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When Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would become a severe hurricane and start shifting towards the coast of Jamaica. Not a single expert had ever issued such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Predictions

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 storm. Although I am unprepared to forecast that strength yet due to track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the storm moves slowly over very warm sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the first artificial intelligence system focused on tropical cyclones, and now the initial to outperform traditional meteorological experts at their own game. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the region. The confident prediction likely gave residents additional preparation time to prepare for the disaster, possibly saving lives and property.

How The System Functions

Google’s model works by identifying trends that traditional lengthy scientific 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 ex meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are on par with and, in some cases, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” he said.

Understanding Machine Learning

It’s important to note, the system is an example of machine learning – a technique that has been used in data-heavy sciences like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a manner that its model only takes a few minutes to come up with an answer, and can do so on a standard PC – in sharp difference to the primary systems that authorities have used for decades that can take hours to run and require the largest supercomputers in the world.

Expert Reactions and Future Developments

Nevertheless, the reality that Google’s model could outperform earlier top-tier legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a retired forecaster. “The data is sufficient that it’s pretty clear this is not a case of chance.”

He noted that while Google DeepMind is beating all other models on forecasting the future path of storms globally this year, like many AI models it occasionally gets extreme strength forecasts wrong. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

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

“The one thing that nags at me is that although these forecasts appear highly accurate, the results of the system is kind of a opaque process,” said Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has developed a top-level weather model which grants experts a peek into its techniques – in contrast to nearly all other models which are offered at no cost to the general audience in their full form by the governments that designed and maintain them.

Google is not alone in starting to use AI to address challenging meteorological problems. The authorities also have their own AI weather models in the works – which have demonstrated improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies taking swings at previously difficult problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to fill the gaps in the US weather-observing network.