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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. While I am not ready to predict that intensity at this time given path variability, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the storm drifts over very warm ocean waters which is the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the initial to outperform standard weather forecasters at their specialty. Through all tropical systems so far this year, the AI is the best – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in almost 200 years of data collection across the region. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the catastrophe, potentially preserving lives and property.

How The Model Functions

The AI system works by identifying trends that conventional lengthy physics-based prediction systems may overlook.

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

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the slower traditional weather models we’ve traditionally leaned on,” Lowry added.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a method that has been used in data-heavy sciences 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 such a way that its model only requires minutes to generate an result, and can do so on a desktop computer – in strong contrast to the flagship models that governments have utilized for decades that can require many hours to run and need the largest supercomputers in the world.

Professional Reactions and Upcoming Advances

Nevertheless, the reality that Google’s model could outperform previous gold-standard traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense weather systems.

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

Franklin noted that while the AI is beating all other models on forecasting the future path of storms worldwide this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It had difficulty with another storm previously, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, Franklin said he intends to discuss with Google about how it can make the AI results more useful for forecasters by providing extra under-the-hood data they can utilize to assess exactly why it is producing its answers.

“The one thing that nags at me is that although these forecasts appear really, really good, the output of the system is kind of a black box,” remarked Franklin.

Broader Sector Trends

Historically, no a commercial entity that has produced a high-performance weather model which grants experts a peek into its methods – in contrast to most other models which are provided at no cost to the general audience in their entirety by the governments that created and operate them.

The company is not alone in adopting AI to solve challenging meteorological problems. The US and European governments also have their own AI weather models in the works – which have demonstrated improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions appear to involve new firms tackling previously tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.