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When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to escalate to 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 towards the Jamaican shoreline. Not a single expert had ever issued this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa becoming a Category 5 hurricane. Although I am not ready to predict that strength at this time given path variability, that is still plausible.

“It appears likely that a phase of rapid intensification will occur as the system moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer AI model focused on hurricanes, and now the first to beat traditional meteorological experts at their specialty. Through all tropical systems so far this year, Google’s model is the best – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided residents extra time to prepare for the catastrophe, possibly saving people and assets.

How Google’s System Functions

The AI system works by spotting patterns that conventional time-intensive scientific weather models may miss.

“They do it far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the less rapid traditional forecasting tools we’ve relied upon,” Lowry added.

Understanding Machine Learning

It’s important to note, the system is an example of AI training – a technique that has been employed in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a such a way that its system only requires minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have used for years that can take hours to process and need the largest supercomputers in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the reality that Google’s model could outperform previous top-tier traditional systems so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense storms.

“It’s astonishing,” commented James Franklin, a retired expert. “The data is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin noted that although Google DeepMind is beating all competing systems on predicting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, he said he intends to discuss with Google about how it can make the DeepMind output more useful for forecasters by providing extra under-the-hood data they can utilize to evaluate the reasons it is coming up with its answers.

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

Wider Sector Developments

Historically, no a private, for-profit company that has developed a top-level forecasting system which grants experts a peek into its methods – in contrast to most systems which are offered free to the general audience in their full form by the authorities that designed and maintain them.

Google is not the only one in adopting artificial intelligence to solve challenging meteorological problems. The authorities are developing 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 appear to involve startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is even launching its own atmospheric sensors to address deficiencies in the US weather-observing network.