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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 grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the storm would intensify into a severe hurricane and start shifting 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: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 AI ensemble members show Melissa becoming a Category 5 storm. Although I am not ready to predict that intensity yet due to track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the system drifts over very warm ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the first to beat standard meteorological experts at their specialty. Across all tropical systems this season, the AI is top-performing – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

How Google’s Model Functions

Google’s model works by spotting patterns that conventional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are on par with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a method that has been used in research fields like meteorology for years – and is not generative AI like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a manner 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 primary systems that governments have used for decades that can take hours to process and need the largest supercomputers in the world.

Professional Responses and Upcoming Developments

Nevertheless, the fact that the AI could exceed earlier gold-standard legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a retired expert. “The data is sufficient that it’s evident this is not just chance.”

Franklin noted that although the AI is outperforming all competing systems on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin stated he plans to discuss with the company about how it can enhance the DeepMind output even more helpful for experts by offering extra internal information they can utilize to assess exactly why it is producing its conclusions.

“A key concern that nags at me is that although these forecasts seem to be highly accurate, the results of the model is essentially a opaque process,” said Franklin.

Wider Sector Developments

There has never been a private, for-profit company that has produced a high-performance weather model which allows researchers a peek into its techniques – unlike most systems which are provided at no cost to the public in their entirety by the governments that designed and maintain them.

Google is not the only one in starting to use artificial intelligence to solve challenging meteorological problems. The authorities also have their own AI weather models in the works – which have also shown better performance over earlier non-AI versions.

The next steps in AI weather forecasts seem to be new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.