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As Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had ever issued this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a most intense storm. While I am unprepared to forecast that strength yet due to path variability, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the system moves slowly over very warm sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the first to beat traditional meteorological experts at their own game. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the disaster, potentially preserving lives and property.

How The Model Functions

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

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Understanding AI Technology

To be sure, Google DeepMind is an instance of machine learning – a method that has been employed in research fields like weather science for a long time – and is not generative AI like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a manner that its system only takes a few minutes to generate an answer, and can do so on a desktop computer – in sharp difference to the primary systems that authorities have used for decades that can require many hours to run and require some of the biggest high-performance systems in the world.

Professional Responses and Upcoming Developments

Nevertheless, the reality that the AI could outperform previous gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” said James Franklin, a retired forecaster. “The data is now large enough that it’s evident this is not a case of chance.”

He said that although the AI is beating all other models on forecasting the trajectory of storms worldwide this year, similar to other systems it occasionally gets extreme strength predictions wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he intends to discuss 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 evaluate the reasons it is producing its answers.

“The one thing that troubles me is that while these predictions seem to be really, really good, the results of the system is essentially a black box,” said Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has developed a top-level weather model which grants experts a peek into its methods – unlike nearly all other models which are offered at no cost to the public in their full form by the authorities that created and operate them.

Google is not alone in starting to use AI to address challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have also shown improved skill over previous non-AI versions.

The next steps in artificial intelligence predictions 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 flash flooding – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the national monitoring system.