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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. No forecaster had previously made such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 hurricane. While I am not ready to predict that strength yet given track uncertainty, that remains a possibility.

“There is a high probability that a phase of rapid intensification is expected as the system drifts over very warm sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first AI model focused on tropical cyclones, and now the initial to outperform standard weather forecasters at their specialty. Through all 13 Atlantic storms this season, Google’s model is the best – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the catastrophe, potentially preserving lives and property.

The Way Google’s Model Functions

Google’s model works by spotting patterns that traditional lengthy physics-based weather models may miss.

“They do it far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid physics-based weather models we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

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

AI training takes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the primary systems that authorities have utilized for decades that can require many hours to run and need some of the biggest supercomputers in the world.

Expert Responses and Upcoming Advances

Nevertheless, the fact that the AI could outperform earlier gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a retired expert. “The data is sufficient that it’s evident this is not a case of chance.”

He said that although the AI is beating all competing systems on predicting the future path of storms globally this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he said he intends to talk with the company about how it can enhance the AI results more useful for experts by providing extra under-the-hood data they can utilize to assess the reasons it is producing its answers.

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

Wider Sector Developments

Historically, no a private, for-profit company that has produced a top-level weather model which allows researchers a peek into its methods – unlike most other models which are offered at no cost to the public in their entirety by the governments that designed and maintain them.

The company is not alone in adopting artificial intelligence to solve difficult meteorological problems. The authorities also have their own AI weather models in the development phase – which have also shown better performance over previous non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the national monitoring system.