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As Developing Cyclone Melissa swirled south of Haiti, weather expert 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 weather system would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made this confident prediction for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa evolved into a system of remarkable power that tore through Jamaica.

Growing Reliance on AI Forecasting

Meteorologists 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 primary reason for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa reaching a most intense storm. Although I am unprepared to forecast that intensity yet due to path variability, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected as the system drifts over exceptionally hot sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer AI model focused on tropical cyclones, and currently the first to outperform standard meteorological experts at their specialty. Across all tropical systems this season, Google’s model is the best – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful coastal impacts recorded in almost 200 years of data collection across the region. The confident prediction likely gave residents additional preparation time to get ready for the catastrophe, possibly saving people and assets.

The Way Google’s Model Works

Google’s model operates through spotting patterns that conventional time-intensive physics-based prediction systems may miss.

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

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the slower traditional forecasting tools we’ve relied upon,” he added.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an instance of machine learning – a method that has been used in research fields like weather science for years – and is not generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to generate an result, and can operate on a standard PC – in sharp difference to the flagship models that governments have used for decades that can require many hours to run and require some of the biggest high-performance systems in the world.

Expert Reactions and Future Advances

Still, the reality that Google’s model could exceed previous gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

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

Franklin said that while the AI is outperforming all competing systems on forecasting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It struggled with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

During the next break, he stated he intends to discuss with the company about how it can enhance the AI results more useful for experts by offering additional internal information they can use to evaluate the reasons it is producing its answers.

“A key concern that troubles me is that although these forecasts appear really, really good, the results of the model is kind of a black box,” said Franklin.

Wider Sector Trends

Historically, no a commercial entity that has produced a high-performance forecasting system which allows researchers a peek into its methods – in contrast to most other models which are offered free to the general audience in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use AI to solve difficult weather forecasting problems. The authorities also have their respective artificial intelligence systems in the development phase – which have also shown better performance over previous non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.