The Way Alphabet’s AI Research Tool is Revolutionizing Tropical Cyclone Prediction with Rapid Pace

When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had ever issued this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Predictions

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa reaching a Category 5 storm. Although I am unprepared to predict that strength yet given track uncertainty, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the system drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system focused on hurricanes, and currently the initial to beat standard meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is the best – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls ever documented in almost 200 years of data collection across the region. Papin’s bold forecast likely gave residents additional preparation time to get ready for the catastrophe, possibly saving people and assets.

The Way Google’s Model Functions

The AI system operates through identifying trends that conventional lengthy scientific weather models may miss.

“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“This season’s events has proven in short order is that the recent artificial intelligence systems are on par with and, in some cases, superior than the less rapid physics-based weather models we’ve relied upon,” he added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of AI training – a technique that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the primary systems that governments have used for years that can require many hours to run and need some of the biggest high-performance systems in the world.

Professional Responses and Upcoming Advances

Nevertheless, the reality that the AI could outperform previous gold-standard legacy models so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to predict the most intense weather systems.

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

Franklin noted that although the AI is outperforming all competing systems on forecasting the future path of storms globally this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. 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, he stated he intends to discuss with Google about how it can enhance the AI results even more helpful for experts by offering extra internal information they can use to evaluate the reasons it is producing its answers.

“A key concern that troubles me is that while these forecasts appear highly accurate, the results of the system is essentially a black box,” remarked Franklin.

Broader Industry Trends

There has never been a commercial entity that has developed a top-level forecasting system which allows researchers a peek into its methods – unlike most systems which are provided free to the general audience in their entirety by the governments that created and operate them.

Google is not the only one in adopting AI to solve challenging weather forecasting problems. The authorities also have their own AI weather models in the works – which have demonstrated improved skill over previous traditional systems.

Future developments in AI weather forecasts appear to involve new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.