PNNL

Abstract

Cold-air pools (CAPs), or stable atmospheric boundary layers that form within topographic basins, are associated with poor air quality, hazardous weather, and low wind energy output. Accurate prediction of CAP dynamics presents a challenge for mesoscale forecast models, in part because they occur in regions of complex terrain, where traditional turbulence parameterizations may not be appropriate. This study examines the effect of the planetary boundary layer (PBL) scheme and horizontal diffusion treatment on CAP prediction in the Weather Research and Forecasting (WRF) model. Model runs with a one-dimensional PBL scheme and Smagorinsky-like horizontal diffusion are compared to runs that use a new three-dimensional (3D) PBL scheme to calculate turbulent fluxes. Simulations are completed in a nested configuration with 3 km/750 m horizontal resolution over a 10-day case study in the Columbia River basin, and results are compared to observations from the Second Wind Forecast Improvement Project. Using an event-averaged bias metric, the 3D PBL scheme is shown to reduce positive temperature and wind speed bias compared to a standard one-dimensional PBL approach. The use of increased horizontal grid resolution, as well as WRF’s advanced horizontal diffusion treatment, which accounts for grid skewness over sloping terrain, also generally reduces this bias. Bias reduction is accentuated during CAP erosion, when turbulent mixing plays a more dominant role in the dynamics. Lastly, the 3D PBL scheme is shown to reduce near-surface overestimates of turbulence kinetic energy during the CAP event, and the sensitivity of turbulence predictions to the length scale formulation is explored.