Parallel processing and non-uniform grids in global air quality modeling

A large-scale global air quality model, running efficiently on a single vector processor, is enhanced to make more realistic and more long-term simulations feasible. Two strategies are combined: non-uniform grids and parallel processing. The communication through the hierarchy of non-uniform grids interferes with the inter-processor communication. We discuss load balance in the decomposition of the domain, I/O, and inter-processor communication. A model shows that the communication overhead for both techniques is very low, whence non-uniform grids allow for large speed-ups and high speed-up can be expected from parallelization. The implementation is in progress, and results of experiments will be reported elsewhere

A Zooming Technique for Wind Transport of Air Pollution

In air pollution dispersion models, typically systems of millions of equations that describe wind transport, chemistry and vertical mixing have to be integrated in time. To have more accurate results over specific fixed areas of interest---usually highly polluted areas with intensive emissions---a local grid refinement or zoom is often required. For the wind transport part of the models, i.e.\ for finite volume discretizations of the transport equation, we propose a zoom technique that is positive, mass-conservative and allows to use smaller time steps as enforced by the CFL restriction in the zoom regions only

Solving Vertical Transport and Chemistry in Air Pollution Models.

For the time integration of stiff transport-chemistry problems from air pollution modelling, standard ODE solvers are not feasible due to the large number of species and the 3D nature. The popular alternative, standard operator splitting, introduces artificial transients for short-lived species. This complicates the chemistry solution, easily causing large errors for such species. In the framework of an operational global air pollution model, we focus on the problem formed by chemistry and vertical transport, which is based on diffusion, cloud-related vertical winds, and wet deposition. Its specific nature leads to full Jacobian matrices, ruling out standard implicit integration. We compare Strang operator splitting with two alternatives: source splitting and an (unsplit) Rosenbrock method with approximate matrix factorization, all having equal computational cost. The comparison is performed with real data. All methods are applied with half-hour time steps, and give good accuracies. Rosenbrock is the most accurate, and source splitting is more accurate than Strang splitting. Splitting errors concentrate in short-lived species sensitive to solar radiation and species with strong emissions and depositions