Importance of location for describing typical and extreme wind speed behavior

Abstract

[1] Several recent studies have considered the potential impact of climate change on regional wind intensity. However, previous wind speed studies in the Pacific Northwest (PNW) present conflicting results for wind speed trends in relation to climate drivers. This study analyzes the percentiles (50th, 75th, and 95th) of the strongly positively skewed distributions for PNW maximum daily wind speeds from 92 meteorological stations, and reveals different behaviors for average and extreme wind speeds. Considerably stronger winds are found at coastal locations compared with sites further inland. Extreme wind speeds at these coastal locations appear to follow an eight to nine-year cyclic pattern, while mainland sites have a small, linear downward wind speed trend. This finding of a behavioral dependence on location helps reconcile previous, apparently contradictory results and has important global implications for wind research and infrastructure planning, such as wind energy feasibility studies and air quality management activities. Citation: Griffin, B. J., K. E. Kohfeld, A. B. Cooper, and G. Boenisch (2010), Importance of location for describing typical and extreme wind speed behavior, Geophys.

Recent trends in wind speed across Saudi Arabia, 1978–2013: a break in the stilling

“…Previous studies also detected a more statistically significant atmospheric stilling over coastal stations (e.g. Griffin et al, ; Minola et al, ).…”

Trends in Wind Speed at Wind Turbine Height of 80 m over the Contiguous United States Using the North American Regional Reanalysis (NARR)

“…In addition to the negative trends found in the ground-based observation record, Pryor et al (2009) also showed the dominance of positive trends in the National Centers for Environmental PredictionNational Center for Atmospheric Research (NCEP-NCAR) reanalysis and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) ReAnalysis (ERA-40) for the 50th-percentile and 90th-percentile annual mean wind speed and further found that wind speed trends at 0000 and 1200 UTC from the North American Regional Reanalysis (NARR) are opposite in sign over the west. Overall, past studies support the synthesis by Pryor et al (2009) that the surface wind speeds over the continental United States exhibit different and sometimes opposite trends depending on the dataset used, the time period examined, and the region of interest (Pryor et al 2007;McVicar et al 2008;Pryor et al 2009;Li et al 2010;Griffin et al 2010).…”

“…Only the linear trends at the 90% significance level or higher are considered to be statistically significant [similar to what what is used in Pryor et al (2009)], using a two-tailed t test for each grid point. OLS is widely used in the analysis of wind trend (McVicar et al 2008;Pryor et al 2009;Li et al 2010), but Pryor and Ledolter (2010) and Griffin et al (2010) recently noted that significant temporal autocorrelation of wind may occur in a time span of 30 years, depending on the geographical location, and must be filtered out of the time series before applying OLS; otherwise, the temporal autocorrelation may exaggerate the statistical significance of the trend.…”

“…Over the United States, Pryor et al (2009) ; Table 1) and hence are unlikely to interrupt the sustainability of wind energy resources, at least during the life span of a typical wind power plant (presumably 20-30 years) (Pryor and Barthelmie 2011). It is still important to assess the distribution of the trend in wind speed at the continental to global scale, however, to better understand the causes of the trend as well as to develop wind farms strategically and economically (Pryor et al 2009;Griffin et al 2010). This is especially true in the continental United States, where wind farms (triangles in Fig.…”

Wind speed variability over the Canary Islands, 1948–2014: focusing on trend differences at the land–ocean interface and below–above the trade-wind inversion layer

“…Statistically significant trends were defined as those where p < 0.05 (in bold and in parenthesis) and p < 0.10 (in bold) 1948-2014 1989-2014 1948-2014 1989-2014 1948-2014 1989-2014 features (e.g., surface roughness; Wever 2012) against prevailing wind flows (Griffin et al 2010). Therefore, even though the role of surface roughness (Vautard et al 2010) on the global decline of wind speed over land when compared to the ocean surfaces has not been observed in this study, both simulations and observations revealed that local features play a key role in the reported trends (confirming results of Vautard et al 2010;Griffin et al 2010;Wever 2012). For example, high-spatial resolution hindcast products, particularly those downscaled from SeaWind II at 15-km horizontal grid-spacing, demonstrated the orographic effects of the Canary Islands on small-scale atmospheric processes in ocean winds (Chelton et al 2004).…”