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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made such a bold forecast for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs show Melissa reaching a most intense hurricane. While I am not ready to predict that intensity yet due to track uncertainty, that is still plausible.

“There is a high probability that a phase of rapid intensification is expected as the storm drifts over very warm sea temperatures which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first AI model dedicated to tropical cyclones, and currently the first to outperform traditional weather forecasters at their own game. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to prepare for the disaster, potentially preserving people and assets.

The Way The System Works

The AI system works by identifying trends that conventional lengthy scientific prediction systems may overlook.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid physics-based weather models we’ve relied upon,” Lowry added.

Understanding AI Technology

To be sure, the system is an example of machine learning – a technique that has been used in research fields like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns 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 sharp difference to the flagship models that governments have used for decades that can take hours to process and need the largest supercomputers in the world.

Professional Reactions and Future Advances

Nevertheless, the fact that Google’s model could exceed previous top-tier legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense storms.

“I’m impressed,” commented James Franklin, a former expert. “The data is sufficient that it’s evident this is not just chance.”

Franklin said that although the AI is beating all competing systems on predicting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, he stated he intends to discuss with the company about how it can make the DeepMind output more useful for forecasters by offering extra under-the-hood data they can utilize to evaluate exactly why it is producing its answers.

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

Broader Sector Developments

Historically, no a private, for-profit company that has produced a high-performance forecasting system which grants experts a peek into its methods – in contrast to most systems which are provided free to the public in their entirety by the governments that created and operate them.

Google is not alone in adopting artificial intelligence to address challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have demonstrated better performance over previous non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.