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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: AI technology in the form of Google’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are heavily relying upon 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: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 hurricane. Although I am unprepared to predict that intensity yet due to path variability, that is still plausible.

“It appears likely that a phase of rapid intensification will occur as the storm moves slowly over exceptionally hot ocean waters which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first AI model dedicated to hurricanes, and now the initial to beat traditional meteorological experts at their specialty. Through all tropical systems this season, the AI is the best – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided residents additional preparation time to prepare for the catastrophe, potentially preserving lives and property.

The Way The Model Functions

The AI system works by identifying trends that traditional time-intensive physics-based 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 meteorologist.

“What this hurricane season has proven in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the slower traditional forecasting tools we’ve traditionally leaned on,” he said.

Understanding Machine Learning

To be sure, Google DeepMind is an example of AI training – a technique 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 processes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to come up with an result, and can do so on a standard PC – in sharp difference to the flagship models that authorities have utilized for years that can require many hours to process and require the largest supercomputers in the world.

Expert Reactions and Future Advances

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

“It’s astonishing,” commented James Franklin, a former expert. “The data is sufficient that it’s evident this is not a case of beginner’s luck.”

He said that while the AI is beating all other models on predicting the future path of hurricanes globally this year, like many AI models it occasionally gets extreme strength predictions inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

During the next break, Franklin said he plans to discuss with Google about how it can make the AI results more useful for experts by providing additional internal information they can utilize to evaluate exactly why it is producing its conclusions.

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

Wider Sector Trends

Historically, no a private, for-profit company that has produced a top-level forecasting system which allows researchers a view of its methods – in contrast to most other models which are provided at no cost to the public in their full form by the governments that created and operate them.

Google is not alone in starting to use artificial intelligence to solve difficult meteorological problems. The authorities are developing their respective AI weather models in the works – which have also shown better performance over previous non-AI versions.

Future developments in AI weather forecasts appear to involve new firms taking swings at previously tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.