How Google’s DeepMind Tool is Transforming Tropical Cyclone Forecasting with Speed

When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it would soon grow into a monster hurricane.

As the primary meteorologist on duty, he forecasted that in a single day the storm would become a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense storm. Although I am unprepared to predict that strength at this time due to path variability, that remains a possibility.

“There is a high probability that a phase of quick strengthening is expected as the system moves slowly over very warm ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the first AI model dedicated to tropical cyclones, and currently the initial to outperform standard weather forecasters at their specialty. Through all tropical systems this season, Google’s model is the best – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to prepare for the disaster, potentially preserving lives and property.

The Way Google’s Model Works

Google’s model operates through identifying trends that traditional lengthy scientific prediction systems may overlook.

“They do it far faster than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid traditional forecasting tools we’ve relied upon,” Lowry said.

Clarifying Machine Learning

To be sure, the system is an example of AI training – a method that has been used in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a manner that its model only requires minutes to come up with an result, and can do so on a desktop computer – in sharp difference to the flagship models that authorities have used for years that can require many hours to run and require some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Advances

Nevertheless, the reality that the AI could exceed earlier top-tier traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to predict the world’s strongest storms.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not just chance.”

Franklin noted that although Google DeepMind is beating all other models on predicting the trajectory of storms worldwide this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin stated he intends to talk with Google about how it can enhance the DeepMind output even more helpful for forecasters by offering extra under-the-hood data they can utilize to evaluate exactly why it is producing its conclusions.

“The one thing that troubles me is that while these forecasts appear really, really good, the results of the system is kind of a black box,” said Franklin.

Wider Industry Developments

Historically, no a commercial entity that has produced a top-level forecasting system which allows researchers a view of its techniques – unlike nearly all systems which are offered free to the public in their entirety by the authorities that designed and maintain them.

Google is not alone in starting to use AI to solve difficult weather forecasting problems. The US and European governments also have their own AI weather models in the development phase – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts seem to be startup companies taking swings at previously difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the US weather-observing network.