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When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind hurricane model – launched for the initial occasion 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 Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his confidence: “Roughly 40/50 AI simulation runs show Melissa reaching a Category 5 storm. Although I am not ready to predict that strength at this time due to path variability, that is still plausible.

“There is a high probability that a phase of quick strengthening will occur as the storm moves slowly over exceptionally hot ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to outperform traditional weather forecasters at their own game. Across all tropical systems this season, Google’s model is the best – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

The Way Google’s Model Works

The AI system operates through identifying trends that conventional lengthy scientific prediction systems may overlook.

“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the recent AI weather models are competitive with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like meteorology for a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to come up with an result, and can do so on a standard PC – in strong contrast to the primary systems that authorities have utilized for decades that can take hours to run and require the largest supercomputers in the world.

Expert Reactions and Upcoming Developments

Still, the reality that the AI could exceed earlier gold-standard legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

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

He said that while Google DeepMind is outperforming all other models on predicting the trajectory of storms worldwide this year, like many AI models it occasionally gets extreme strength predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, Franklin stated he plans to talk with Google about how it can enhance the DeepMind output even more helpful for forecasters by offering extra under-the-hood data they can utilize to assess exactly why it is coming up with its answers.

“The one thing that nags at me is that while these predictions appear really, really good, the output of the model is essentially a opaque process,” said Franklin.

Wider 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 methods – unlike most other models which are offered free to the general audience in their full form by the governments that designed and maintain them.

The company is not the only one in adopting artificial intelligence to address difficult weather forecasting problems. The US and European governments are developing their own AI weather models in the development phase – which have demonstrated improved skill over earlier traditional systems.

Future developments in AI weather forecasts appear to involve new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.