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As Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had ever issued such a bold forecast for quick intensification.

However, Papin possessed a secret advantage: AI technology in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a most intense hurricane. Although I am unprepared to forecast that intensity yet due to track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening is expected as the storm moves slowly over exceptionally hot ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the first to beat traditional meteorological experts at their specialty. Across all 13 Atlantic storms this season, Google’s model is the best – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the region. The confident prediction likely gave residents additional preparation time to get ready for the disaster, potentially preserving lives and property.

The Way Google’s Model Functions

Google’s model works by identifying trends that conventional lengthy scientific prediction systems may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, more accurate than the slower physics-based weather models we’ve relied upon,” he added.

Understanding AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been employed in data-heavy sciences like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the flagship models that authorities have used for years that can require many hours to run and need some of the biggest supercomputers in the world.

Professional Responses and Upcoming Developments

Still, the reality that the AI could outperform previous gold-standard legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not just beginner’s luck.”

He noted that although the AI is outperforming all competing systems on predicting the future path of hurricanes globally this year, like many AI models it occasionally gets high-end intensity predictions wrong. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he plans to talk with Google about how it can enhance the AI results more useful for experts by offering additional internal information they can utilize to assess exactly why it is coming up with its conclusions.

“A key concern that nags at me is that while these forecasts seem to be highly accurate, the output of the model is kind of a black box,” said Franklin.

Wider Industry Developments

Historically, no a commercial entity that has produced a top-level weather model which allows researchers a view of its methods – unlike nearly all other models which are provided free to the general audience in their full form by the authorities that designed and maintain them.

Google is not alone in starting to use artificial intelligence to solve difficult weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the works – which have also shown better performance over previous traditional systems.

Future developments in artificial intelligence predictions appear to involve new firms taking swings at previously tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the national monitoring system.