🔗 Share this article

As Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.

As the lead forecaster on duty, he forecasted that in a single day the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. No forecaster had previously made this confident forecast for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 AI ensemble members show Melissa reaching a Category 5 storm. Although I am not ready to forecast that intensity at this time given path variability, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected as the storm drifts over exceptionally hot sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and now the initial to beat standard weather forecasters at their own game. Through all tropical systems so far this year, Google’s model is the best – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the region. The confident prediction probably provided people in Jamaica extra time to prepare for the disaster, potentially preserving lives and property.

How The Model Works

Google’s model operates through spotting patterns that conventional time-intensive physics-based prediction systems may overlook.

“The AI performs far faster than their traditional counterparts, and the computing power is more affordable and demanding,” stated Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the less rapid traditional forecasting tools we’ve relied upon,” Lowry added.

Understanding Machine Learning

To be sure, the system is an example of machine learning – a technique that has been used in data-heavy sciences like weather science for a long time – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to generate an result, and can operate on a standard PC – in strong contrast to the flagship models that authorities have used for decades that can take hours to process and need the largest high-performance systems in the world.

Professional Responses and Future Developments

Still, the reality that the AI could exceed earlier top-tier legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s evident this is not a case of beginner’s luck.”

He noted that while the AI is beating all other models on predicting the trajectory of storms worldwide this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin earlier this year, 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 enhance the AI results even more helpful for experts by offering extra internal information they can utilize to evaluate the reasons it is producing its conclusions.

“The one thing that nags at me is that while these predictions appear really, really good, the output of the model is kind of a opaque process,” remarked Franklin.

Wider Sector Developments

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its methods – in contrast to most systems which are offered free to the public in their entirety by the governments that created and operate them.

Google is not the only one in starting to use artificial intelligence to address difficult weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the works – which have also shown better performance over earlier traditional systems.

The next steps in artificial intelligence predictions appear to involve new firms taking swings at previously tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.