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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

As the lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. No forecaster had previously made this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a most intense storm. While I am unprepared to forecast that strength yet given track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the system drifts over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first artificial intelligence system focused on hurricanes, and now the first to beat standard weather forecasters at their own game. Across all tropical systems this season, the AI is the best – surpassing experts on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the disaster, potentially preserving people and assets.

How The System Functions

The AI system operates through spotting patterns that conventional time-intensive scientific prediction systems may overlook.

“The AI performs much more quickly 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 newcomer AI weather models are competitive with and, in certain instances, superior than the less rapid traditional forecasting tools we’ve relied upon,” Lowry added.

Clarifying AI Technology

It’s important to note, the system is an instance of machine learning – a technique that has been employed in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes mounds of data and extracts trends from them in a manner that its system only requires minutes to come up with an answer, and can do so on a desktop computer – in sharp difference to the flagship models that governments have used for decades that can take hours to process and require the largest supercomputers in the world.

Expert Reactions and Future Developments

Nevertheless, the reality that the AI could outperform previous gold-standard traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” said James Franklin, a former forecaster. “The data is now large enough that it’s evident this is not just beginner’s luck.”

Franklin noted that while Google DeepMind is beating all competing systems on forecasting the trajectory of storms worldwide this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It struggled with another storm previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin stated he intends to discuss with Google about how it can enhance the DeepMind output more useful for forecasters by providing extra under-the-hood data they can utilize to evaluate exactly why it is producing its answers.

“A key concern that troubles me is that although these predictions appear highly accurate, the results of the system is essentially a black box,” remarked Franklin.

Wider Industry Developments

There has never been a commercial entity that has developed a top-level forecasting system which allows researchers a peek into its techniques – unlike nearly all systems which are provided free to the general audience in their full form by the authorities that created and operate them.

Google is not the only one in starting to use AI to address challenging meteorological problems. The authorities also have their own artificial intelligence systems in the works – which have also shown improved skill over previous non-AI versions.

The next steps in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to do so. One company, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the US weather-observing network.