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When Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in a single day the weather system would become a severe hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had ever issued such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI simulation runs show Melissa reaching a Category 5 storm. Although I am unprepared to predict that intensity at this time due to track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the storm drifts over very warm sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the initial to outperform traditional weather forecasters at their own game. Through all 13 Atlantic storms so far this year, the AI is top-performing – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to get ready for the disaster, potentially preserving people and assets.

The Way Google’s Model Functions

Google’s model works by identifying trends that conventional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“What this hurricane season has proven in quick time is that the recent AI weather models are on par with and, in some cases, superior than the less rapid traditional weather models we’ve traditionally leaned on,” he added.

Clarifying AI Technology

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

Machine learning takes mounds of data and pulls out patterns 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 desktop computer – in strong contrast to the primary systems that authorities have utilized for years that can require many hours to run and require the largest supercomputers in the world.

Expert Responses and Future Advances

Nevertheless, the fact that the AI could exceed previous top-tier traditional systems so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest weather systems.

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

Franklin noted that although Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes globally this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It struggled with Hurricane Erin previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin said he plans to discuss with Google about how it can enhance the AI results more useful for experts by providing additional internal information they can use to assess exactly why it is producing its conclusions.

“The one thing that troubles me is that although these forecasts appear really, really good, the results of the model is kind of a black box,” remarked Franklin.

Broader Sector Trends

There has never been a commercial entity that has produced a top-level weather model which allows researchers a peek into its methods – in contrast to most other models which are provided at no cost to the general audience in their full form by the governments that designed and maintain them.

Google is not the only one in adopting AI to solve difficult weather forecasting problems. The authorities also have their respective artificial intelligence systems in the development phase – which have also shown better performance over previous traditional systems.

The next steps in AI weather forecasts seem to be new firms tackling formerly tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and sudden deluges – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the US weather-observing network.