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When Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and start shifting towards 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: AI technology in the form of Google’s new DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his confidence: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 hurricane. Although I am unprepared to predict that strength at this time given track uncertainty, that is still plausible.

“There is a high probability that a phase of rapid intensification is expected as the system drifts over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Models

The AI model is the pioneer AI model focused on hurricanes, and currently the initial to beat traditional weather forecasters at their own game. Across all tropical systems this season, Google’s model is top-performing – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, among the most powerful landfalls recorded in almost 200 years of data collection across the region. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the disaster, possibly saving people and assets.

The Way Google’s Model Functions

Google’s model works by spotting patterns that traditional lengthy scientific weather models may overlook.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex meteorologist.

“This season’s events has proven in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve relied upon,” he added.

Understanding Machine Learning

To be sure, Google DeepMind is an example of AI training – a technique that has been employed in data-heavy sciences like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to come up with an answer, and can do so on a standard PC – in sharp difference to the primary systems that governments have utilized for years that can take hours to process and need some of the biggest supercomputers in the world.

Expert Responses and Future Developments

Nevertheless, the reality that Google’s model could outperform previous gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

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

Franklin noted that while Google DeepMind is outperforming all other models on forecasting the trajectory of storms globally this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 above 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 experts by offering additional under-the-hood data they can utilize to assess the reasons it is coming up with its answers.

“The one thing that nags at me is that while these forecasts seem to be highly accurate, the results of the model is essentially a black box,” remarked Franklin.

Wider Sector Developments

There has never been a commercial entity that has developed a high-performance forecasting system which grants experts a peek into its techniques – in contrast to nearly all systems which are offered at no cost to the public in their entirety by the governments that designed and maintain them.

Google is not alone in starting to use AI to solve difficult meteorological problems. The authorities also have their respective artificial intelligence systems in the works – which have demonstrated better performance over previous traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at previously difficult problems such as long-range forecasts and improved early alerts of tornado outbreaks and sudden deluges – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the US weather-observing network.