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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as 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 Jamaican shoreline. Not a single expert had previously made this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa did become a system of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa reaching a Category 5 storm. While I am not ready to predict that strength yet given track uncertainty, that is still plausible.

“It appears likely that a phase of rapid intensification will occur as the storm drifts over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and now the first to outperform traditional meteorological experts at their specialty. Across all tropical systems so far this year, Google’s model is the best – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the region. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the disaster, possibly saving lives and property.

The Way Google’s System Works

Google’s model works by spotting patterns that traditional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

It’s important to note, the system is an example of AI training – a method that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes mounds of data and extracts trends from them in a such a way that its model only requires minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the primary systems that authorities have used for decades that can require many hours to process and need the largest high-performance systems in the world.

Expert Reactions and Upcoming Developments

Nevertheless, the reality that the AI could outperform previous gold-standard traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” said James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not just beginner’s luck.”

He said that although Google DeepMind is beating all competing systems on predicting the future path of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions wrong. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, he said he intends to discuss with the company about how it can enhance the AI results even more helpful for forecasters by offering additional under-the-hood data 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 really, really good, the results of the system is essentially a black box,” remarked Franklin.

Wider Sector Trends

There has never been a commercial entity that has produced a high-performance weather model which grants experts a peek into its methods – unlike nearly all other models which are provided free to the public in their entirety by the authorities that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to solve challenging 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 appear to involve new firms taking swings at previously difficult problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they have secured federal support to do so. A particular firm, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the national monitoring system.