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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made this confident prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his certainty: “Roughly 40/50 AI simulation runs show Melissa reaching a Category 5 hurricane. Although I am not ready to forecast that strength at this time given path variability, that is still plausible.

“There is a high probability that a period of quick strengthening will occur as the system drifts over exceptionally hot sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and now the first to outperform standard weather forecasters at their own game. Through all tropical systems so far this year, Google’s model is the best – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the region. Papin’s bold forecast likely gave residents extra time to prepare for the disaster, possibly saving people and assets.

The Way The Model Functions

The AI system works by identifying trends that traditional time-intensive physics-based prediction systems may miss.

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the slower traditional weather models we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a technique that has been used in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have used for decades that can require many hours to run and require some of the biggest supercomputers in the world.

Expert Responses and Upcoming Developments

Nevertheless, the fact that Google’s model could outperform earlier gold-standard legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the most intense storms.

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

Franklin noted that while Google DeepMind is outperforming all other models on forecasting the future path of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It had difficulty with another storm previously, as it was also undergoing 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 AI results more useful for forecasters by providing additional internal information they can use to evaluate exactly why it is producing its answers.

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

Broader Industry Trends

There has never been a private, for-profit company that has developed a high-performance forecasting system which allows researchers a view of its techniques – in contrast to nearly all other models which are offered free to the general audience in their full form by the governments that created and operate them.

The company is not the only one in adopting artificial intelligence to address challenging meteorological problems. The authorities also have their respective AI weather models in the works – which have also shown improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is also launching its own atmospheric sensors to fill the gaps in the national monitoring system.