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When Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in a single day the storm would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had ever issued such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: 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 did become a system of remarkable power that tore through Jamaica.

Increasing Reliance on AI Forecasting

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members show Melissa becoming a Category 5 storm. Although I am unprepared to forecast that strength yet given path variability, that is still plausible.

“There is a high probability that a phase of quick strengthening will occur as the storm moves slowly over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and now the initial to beat traditional weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, the AI is top-performing – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to prepare for the disaster, potentially preserving lives and property.

How Google’s Model Functions

The AI system operates through spotting patterns that traditional time-intensive physics-based weather models may miss.

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

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are on par with and, in certain instances, superior than the slower physics-based weather models we’ve traditionally leaned on,” he added.

Understanding Machine Learning

It’s important to note, the system is an example of machine learning – a technique that has been employed in research fields like weather science for years – and is not creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a such a way that its model only requires minutes to come up with an result, and can do so on a standard PC – in strong contrast to the primary systems that governments have used for decades that can take hours to run and require the largest high-performance systems in the world.

Expert Responses and Upcoming Advances

Nevertheless, the fact that Google’s model could exceed earlier gold-standard traditional systems so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” commented James Franklin, a former expert. “The data is now large enough that it’s evident this is not a case of beginner’s luck.”

He noted that although the AI is beating all other models on forecasting the trajectory of storms worldwide this year, like many AI models it sometimes errs on high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

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

“The one thing that nags at me is that although these forecasts appear really, really good, the results of the system is essentially a opaque process,” remarked Franklin.

Broader Industry Developments

There has never been a private, for-profit company that has produced a high-performance forecasting system which allows researchers a peek into its methods – unlike nearly all systems which are provided at no cost to the public in their full form by the authorities that created and operate them.

The company is not the only one in adopting AI to solve difficult weather forecasting problems. The US and European governments also have their own AI weather models in the development phase – which have demonstrated improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve startup companies tackling previously difficult problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the national monitoring system.