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As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa becoming a Category 5 storm. While I am unprepared to predict that strength at this time due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the storm moves slowly over exceptionally hot sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer AI model focused on hurricanes, and currently the initial to outperform standard meteorological experts at their specialty. Across all tropical systems so far this year, the AI is top-performing – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts recorded in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave residents additional preparation time to prepare for the catastrophe, possibly saving people and assets.

The Way The Model Works

Google’s model works by identifying trends that conventional lengthy physics-based weather models may miss.

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

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the slower traditional weather models we’ve relied upon,” he said.

Clarifying Machine Learning

It’s important to note, the system is an instance of machine learning – a technique that has been used in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a such a way that its model only takes a few 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 years that can require many hours to process and need the largest high-performance systems in the world.

Expert Reactions and Future Advances

Nevertheless, the reality that Google’s model could outperform earlier gold-standard legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not a case of chance.”

He said that although the AI is outperforming all competing systems on forecasting the future path of storms worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It had difficulty with another storm previously, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin said he plans to discuss with the company about how it can enhance the AI results more useful for forecasters by providing extra under-the-hood data they can use to evaluate the reasons it is producing its conclusions.

“The one thing that troubles me is that although these predictions appear really, really good, the output of the system is kind of a black box,” remarked Franklin.

Wider Sector Developments

There has never been a private, for-profit company that has produced a top-level weather model which grants experts a peek into its techniques – unlike most systems which are provided 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 AI to address difficult meteorological problems. The US and European governments also have their respective AI weather models in the development phase – which have demonstrated better performance over earlier non-AI versions.

The next steps in AI weather forecasts seem to be new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the US weather-observing network.