|Gurney, Robert - UNIV OF READING|
Submitted to: Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: November 27, 2007
Publication Date: December 8, 2007
Citation: Winstral, A.H., Marks, D.G., Gurney, R. 2007. Simple and Computationally efficient modeling of surface wind speeds over heterogeneous terrain. EOS Trasnactions of the American Geophysical Union, 88(52) Fall Meeting Supplement, Abstract C21-0469. Technical Abstract: In mountain catchments wind frequently is the dominant process controlling snow distribution. The spatial variability of winds over mountain landscapes is considerable producing great spatial variability in mass and energy fluxes. Distributed models capable of capturing the variability of these mass and energy fluxes require time-series of distributed wind data at compatible fine spatial scale. Atmospheric and surface wind flow models in these regions have been limited by our abilities to represent the inherent complexities of the processes being modeled in a computationally efficient manner. Simplified parameterized models, such as those based on terrain and vegetation, though not as explicit as a model of fluid flow, are computationally efficient for operational use, including in real time. Recent work described just such a model that related a measure of topographic exposure to wind speed differences at proximal locations with varied exposures. The current work used a more expansive network of stations in the Reynolds Creek Experimental Watershed in southwestern Idaho, USA to test extension of the previous findings to larger domains. The stations in the study have varying degrees of wind exposure and comprise an area of approximately 125 km2 and an elevation range of 1200 - 2100 masl. Subsets of site data were detrended based on the relationship derived in the prior work to a selected standard exposure to ascertain and model the presence of any elevation-based trends in the hourly observations. Hourly wind speeds at the withheld stations were then predicted based on elevation and topographic exposure at each respective site. It was found that reasonable predictions of wind speed across this heterogeneous landscape capturing both large-scale elevation trends and small-scale topographic variability could be achieved in a computationally efficient manner.