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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had ever issued this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Dependence on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a most intense storm. While I am unprepared to forecast that intensity at this time due to path variability, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the system moves slowly over very warm ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the pioneer AI model dedicated to tropical cyclones, and currently the initial to outperform traditional meteorological experts at their specialty. Through all 13 Atlantic storms this season, Google’s model is top-performing – surpassing experts on track predictions.

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 region. The confident prediction likely gave people in Jamaica extra time to get ready for the catastrophe, possibly saving people and assets.

The Way Google’s System Functions

Google’s model operates through spotting patterns that traditional lengthy scientific weather models may miss.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are competitive with and, in some cases, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an instance of AI training – a method that has been used in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

AI training processes 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 result, and can operate on a standard PC – in strong contrast to the flagship models that authorities have used for decades that can take hours to process and require some of the biggest supercomputers in the world.

Professional Reactions and Future Developments

Nevertheless, the fact that the AI could exceed previous top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

He noted that although the AI is outperforming all competing systems on forecasting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions wrong. It had difficulty with Hurricane Erin previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, he stated he intends to talk with Google about how it can enhance the DeepMind output more useful for experts by offering extra under-the-hood data they can use to evaluate exactly why it is coming up with its conclusions.

“The one thing that troubles me is that although these predictions appear really, really good, the output of the model is essentially a opaque process,” said Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has produced a top-level forecasting system which allows researchers a peek into its techniques – in contrast to most other models which are provided 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 artificial intelligence to solve challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the works – which have demonstrated improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.