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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had ever issued such a bold prediction for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI ensemble members show Melissa becoming a Category 5 hurricane. Although I am unprepared to forecast that strength at this time due to path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification is expected as the system moves slowly over exceptionally hot ocean waters which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first AI model focused on tropical cyclones, and currently the initial to outperform traditional meteorological experts at their specialty. Across all tropical systems this season, Google’s model is the best – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls recorded in almost 200 years of data collection 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 scientific weather models may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the slower traditional weather models we’ve relied upon,” Lowry added.

Understanding AI Technology

To be sure, the system is an example of AI training – a method that has been used in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training 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 governments have utilized for decades that can require many hours to run and need the largest supercomputers in the world.

Professional Reactions and Future Advances

Nevertheless, the reality that the AI could outperform earlier top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a retired expert. “The data is sufficient that it’s pretty clear this is not a case of chance.”

Franklin said that while Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes globally this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he stated he plans to talk with Google about how it can enhance the DeepMind output even more helpful for experts by providing additional internal information they can use to evaluate exactly why it is producing its conclusions.

“The one thing that troubles me is that while these predictions seem to be highly accurate, the output of the model is essentially a black box,” remarked Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has developed a high-performance weather model which grants experts a view of its techniques – unlike most systems which are offered at no cost to the public in their entirety by the governments that designed and maintain them.

The company is not the only one in starting to use AI to address challenging weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the development phase – which have demonstrated better performance over previous non-AI versions.

Future developments in AI weather forecasts appear to involve new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they have secured federal support to do so. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.