Numerical weather prediction (NWP) has proven to be computationally challenging due to its inherent multiscale nature. Currently, the highest resolution NWP models use a horizontal resolution of approximately 15 km. At this resolution many important processes in the atmosphere are not resolved. To increase the resolution of NWP models highly scalable atmospheric models are needed.
The author is one of the developers of the Non-hydrostatic Uniﬁed Model of the Atmosphere (NUMA) which is used by the Naval Research Laboratory, Monterey as the dynamical core inside its next generation weather prediction system NEPTUNE. NUMA solves the fully compressible Navier-Stokes equations by means of high-order Galerkin methods (both spectral element as well as discontinuous Galerkin methods can be used). NUMA is capable of running middle and upper atmosphere simulations since it does not make use of the shallow-atmosphere approximation.
The author optimized NUMA for the Blue Gene Q supercomputer Mira of the Argonne National Laboratory. NUMA achieves an excellent strong scaling efficiency of 99% which allows to run a one day forecast of a baroclinic instability test case at 3.0km global horizontal resolution at double precision within the time frame required for operational weather prediction by using the entire 3.14 million hardware threads of Mira. This presentation gives an overview of the optimizations and scalability studies that were performed and discusses the expected implications of this work for using NUMA on next generation supercomputers.