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

As the lead forecaster on duty, he forecasted that in a single day the storm would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. No forecaster had ever issued such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense storm. Although I am not ready to forecast that strength yet due to path variability, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the system moves slowly over very warm ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to outperform standard weather forecasters at their own game. Through all 13 Atlantic storms this season, Google’s model is top-performing – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful landfalls recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided residents additional preparation time to get ready for the disaster, possibly saving lives and property.

How The System Works

The AI system operates through identifying trends that traditional lengthy physics-based prediction systems may miss.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a former forecaster.

“This season’s events has demonstrated in quick time is that the recent AI weather models are competitive with and, in some cases, more accurate than the slower physics-based forecasting tools we’ve relied upon,” Lowry added.

Understanding AI Technology

To be sure, Google DeepMind is an example of machine learning – a technique that has been employed in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a manner that its system only requires minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have utilized for decades that can take hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Future Advances

Still, the fact that the AI could exceed previous gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

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

Franklin noted that while the AI is beating all other models on predicting the trajectory of storms worldwide this year, like many AI models it occasionally gets extreme strength forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

During the next break, he said he plans to discuss with the company about how it can make the AI results more useful for experts by offering extra under-the-hood data they can use to assess exactly why it is coming up with its conclusions.

“The one thing that troubles me is that although these predictions seem to be highly accurate, the output of the model is kind of a opaque process,” said Franklin.

Wider Sector Developments

Historically, no a commercial entity that has developed a high-performance forecasting system which allows researchers a view of its techniques – in contrast to nearly all other models which are provided free to the general audience in their full form by the authorities that created and operate them.

Google is not the only one in adopting artificial intelligence to address challenging meteorological problems. The authorities are developing their respective AI weather models in the development phase – which have demonstrated improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly difficult problems such as long-range forecasts and improved advance warnings of severe weather and sudden deluges – and they are receiving 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.