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As Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would intensify into a category 4 hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made such a bold prediction for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa evolved into a system of remarkable power that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members show Melissa becoming a most intense storm. Although I am unprepared 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 ocean waters which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer artificial intelligence system focused on hurricanes, and now the initial to beat standard weather forecasters at their own game. Across all tropical systems this season, the AI is the best – even beating 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 region. Papin’s bold forecast probably provided people in Jamaica additional preparation time to prepare for the disaster, potentially preserving lives and property.

The Way Google’s System Functions

Google’s model operates through spotting patterns that traditional time-intensive scientific weather models may overlook.

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the recent AI weather models are competitive with and, in certain instances, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of AI training – a technique that has been used in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training processes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the flagship models that governments have utilized for years that can take hours to process and need the largest high-performance systems in the world.

Expert Reactions and Future Advances

Nevertheless, the reality that the AI could exceed earlier gold-standard traditional systems so quickly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that although Google DeepMind is beating all competing systems on predicting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he plans to discuss with Google about how it can enhance the AI results even more helpful for experts by providing additional under-the-hood data they can use to assess the reasons it is producing its conclusions.

“The one thing that troubles me is that while these predictions appear highly accurate, the results of the system is essentially a opaque process,” said Franklin.

Broader Sector Trends

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

Google is not the only one in starting to use artificial intelligence to address challenging weather forecasting problems. The authorities also have their own artificial intelligence systems in the development phase – which have demonstrated better performance over previous traditional systems.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.