The field of AI-driven weather modeling is advancing at a rapid pace, as illustrated by a new model that has critical advantages over other approaches.
Why it matters: Applying artificial intelligence to weather prediction holds the promise of significantly advancing forecast precision, reliability and delivery to the developing world.
- It could augment the role of human weather forecasters, providing them with another tool for forecasting extreme weather events as well as routine conditions.
Driving the news: The new model is the result of an international effort among the University of Cambridge, Alan Turing Institute, Microsoft Research and European Centre for Medium-Range Weather Forecasts (ECMWF).
Zoom in: The model, detailed in a study in the journal Nature, is known as Aardvark Weather. It offers what its creators call an "end-to-end AI forecasting system."
- Previous AI models developed by technology giants like Nvidia and Google take in real-world observations and apply AI methods to predict how weather conditions would unfold over time.
- These models don't require supercomputers and can be run at a fraction of the time of regular physics-based numerical models like the U.S. Global Forecast System, or GFS.
Yes, but: The AI models developed to date are still somewhat dependent on the work of traditional numerical systems at the initial step of incorporating vast amounts of weather data, a process known as data assimilation.
- What sets Aardvark Weather apart — and may usher in a new era in AI-driven models — is that it uses a single machine-learning model that takes in observations from satellites, weather stations, ships and other sensors, and yields high-resolution global and local forecasts.
- It doesn't involve traditional numerical weather models at any step of the process, setting it apart from other new AI systems.
- In other words, it's a purely AI-driven weather play.
Aardvark also uses far fewer observations as inputs compared to both traditional models in use and other AI-driven ones.
The intrigue: The researchers tout Aardvark's ability to result in specially-tailored forecasts while being run on a desktop computer, providing results that are available within minutes.
- Importantly, they claim that even with just a fraction of the input data from current weather observations, the system outperforms the GFS model on particular variables and competes with National Weather Service forecasts made using a combination of modeling and human forecast expertise.
- Perhaps the biggest breakthrough of the new model is that its simplicity and the way it's designed to learn from input data can provide a means for tailoring forecasts for specific applications and regions.
- These could include forecasting wind speeds for renewable energy installations or predicting rainfall for agricultural interests.
What we're watching: How the broader field of AI weather modeling evolves and is incorporated into the work of government forecast agencies
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