Global patterns of offshore wind variability

Abstract

Using the 40‐year hourly gridded ERA5 reanalysis, we study the offshore patterns of wind variability using the probability density function (PDF) and the power spectral density (PSD). To summarize wind variability, we compute the Weibull parameters from the PDF and the PSD for six spectral bands: interannual, annual, multimonth, storm, diurnal, and semidiurnal. We characterize the storm spectral peak using a Gaussian function in normallog10 frequency space. These parameters are plotted along two pole‐to‐pole transects through the Atlantic Ocean at 25°W and the Pacific Ocean at 170°W longitude and at 16 current and proposed offshore wind sites. We test the idea that coastal and open‐ocean wind statistics may be described by a single set of meridional profiles. In mid‐latitudes, the storm band variance exceeding 20 m2 s−2 dominates. The Weibull shape parameter is close to k=2. The storm interval varies from 4 to 7 days depending on location. In the Northern Hemisphere only, the annual variance is significant, exceeding 3 m2 s−2. In the equatorial region, we find steadier winds with small storm variance less than 3 m2 s−2. The Weibull shape parameter is k=4 or greater, suggesting the possibility of a high capacity factor there. The storm interval can exceed 15 days. The equatorial zone has an annual wind variance reaching 2 m2 s−2, comparable to the storm variance. In the tropical Pacific, we find interannual variance associated with El Niño‐Southern Oscillation (ENSO). Semidiurnal variance is detected in the tropics from the atmospheric tide.

Can synoptic drivers explain rotor‐area wind conditions and predict energy output?

“…Studies of seasonal‐to‐interannual variability in hub‐height wind speed (and thus wind energy) have demonstrated its strong linkage with midlatitude synoptic‐scale weather (i.e., high‐ and low‐pressure systems). For example, Millstein et al 1 identified synoptic‐scale patterns corresponding to periods of above‐ and below‐normal wind speed in central and southern California, USA; Coburn 2 showed that synoptic‐scale weather patterns have a significant impact on total energy output at wind energy sites in a midcontinental region of the USA, and West and Smith 3 found that synoptic‐scale variability was the dominant driver of offshore wind speed variability in the midlatitudes.…”

Offshore wind power around the Iberian Peninsula: variability, complementarity and added value for the power system

“…Among other aspects, seasonal variations are particularly important in this case, as the seasonal demand shows two maxima in winter and summer, and summer demand is expected to increase in the future due to the growing frequency of heat waves [20]. Previous studies have analysed temporal variability of the wind resource at different time scales [15,[21][22][23]. Spatial complementarity among the different installation sites can be used to smooth out the temporal variability of the combined power supply [24][25][26][27][28][29][30][31][32].…”

On the Indirect Estimation of Wind Wave Heights over the Southern Coasts of Caspian Sea: A Comparative Analysis

“…Two numerical methods can be applied for these purposes; in the first method, defined as the shore protection manual (SPM) method, both significant Water 2022, 14, 843 2 of 13 wave height H m 0 (m) and peak wave period T p (s) can be obtained by employing wind data [12][13][14][15][16][17][18]. The second method is the so-called spectral method [19,20]. When wave peak period and height are needed, these parameters are estimated from the so-called wave spectrum, which is obtained from the SPM method.…”