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As Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would become a category 4 hurricane and start shifting towards the Jamaican shoreline. No forecaster had ever issued this confident forecast for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Growing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa becoming a Category 5 storm. While I am not ready to predict that intensity yet due to path variability, that is still plausible.

“It appears likely that a period of quick strengthening will occur as the storm drifts over exceptionally hot ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

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

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the catastrophe, potentially preserving lives and property.

The Way Google’s System Functions

The AI system works by spotting patterns that conventional time-intensive physics-based prediction systems may miss.

“The AI performs much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

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

Clarifying Machine Learning

To be sure, the system is an example of machine learning – a method that has been used in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to generate an result, and can operate on a standard PC – in sharp difference to the primary systems that governments have utilized for decades that can take hours to process and need some of the biggest supercomputers in the world.

Expert Responses and Upcoming Advances

Nevertheless, the fact that Google’s model could outperform earlier gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense storms.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is sufficient that it’s evident this is not just chance.”

He said that although Google DeepMind is beating all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It had difficulty 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 intends to discuss with Google about how it can make the DeepMind output more useful for experts by providing additional under-the-hood data they can utilize to evaluate exactly why it is coming up with its answers.

“A key concern that nags at me is that although these forecasts appear highly accurate, the output of the model is kind of a opaque process,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has produced a high-performance forecasting system which allows researchers a view of its techniques – in contrast to most systems which are offered at no cost to the general audience in their full form by the governments that created and operate them.

The company is not the only one in adopting artificial intelligence to address difficult meteorological problems. The US and European governments are developing their respective AI weather models in the development phase – which have also shown better performance over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks 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 proprietary atmospheric sensors to address deficiencies in the national monitoring system.