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

Serving as lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and start shifting towards the coast of Jamaica. Not a single expert had ever issued this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense hurricane. While I am not ready to forecast that intensity yet given path variability, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the storm moves slowly over very warm ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer AI model focused on tropical cyclones, and currently the initial to beat standard weather forecasters at their specialty. Through all tropical systems so far this year, the AI is top-performing – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

How Google’s Model Functions

The AI system operates through spotting patterns that conventional lengthy physics-based prediction systems may overlook.

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

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry added.

Understanding Machine Learning

To be sure, Google DeepMind is an example of AI training – a method that has been used in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the flagship models that governments have used for years that can require many hours to process and need some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Developments

Still, the fact that the AI could outperform earlier top-tier legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s pretty clear this is not just chance.”

He said that while Google DeepMind is beating all other models on forecasting the future path of storms globally this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It struggled with another storm previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

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

“A key concern that troubles me is that while these predictions seem to be highly accurate, the output of the model is essentially a black box,” remarked Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has developed a high-performance forecasting system which grants experts a peek into its techniques – in contrast to most other models which are offered free to the public in their full form by the authorities that designed and maintain them.

The company is not alone in starting to use AI to solve challenging meteorological problems. The US and European governments are developing their own AI weather models in the development phase – which have demonstrated better performance over previous traditional systems.

The next steps in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as long-range forecasts and improved advance warnings of severe weather and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.