasting problems. The authorities are developing their respective AI weather models in the development phase – which have demonstrated better performance over earlier traditional systems.

The next steps in artificial intelligence predictions seem to be new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.