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As Developing Cyclone Melissa swirled 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 predicted that in just 24 hours the storm would become a severe hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had ever issued this confident prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form 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 storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his confidence: “Roughly 40/50 AI simulation runs show Melissa reaching a most intense storm. While I am not ready to predict that strength at this time given path variability, that remains a possibility.

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

Surpassing Traditional Systems

Google DeepMind is the pioneer AI model focused on tropical cyclones, and currently the first to beat standard meteorological experts at their own game. Across all tropical systems so far this year, Google’s model is the best – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts ever documented in nearly two centuries of record-keeping across the region. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the disaster, possibly saving lives and property.

How Google’s System Functions

The AI system operates through identifying trends that traditional time-intensive scientific weather models may overlook.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex meteorologist.

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

Clarifying Machine Learning

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

Machine learning takes mounds of data and extracts trends from them in a such a way that its system only takes a few minutes to generate an result, and can do so on a standard PC – in sharp difference to the primary systems that governments have utilized for decades that can require many hours to process and need the largest high-performance systems in the world.

Expert Responses and Future Developments

Nevertheless, the fact that the AI could outperform earlier gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former expert. “The sample is sufficient that it’s pretty clear this is not just beginner’s luck.”

Franklin said that although the AI is beating all other models on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, he said he intends to discuss with the company about how it can enhance the DeepMind output even more helpful for forecasters by providing extra internal information they can use to assess exactly why it is producing its answers.

“A key concern that troubles me is that while these predictions seem to be really, really good, the results of the model is essentially a black box,” said Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has developed a top-level forecasting system which allows researchers a view of its methods – unlike nearly all other models which are provided at no cost to the general audience in their entirety by the governments that designed and maintain them.

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

Future developments in artificial intelligence predictions seem to be new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the US weather-observing network.