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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. No forecaster had previously made such a bold prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa becoming a most intense storm. Although I am not ready to forecast that intensity at this time due to track uncertainty, that is still plausible.

“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 highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and currently the initial to beat standard weather forecasters at their specialty. Through all tropical systems this season, Google’s model is top-performing – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the region. Papin’s bold forecast likely gave residents extra time to get ready for the catastrophe, potentially preserving lives and property.

How Google’s Model Functions

Google’s model works by spotting patterns that conventional lengthy physics-based prediction systems may overlook.

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

“This season’s events has demonstrated in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

It’s important to note, the system is an example of AI training – a technique that has been employed in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to generate an result, and can do so on a desktop computer – in sharp difference to the flagship models that governments have used for years that can take hours to process and need some of the biggest high-performance systems in the world.

Professional Responses and Future Advances

Nevertheless, the reality that the AI could exceed earlier top-tier traditional systems so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

“It’s astonishing,” said James Franklin, a former forecaster. “The sample is now large enough that it’s pretty clear this is not just beginner’s luck.”

Franklin noted that although the AI is outperforming all other models on predicting the future path of storms worldwide this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he intends to talk with the company about how it can make the DeepMind output even more helpful for forecasters by providing extra internal information they can utilize to assess the reasons it is producing its conclusions.

“A key concern that nags at me is that although these predictions appear highly accurate, the results of the model is essentially a black box,” remarked Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has produced a top-level weather model which allows researchers a view of its techniques – in contrast to nearly all systems which are provided at no cost to the public in their full form by the authorities that designed and maintain them.

The company is not alone in starting to use artificial intelligence to address challenging meteorological problems. The authorities are developing their own AI weather models in the development phase – which have also shown better performance over earlier non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the US weather-observing network.