HPC for Weather Forecasting

Numerical weather prediction (NWP) is one of the first applications of scientific computing and remains an insatiable consumer of high-performance computing today. In the face of a half-century’s exponential and sometimes disruptive growth in HPC capability, major weather services around the world continuously develop and incorporate new meteorological research into large expensive operational forecasting software suites. At the heart is the weather model itself: a computational fluid dynamics core on a spherical domain with physics. The mapping of a planar grid to the geometry of the earth’s atmosphere presents one of a number of challenges for stability, accuracy, and computational efficiency that lead to development of the major numerical formulations and grid systems in use today: grid point, globally spectral, and finite/spectral element. Significant challenges await in the exascale era: large memory working sets, overall low computational intensity, load imbalance, and a fundamental lack of weak scalability in the face of critical real-time forecasting speed requirements. This chapter provides a history of weather and climate modeling on high-performance computing systems, a discussion of each of the major types of model dynamics formulations, grids, and model physics, and directions going forward on emerging HPC architectures.