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As Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in a single day the storm would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made this confident prediction for quick intensification.

But, Papin possessed a secret advantage: 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 storm of remarkable power that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. Although I am unprepared to predict that intensity yet due to track uncertainty, 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 sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the first AI model dedicated to tropical cyclones, and currently the initial to outperform traditional weather forecasters at their specialty. Through all tropical systems so far this year, Google’s model is top-performing – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the region. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the disaster, possibly saving lives and property.

The Way Google’s System Works

The AI system operates through identifying trends that conventional time-intensive scientific prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“What this hurricane season has proven in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the less rapid physics-based weather models we’ve relied upon,” Lowry said.

Understanding AI Technology

To be sure, Google DeepMind is an example of machine learning – a technique that has been used in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to generate 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 require many hours to run and require some of the biggest high-performance systems in the world.

Professional Reactions and Future Developments

Still, the fact that the AI could outperform earlier top-tier traditional systems so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former forecaster. “The data is sufficient that it’s pretty clear this is not a case of chance.”

Franklin said that although the AI is outperforming all other models on predicting the future path of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength predictions inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, he stated he plans to talk with the company about how it can enhance the AI results more useful for forecasters by offering extra under-the-hood data they can utilize to evaluate the reasons it is coming up with its conclusions.

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

Broader Industry Trends

Historically, no a commercial entity that has produced a top-level weather model which grants experts a peek into its techniques – unlike most other models which are provided free to the general audience in their entirety by the governments that designed and maintain them.

The company is not the only one in adopting AI to solve difficult weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be new firms taking swings at previously tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to pursue this. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.