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As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

Serving as lead forecaster 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. Not a single expert had previously made this confident forecast for rapid strengthening.

However, 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 did become a system of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Forecasting

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 hurricane. While I am unprepared to forecast that strength at this time given path variability, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the system drifts over very warm ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and currently the first to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the region. The confident prediction likely gave residents additional preparation time to get ready for the disaster, possibly saving lives and property.

The Way Google’s System Functions

Google’s model works by identifying trends that conventional time-intensive scientific prediction systems may overlook.

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

“What this hurricane season has proven in quick time is that the newcomer AI weather models are on par with and, in certain instances, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example 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.

AI training processes large datasets and extracts trends from them in a manner that its system only requires minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the primary systems that governments have used for decades that can take hours to run and require the largest supercomputers in the world.

Expert Responses and Upcoming Advances

Nevertheless, the fact that the AI could exceed earlier top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense storms.

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

Franklin said that although Google DeepMind is outperforming all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he plans to discuss with Google about how it can make the DeepMind output even more helpful for forecasters by providing additional under-the-hood data they can use to assess the reasons it is coming up with its answers.

“A key concern that troubles me is that while these forecasts seem to be really, really good, the output of the system is kind of a black box,” remarked Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has produced a high-performance forecasting system which allows researchers a peek into its techniques – in contrast to most systems which are provided at no cost to the general audience in their entirety by the governments that designed and maintain them.

Google is not the only one in adopting AI to address difficult meteorological problems. The authorities are developing their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over earlier traditional systems.

The next steps in AI weather forecasts seem to be startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they are receiving US government funding to do so. One company, WindBorne Systems, is also launching its proprietary weather balloons to fill the gaps in the national monitoring system.