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When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and start shifting towards the coast of Jamaica. Not a single expert had previously made this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a most intense storm. Although I am not ready to predict that strength at this time due to path variability, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the storm drifts over very warm ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the first artificial intelligence system dedicated to hurricanes, and now the first to outperform standard weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is the best – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to get ready for the disaster, potentially preserving people and assets.

How The System Works

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

“The AI performs much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are competitive with and, in certain instances, superior than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

To be sure, Google DeepMind is an example of AI training – a method that has been employed in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its system only requires minutes to generate an answer, and can do so on a desktop computer – in sharp difference to the flagship models that authorities have utilized for decades that can require many hours to run and need some of the biggest high-performance systems in the world.

Professional Responses and Future Advances

Nevertheless, the fact that Google’s model could outperform earlier top-tier legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the world’s strongest storms.

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

Franklin noted that although Google DeepMind is beating all competing systems on forecasting the future path of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity predictions wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he intends to talk with the company about how it can enhance the AI results even more helpful for forecasters by offering additional under-the-hood data they can use to evaluate exactly why it is producing its answers.

“The one thing that troubles me is that although these forecasts seem to be really, really good, the results of the system is essentially a black box,” said Franklin.

Wider Sector Developments

There has never been a commercial entity that has produced a top-level forecasting system which allows researchers a peek into its techniques – unlike most systems which are provided at no cost to the public in their entirety by the authorities that created and operate them.

The company is not the only one in adopting artificial intelligence to address difficult meteorological problems. The authorities also have their own artificial intelligence systems in the development phase – which have also shown better performance over earlier non-AI versions.

The next steps in AI weather forecasts seem to be startup companies tackling formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the national monitoring system.