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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would become a severe hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a system 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 explained in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a most intense hurricane. Although I am unprepared to predict that intensity yet given path variability, that remains a possibility.

“It appears likely that a period of rapid intensification will occur as the system moves slowly over very warm ocean waters which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer artificial intelligence system focused on hurricanes, and currently the initial to outperform traditional meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, the AI is top-performing – even beating experts on path forecasts.

Melissa eventually made landfall in Jamaica at maximum 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, possibly saving people and assets.

How Google’s System Functions

Google’s model operates through spotting patterns that traditional time-intensive scientific weather models may miss.

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

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the slower physics-based weather models we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

It’s important to note, the system is an example of AI training – a technique that has been employed in research fields like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a such a way that its model only takes a few minutes to come up with an result, and can operate on a desktop computer – in strong contrast to the flagship models that governments have used for years that can require many hours to process and need some of the biggest high-performance systems in the world.

Professional Responses and Future Developments

Still, the reality that Google’s model could exceed earlier top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the world’s strongest storms.

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

He said that while the AI is beating all other models on forecasting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing quick strengthening to maximum intensity above 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 experts by providing extra internal information they can use to evaluate exactly why it is coming up with its conclusions.

“A key concern that troubles me is that although these predictions seem to be highly accurate, the output of the model is essentially a black box,” said Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has developed a top-level weather model which grants experts a peek into its techniques – unlike nearly all systems which are offered at no cost to the general audience in their full form by the authorities that designed and maintain them.

The company is not alone in starting to use AI to address difficult weather forecasting problems. The authorities are developing their own AI weather models in the development phase – which have demonstrated improved skill over previous traditional systems.

The next steps in AI weather forecasts appear to involve startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also deploying its proprietary weather balloons to fill the gaps in the national monitoring system.