Development of an immersed boundary method to resolve complex terrain in the Weather Research and Forecasting model

This presentation describes implementation and testing of an immersed boundary method (IBM) for explicitly resolving complex urban terrain for atmospheric dispersion simulations using the Weather Research and Forecasting (WRF) model.

Flow and dispersion in urban areas is greatly complicated by the presence of buildings which divert mean flow, affect surface heating and cooling, and alter the structure of turbulence in the lower part of the boundary layer. Traditionally, urban flow simulations have been performed by specialized computational fluid dynamics (CFD) codes which can accommodate the geometric complexity inherent in urban landscapes. These CFD codes typically use unstructured meshes which must be generated according to complex algorithms combined with time-consuming manual adjustment to align with solid boundaries. In addition, CFD codes for urban dispersion usually solve the incompressible flow equations and have limited options for representing surface fluxes and dynamic forcing due to synoptic-scale atmospheric conditions. These limitations make it difficult, if not impossible, to incorporate realistic atmospheric forcing and its effect on dispersion at urban scales.

We are developing a framework for building-resolving urban flow simulations using the WRF model. WRF is formulated as a meso-scale model which solves the compressible flow equations with full surface physics. WRF is commonly used at resolutions as fine as 1 km. Additionally, WRF allows for lateral boundary forcing based on synoptic data and performs two-way grid nesting at subsequent finer nested grid levels. To accommodate complex building geometries, we have implemented an immersed boundary method (IBM) along with a rough surface parameterization. This IBM alleviates difficulties associated with terrain following coordinates in WRF (which are unsuitable for steep topographic gradients such as those presented by buildings) and provides flexible and efficient grid generation for urban areas. Our progress to date will be described with illustrative examples.