However, we have recently ported our LES code to the GPU, resulting in an entirely GPU-resident Atmospheric LES (called GALES; Schalkwijk et al, BAMS, 2012). The use of the GPU provides a huge computational boost to the possibilities of LES: simulations formerly performed on 30-60 computer cores can now be performed on a single GPU. This is a potential leap in the possibilities of LES application.
At present, this speedup allows GALES to run on a day by day basis in a set-up in which the LES represents a single grid box (about 25km x 25km) of a large-scale weather model (LSM). This set-up this allows GALES to run in a 'predictive' mode, with the LSM providing the current large-scale forcings, and is a prudent test into the possibilities of LES weather prediction. Moreover, the set-up provides an unprecedented three-dimensional high-resolution dataset of daily cloud conditions, structure and evolution. By running in hindcast as well, we have already simulated a full year of real-weather cloud evolution.
A recent grant on computer time on the CURIE-GPU cluster in France allows us to go even further, however. By up-scaling the above set-up, i.e. by simulating with multiple GPUs a matrix of weather model grid boxes, it becomes possible to fully encompass a small country as the Netherlands or an appreciable part of a US state (approx. 400km x 400km with ~256 GPUs), providing rain and cloud field data with high temporal and spatial resolution. This allows us to bridge a part of the gap between large scale models and LES, such that we can study, for example, mesoscale dynamics interacting with the turbulent boundary layer.