Articles | Volume 16, issue 8
Atmos. Chem. Phys., 16, 5229–5241, 2016
Atmos. Chem. Phys., 16, 5229–5241, 2016

Research article 27 Apr 2016

Research article | 27 Apr 2016

Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja

Natalie S. Wagenbrenner1, Jason M. Forthofer1, Brian K. Lamb2, Kyle S. Shannon1, and Bret W. Butler1 Natalie S. Wagenbrenner et al.
  • 1US Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory, 5775 W Highway 10, Missoula, MT 59808, USA
  • 2Laboratory for Atmospheric Research, Department of Civil and Environmental Engineering, Washington State University, Pullman, WA 99164, USA

Abstract. Wind predictions in complex terrain are important for a number of applications. Dynamic downscaling of numerical weather prediction (NWP) model winds with a high-resolution wind model is one way to obtain a wind forecast that accounts for local terrain effects, such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and flows induced by local surface heating and cooling. In this paper we investigate the ability of a mass-consistent wind model for downscaling near-surface wind predictions from four NWP models in complex terrain. Model predictions are compared with surface observations from a tall, isolated mountain. Downscaling improved near-surface wind forecasts under high-wind (near-neutral atmospheric stability) conditions. Results were mixed during upslope and downslope (non-neutral atmospheric stability) flow periods, although wind direction predictions generally improved with downscaling. This work constitutes evaluation of a diagnostic wind model at unprecedented high spatial resolution in terrain with topographical ruggedness approaching that of typical landscapes in the western US susceptible to wildland fire.

Short summary
We investigated the ability of WindNinja to improve wind predictions in complex terrain. Predictions are compared with surface observations from a tall, isolated mountain. Results show that WindNinja is capable of capturing important local-scale flow features induced by mechanical and thermal effects of the underlying terrain and incorporating those terrain-driven flow features into coarse-scale weather forecasts in order to improve near-surface wind predictions in complex terrain.
Final-revised paper