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When Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had ever issued this confident forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 hurricane. While I am not ready to predict that intensity yet given track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening will occur as the system moves slowly over very warm sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer AI model focused on tropical cyclones, and now the first to beat standard weather forecasters at their own game. Across all tropical systems so far this year, Google’s model is the best – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the region. The confident prediction probably provided residents additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

The Way Google’s Model Works

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

“They do it far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid traditional forecasting tools we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, the system is an instance of machine learning – a method that has been employed in research fields like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an result, and can do so on a standard PC – in sharp difference to the primary systems that authorities have used for years that can take hours to process and require the largest supercomputers in the world.

Professional Responses and Future Advances

Nevertheless, the reality that Google’s model could outperform previous top-tier legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former expert. “The sample is sufficient that it’s evident this is not a case of beginner’s luck.”

Franklin noted that although Google DeepMind is outperforming all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it occasionally gets high-end intensity predictions wrong. It struggled with another storm previously, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin said he intends to discuss with the company about how it can make the AI results more useful for experts by offering extra under-the-hood data they can utilize to assess the reasons it is coming up with its answers.

“A key concern that nags at me is that while these forecasts seem to be really, really good, the output of the system is kind of a opaque process,” said Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has produced a high-performance forecasting system which allows researchers a view of its techniques – unlike most other models which are offered free to the public in their entirety by the governments that designed and maintain them.

The company is not alone in adopting artificial intelligence to solve challenging meteorological problems. The authorities are developing their own AI weather models in the works – which have also shown improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve new firms taking swings at previously difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.