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

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, flo...

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Bibliographic Details
Published inAtmospheric chemistry and physics Vol. 16; no. 8; pp. 5229 - 5241
Main Authors Wagenbrenner, Natalie S, Forthofer, Jason M, Lamb, Brian K, Shannon, Kyle S, Butler, Bret W
Format Journal Article
LanguageEnglish
Published Katlenburg-Lindau Copernicus GmbH 27.04.2016
Copernicus Publications
Subjects
Atmospheric models
Atmospheric stability
Channeling
Comparative analysis
Diagnostic systems
Evaluation
Fires
Flow (Dynamics)
Flow separation
Flow stability
Heating
Heating and cooling
High resolution
Landscape
Meteorological satellites
Mountains
Numerical weather forecasting
Numerical weather prediction
Prediction models
Researchers
Resolution
Ridges
Ruggedness
Simulation
Spatial discrimination
Spatial resolution
Surface wind
Terrain
Valleys
Weather
Weather forecasting
Wildfires
Wind
Wind direction
Wind effects
Wind speed
Winds
United States > US
Pacific Northwest
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Summary: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.
ISSN:1680-7324
1680-7316
1680-7324
DOI:10.5194/acp-16-5229-2016