Abstract
Abstract. A number of numerical wind flow models have been developed for simulating wind flow at relatively fine spatial resolutions (e.g., ~ 100 m); however, there are very limited observational data available for evaluating these high-resolution models. This study presents high-resolution surface wind data sets collected from an isolated mountain and a steep river canyon. The wind data are presented in terms of four flow regimes: upslope, afternoon, downslope, and a synoptically driven regime. There were notable differences in the data collected from the two terrain types. For example, wind speeds on the isolated mountain increased with distance upslope during upslope flow, but generally decreased with distance upslope at the river canyon site during upslope flow. In a downslope flow, wind speed did not have a consistent trend with position on the isolated mountain, but generally increased with distance upslope at the river canyon site. The highest measured speeds occurred during the passage of frontal systems on the isolated mountain. Mountaintop winds were often twice as high as wind speeds measured on the surrounding plain. The highest speeds measured in the river canyon occurred during late morning hours and were from easterly down-canyon flows, presumably associated with surface pressure gradients induced by formation of a regional thermal trough to the west and high pressure to the east. Under periods of weak synoptic forcing, surface winds tended to be decoupled from large-scale flows, and under periods of strong synoptic forcing, variability in surface winds was sufficiently large due to terrain-induced mechanical effects (speed-up over ridges and decreased speeds on leeward sides of terrain obstacles) that a large-scale mean flow would not be representative of surface winds at most locations on or within the terrain feature. These findings suggest that traditional operational weather model (i.e., with numerical grid resolutions of around 4 km or larger) wind predictions are not likely to be good predictors of local near-surface winds on sub-grid scales in complex terrain. Measurement data can be found at http://www.firemodels.org/index.php/windninja-introduction/windninja-publications.
Highlights
IntroductionPredictions of terrain-driven winds are important in regions with complex topography for a number of issues, including wildland fire behavior and spread (Sharples et al, 2012; Simpson et al, 2013), transport and dispersion of pollutants (Jiménez et al, 2006; Grell et al, 2000), simulation of convection-driven processes (Banta, 1984; Langhans et al, 2013), wind resource assessment for applications such as wind turbine siting (Chrust et al, 2013; Palma et al, 2008), wind forecasting (Forthofer et al, 2014), and climate change impacts (Daly et al, 2010)
1. a downslope regime, which included downslope and downvalley flows, forced by nighttime surface cooling under weak synoptic forcing; 2. an upslope regime, which included upslope and upvalley flows, forced by daytime surface heating under weak synoptic forcing; 3. an afternoon regime, during which local flows were influenced by larger-scale flows, either through convective mixing or through formation of upvalley drainage winds under weak synoptic forcing; and
We have presented an analysis of two high-resolution surface wind data sets, one collected from a tall isolated mountain, and the other from a steep river canyon
Summary
Predictions of terrain-driven winds are important in regions with complex topography for a number of issues, including wildland fire behavior and spread (Sharples et al, 2012; Simpson et al, 2013), transport and dispersion of pollutants (Jiménez et al, 2006; Grell et al, 2000), simulation of convection-driven processes (Banta, 1984; Langhans et al, 2013), wind resource assessment for applications such as wind turbine siting (Chrust et al, 2013; Palma et al, 2008), wind forecasting (Forthofer et al, 2014), and climate change impacts (Daly et al, 2010). Numerous efforts have focused on improving boundary-layer flow predictions from numerical weather prediction (NWP) models by either reducing the horizontal grid size in order to resolve the effects of finer-scale topographical features on atmospheric flow (Lundquist et al, 2010; Zhong and Fast, 2003) or adding. Butler et al.: High-resolution observations of the near-surface wind field new parameterizations to account for unresolved terrain features (Jiménez and Dudhia, 2012). Because NWP simulations are computationally demanding and suffer from inherent limitations of terrain-following coordinate systems in steep terrain (Lundquist et al, 2010), a number of high-resolution diagnostic wind models have been developed to downscale wind predictions from NWP models in order to meet the needs of the aforementioned applications (e.g., Beaucage et al, 2012). There are limited observational data available to evaluate and improve such high-resolution models
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