Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja
Item
Title (Dublin Core)
Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja
Description (Dublin Core)
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.
Creator (Dublin Core)
Wagenbrenner, Natalie S.
Forthofer, Jason M.
Lamb, Brian K.
Shannon, Kyle S.
Butler, Bret W.
Date (Dublin Core)
2018-09-20
Type (Dublin Core)
Text
Format (Dublin Core)
application/pdf
Identifier (Dublin Core)
10.5194/acp-16-5229-2016
https://acp.copernicus.org/articles/16/5229/2016/
Source (Dublin Core)
eISSN: 1680-7324
Language (Dublin Core)
eng
