Enlarge / Image of some of the atmospheric circulation seen during NeuralGCM runs. (credit: Google)
Right now, the world's best weather forecast model is a General Circulation Model, or GCM, put together by the European Center for Medium-Range Weather Forecasts. A GCM is in part based on code that calculates the physics of various atmospheric processes that we understand well. For a lot of the rest, GCMs rely on what's termed "parameterization," which attempts to use empirically determined relationships to approximate what's going on with processes where we don't fully understand the physics.
Lately, GCMs have faced some competition from machine-learning techniques, which train AI systems to recognize patterns in meteorological data and use those to predict the conditions that will result over the next few days. Their forecasts, however, tend to get a bit vague after more than a few days and can't deal with the sort of long-term factors that need to be considered when GCMs are used to study climate change.
On Monday, a team from Google's AI group and the European Centre for Medium-Range Weather Forecasts are announcing NeuralGCM, a system that mixes physics-based atmospheric circulation with AI parameterization of other meteorological influences. Neural GCM is computationally efficient and performs very well in weather forecast benchmarks. Strikingly, it can also produce reasonable-looking output for runs that cover decades, potentially allowing it to address some climate-relevant questions. While it can't handle a lot of what we use climate models for, there are some obvious routes for potential improvements.
