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When Tropical Storm Melissa was churning off the coast 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 weather system would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made this confident prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the form of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a system of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 AI simulation runs indicate Melissa becoming a most intense hurricane. Although I am unprepared to predict that strength at this time given path variability, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the storm moves slowly over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat traditional meteorological experts at their specialty. Through all 13 Atlantic storms this season, Google’s model is the best – even beating experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving lives and property.

The Way The System Functions

Google’s model works by identifying trends that traditional time-intensive physics-based prediction systems may overlook.

“The AI performs far faster than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the recent AI weather models are competitive with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry added.

Understanding Machine Learning

To be sure, the system is an instance of AI training – a method that has been used in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a such a way that its system only takes a few minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the flagship models that authorities have used for decades that can require many hours to process and require some of the biggest high-performance systems in the world.

Professional Reactions and Future Developments

Still, the reality that the AI could exceed previous top-tier legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“It’s astonishing,” commented James Franklin, a former forecaster. “The data is now large enough that it’s evident this is not just chance.”

He noted that while the AI is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity predictions wrong. It struggled with another storm previously, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin stated he plans to discuss with Google about how it can enhance the DeepMind output more useful for experts by offering additional under-the-hood data they can utilize to assess the reasons it is producing its conclusions.

“A key concern that troubles me is that although these predictions seem to be highly accurate, the results of the system is essentially a black box,” remarked Franklin.

Broader Sector Trends

Historically, no a commercial entity that has produced a high-performance weather model which grants experts a view of its techniques – unlike most other models which are offered free to the public in their entirety by the authorities that designed and maintain them.

Google 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 traditional systems.

Future developments in AI weather forecasts appear to involve new firms taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the national monitoring system.