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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the storm would become a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had ever issued this confident prediction for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Forecasting

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 simulation runs show Melissa becoming a Category 5 hurricane. Although I am unprepared to forecast that strength yet due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the system moves slowly over exceptionally hot sea temperatures which is the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat standard meteorological experts at their specialty. Through all tropical systems so far this year, Google’s model is top-performing – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in almost 200 years of data collection across the region. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the disaster, potentially preserving lives and property.

How The System Functions

The AI system operates through spotting patterns that conventional lengthy physics-based prediction systems may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer artificial intelligence systems are competitive with and, in some cases, more accurate than the slower traditional forecasting tools we’ve relied upon,” Lowry said.

Clarifying AI Technology

To be sure, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes mounds of data and extracts trends from them in a such a way that its model only requires minutes to come up with an result, and can do so on a standard PC – in strong contrast to the flagship models that governments have utilized for years that can require many hours to run and need the largest supercomputers in the world.

Expert Reactions and Upcoming Advances

Still, the fact that Google’s model could exceed previous top-tier traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The data is sufficient that it’s evident this is not just chance.”

He noted that while the AI is beating all competing systems on predicting the trajectory of storms globally this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It struggled with another storm previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin said he intends to talk with Google about how it can make the AI results even more helpful for experts by providing extra internal information they can utilize to evaluate the reasons it is coming up with its answers.

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

Broader Sector Trends

There has never been a commercial entity that has developed a high-performance forecasting system which grants experts a view of its techniques – unlike nearly all other models which are offered free to the public in their full form by the authorities that created and operate them.

The company is not alone in starting to use AI to solve challenging weather forecasting problems. The US and European governments also have their own AI weather models in the works – which have also shown improved skill over previous traditional systems.

Future developments in AI weather forecasts seem to be startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the national monitoring system.