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When Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had ever issued this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 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 reaching a Category 5 hurricane. While I am unprepared to predict that strength yet due to track uncertainty, that is still plausible.

“It appears likely that a period of quick strengthening will occur as the storm moves slowly over very warm sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

The AI model is the first artificial intelligence system focused on tropical cyclones, and now the first to outperform standard weather forecasters at their own game. Through all tropical systems so far this year, the AI is top-performing – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls ever documented in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided residents additional preparation time to prepare for the catastrophe, possibly saving lives and property.

The Way The System Works

The AI system operates through spotting patterns that traditional time-intensive physics-based prediction systems may overlook.

“The AI performs far faster than their traditional counterparts, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in some cases, superior than the less rapid traditional weather models we’ve relied upon,” he said.

Clarifying Machine Learning

It’s important to note, the system is an example of AI training – a technique that has been employed in research fields like weather science for a long time – and is not generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a manner that its model only requires minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the primary systems that authorities have utilized for decades that can require many hours to run and need the largest supercomputers in the world.

Expert Reactions and Upcoming Developments

Still, the fact that Google’s model could exceed earlier gold-standard traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the most intense weather systems.

“I’m impressed,” commented James Franklin, a former expert. “The data is now large enough that it’s evident this is not just chance.”

Franklin said that although 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 had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he plans to talk with Google about how it can enhance the AI results even more helpful for forecasters by offering extra under-the-hood data they can utilize to assess the reasons it is coming up with its conclusions.

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

Broader Sector Trends

There has never been a private, for-profit company that has produced a high-performance forecasting system which allows researchers a peek into its techniques – unlike nearly all other models which are provided free 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 challenging meteorological problems. The authorities also have their respective artificial intelligence systems in the development phase – which have also shown better performance over earlier traditional systems.

The next steps in artificial intelligence predictions seem to be startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is also launching its proprietary weather balloons to fill the gaps in the national monitoring system.