Abstract
Winds, particularly high winds, strongly affect snowmelt and snow redistribution. High winds during rain-on-snow events can lead to catastrophic flooding while strong redistribution events in mountain environments can generate dangerous avalanche conditions. To provide adequate warnings, accurate wind data are required. Yet, mountain wind fields exhibit a high degree of heterogeneity at small spatial lengths that are not resolved by currently available gridded forecast data. Wind data from over 200 stations across Switzerland were used to evaluate two forecast surface wind products (~2- and 7-km horizontal resolution) and develop a statistical downscaling technique to capture these finer-scaled heterogeneities. Wind exposure metrics derived from a 25-m horizontal resolution digital elevation model effectively segregated high, moderate, and low wind speed sites. Forecast performance was markedly compromised and biased low at the exposed sites and biased high at the sheltered, valley sites. It was also found that the variability of predicted wind speeds at these sites did not accurately represent the observed variability. A novel optimization scheme that accounted for local terrain structure while also nudging the forecasted distributions to better match the observed distributions and variability was developed. The resultant statistical downscaling technique notably decreased biases across a range of elevations and exposures and provided a better match to observed wind speed distributions.
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