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When Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the storm would intensify into a severe hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had previously made this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. Although I am not ready to forecast that strength yet given track uncertainty, that is still plausible.

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

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system focused on hurricanes, and currently the initial to outperform traditional weather forecasters at their own game. Through all 13 Atlantic storms so far this year, Google’s model is the best – even beating human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at maximum 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 extra time to prepare for the disaster, potentially preserving people and assets.

The Way Google’s System Works

The AI system works by identifying trends that traditional time-intensive scientific weather models may miss.

“They do it far faster than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” he said.

Understanding AI Technology

To be sure, the system is an instance of machine learning – a technique that has been employed in research fields like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to generate an result, and can operate on a standard PC – in strong contrast to the primary systems that authorities have utilized for decades that can require many hours to run and require some of the biggest supercomputers in the world.

Expert Responses and Upcoming Advances

Still, the reality that the AI could outperform previous top-tier legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

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

He noted that while the AI is outperforming all other models on predicting the future path of hurricanes globally this year, like many AI models it sometimes errs on extreme strength forecasts wrong. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, Franklin said he plans to discuss with Google about how it can enhance the DeepMind output more useful for forecasters by offering extra under-the-hood data they can use to evaluate the reasons it is producing its answers.

“A key concern that troubles me is that while these forecasts appear really, really good, the output of the system is essentially a black box,” said Franklin.

Broader Sector Trends

There has never been a private, for-profit company that has produced a high-performance forecasting system which grants experts a peek into its methods – in contrast to nearly all other models which are offered at no cost to the public in their entirety by the governments that created and operate them.

The company is not the only one in adopting artificial intelligence to address difficult meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have demonstrated better performance over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at formerly difficult problems such as long-range forecasts and better early alerts of severe weather and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the US weather-observing network.