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When Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a monster hurricane.

Serving as lead forecaster on duty, he predicted that in a single day the weather system would become a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Predictions

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 hurricane. Although I am unprepared to predict that strength at this time given path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the storm moves slowly over exceptionally hot ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first AI model dedicated to tropical cyclones, and currently the initial to outperform traditional meteorological experts at their own game. Through all tropical systems so far this year, Google’s model is the best – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave residents additional preparation time to prepare for the catastrophe, potentially preserving lives and property.

The Way Google’s Model Functions

The AI system operates through identifying trends that conventional lengthy scientific prediction systems may overlook.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the recent artificial intelligence systems are on par with and, in some cases, superior than the less rapid traditional forecasting tools we’ve relied upon,” he added.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a method that has been used in research fields like weather science for years – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an result, and can operate on a standard PC – in sharp difference to the primary systems that authorities have utilized for years that can require many hours to run and need some of the biggest high-performance systems in the world.

Expert Reactions and Upcoming Developments

Still, the reality that the AI could outperform previous gold-standard traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the most intense storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

Franklin said that while Google DeepMind is beating all competing systems on forecasting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It struggled with another storm previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

During the next break, he said he intends to discuss with the company about how it can make the DeepMind output even more helpful for experts by offering extra internal information they can use to assess the reasons it is coming up with its answers.

“A key concern that nags at me is that while these predictions appear really, really good, the results of the system is essentially a opaque process,” remarked Franklin.

Wider Industry Trends

Historically, no a commercial entity that has produced a top-level weather model which grants experts a peek into its methods – in contrast to nearly all systems which are offered free to the general audience in their entirety by the authorities that designed and maintain them.

The company is not the only one in starting to use AI to solve difficult meteorological problems. The authorities also have their respective AI weather models in the development phase – which have demonstrated improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be new firms taking swings at previously tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the national monitoring system.