As Tropical Storm Melissa was churning south of Haiti, weather expert 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 storm would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made this confident forecast for quick intensification.
But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa evolved into a system of remarkable power that ravaged Jamaica.
Increasing Dependence on Artificial Intelligence Predictions
Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 AI ensemble members show Melissa reaching a most intense hurricane. Although I am not ready to predict that strength at this time given track uncertainty, that is still plausible.
“There is a high probability that a phase of quick strengthening will occur as the storm drifts over very warm ocean waters which represent the most extreme marine thermal energy in the whole Atlantic basin.”
Surpassing Traditional Systems
Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and currently the initial to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is the best – surpassing experts on path forecasts.
Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in almost 200 years of data collection across the region. The confident prediction probably provided residents additional preparation time to get ready for the catastrophe, possibly saving lives and property.
The Way The Model Functions
Google’s model works by identifying trends that conventional lengthy physics-based weather models may overlook.
“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former forecaster.
“This season’s events has proven in quick time is that the newcomer AI weather models are on par with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he added.
Understanding Machine Learning
To be sure, the system is an example of AI training – a technique that has been used in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.
Machine learning processes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to generate an answer, and can do so on a standard PC – in strong contrast to the primary systems that governments have used for decades that can require many hours to run and need the largest supercomputers in the world.
Professional Responses and Future Developments
Still, the fact that the AI could exceed previous top-tier legacy models so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the world’s strongest storms.
“I’m impressed,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s pretty clear this is not just chance.”
Franklin said that although Google DeepMind is outperforming all other models on forecasting the trajectory of storms globally this year, like many AI models it occasionally gets high-end intensity predictions wrong. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.
In the coming offseason, Franklin said he intends to discuss with the company about how it can enhance the AI results more useful for experts by providing additional under-the-hood data they can use to evaluate exactly why it is coming up with its conclusions.
“A key concern that nags at me is that while these forecasts appear really, really good, the output of the model is essentially a opaque process,” remarked Franklin.
Broader Industry Developments
There has never been a commercial entity that has produced a high-performance forecasting system which grants experts a peek into its methods – unlike nearly all other models which are provided free to the public in their entirety by the governments that designed and maintain them.
Google is not the only one in starting to use artificial intelligence to solve difficult meteorological problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have also shown better performance over previous non-AI versions.
Future developments in artificial intelligence predictions seem to be new firms tackling previously difficult problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.
