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When Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as lead forecaster on duty, he predicted that in a single day the storm would become a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa becoming a most intense hurricane. While I am not ready to forecast that intensity yet given track uncertainty, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the storm drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to beat standard weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is top-performing – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the region. The confident prediction likely gave people in Jamaica extra time to get ready for the disaster, possibly saving people and assets.

The Way The System Works

Google’s model operates through spotting patterns that conventional time-intensive physics-based prediction systems may miss.

“The AI performs far faster than their physics-based cousins, and the computing power is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in quick time is that the recent AI weather models are competitive with and, in certain instances, superior than the slower physics-based forecasting tools we’ve traditionally leaned on,” he added.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of AI training – a technique that has been employed in research fields like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its system only requires minutes to generate an result, and can operate on a standard PC – in strong contrast to the flagship models that authorities have utilized for years that can require many hours to process and need the largest supercomputers in the world.

Professional Reactions and Future Advances

Nevertheless, the fact that the AI could outperform earlier gold-standard traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest storms.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s evident this is not just beginner’s luck.”

He said that although Google DeepMind is beating all competing systems on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It struggled with another storm previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin said he plans to discuss with the company about how it can enhance the DeepMind output more useful for forecasters by providing additional under-the-hood data they can use to assess exactly why it is producing its answers.

“The one thing that troubles me is that while these predictions appear really, really good, the results of the model is essentially a black box,” said Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has produced a top-level weather model which allows researchers 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 created and operate them.

Google is not alone in adopting artificial intelligence to solve difficult weather forecasting problems. The authorities also have their respective AI weather models in the development phase – which have also shown better performance over earlier traditional systems.

The next steps in artificial intelligence predictions seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks 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 atmospheric sensors to address deficiencies in the US weather-observing network.