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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had ever issued this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense storm. While I am unprepared to forecast that strength at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the system moves slowly over exceptionally hot sea temperatures which represent the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and now the initial to outperform traditional weather forecasters at their own game. Through all 13 Atlantic storms this season, the AI is the best – even beating experts on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to get ready for the catastrophe, possibly saving people and assets.

The Way Google’s Model Functions

The AI system works by spotting patterns that traditional time-intensive physics-based weather models may miss.

“They do it far faster than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are competitive with and, in certain instances, superior than the slower traditional forecasting tools we’ve relied upon,” Lowry added.

Clarifying AI Technology

It’s important to note, Google DeepMind 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 generative AI like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a manner that its model only requires minutes to generate an result, and can do so on a standard PC – in sharp difference to the primary systems that governments 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 Future Developments

Still, the fact that Google’s model could outperform earlier top-tier legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” commented James Franklin, a retired forecaster. “The data is sufficient that it’s pretty clear this is not just beginner’s luck.”

He noted that although the AI is beating all competing systems on forecasting the future path of storms worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin said he intends to discuss with the company about how it can enhance the AI results even more helpful for experts by providing additional internal information they can use to evaluate exactly why it is producing its conclusions.

“A key concern that nags at me is that while these predictions appear really, really good, the output of the model is essentially a black box,” remarked Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has produced a top-level weather model which allows researchers a peek into its techniques – unlike most other models which are provided free to the public in their full form by the governments that created and operate them.

The company is not alone in adopting AI to solve difficult meteorological problems. The authorities also have their own artificial intelligence systems in the development phase – which have also shown improved skill over previous traditional systems.

Future developments in AI weather forecasts appear to involve new firms tackling previously tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.