Temporal and Spatial Variability of Wind Resources in the United States as Derived from the Climate Forecast System Reanalysis

Abstract

This study examines the spatial and temporal variability of wind speed at 80 m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the winter (summer), and higher (lower) speeds over much of the Midwest and U.S. Northeast (U.S. West and Southeast). Trends are also variable spatially, with more upward trends in areas of the Great Plains and Intermountain West of the United States and more downward trends elsewhere. The leading EOF mode, which accounts for 20% (summer) to 33% (winter) of the total variance and represents in-phase variations across the United States, responds mainly to the North Atlantic Oscillation (NAO) in summer and El Niño-Southern Oscillation (ENSO) in the other seasons. The dominant variation pattern can be explained by a southerly/southwesterly (westerly) anomaly over the U.S. East (U.S. West) as a result of the anomalous mean sea level pressure (MSLP) pattern. The second EOF mode, which explains about 15% of the total variance and shows a seesaw pattern, is mainly related to the springtime Arctic Oscillation (AO), the summertime recurrent circumglobal teleconnection (CGT), the autumn Pacific decadal oscillation (PDO), and the winter El Niño Modoki. The anomalous jet stream and MSLP patterns associated with these indices are responsible for the wind variation.

Solar Radiation Models and Gridded Databases to Fill Gaps in Weather Series and to Project Climate Change in Brazil

“…Similarly, the poor performance of all databases to estimate WS 2m is related to two main aspects: the small magnitude of this variable, which leads to large errors even with small deviations, and its high spatial variability associated with the topography and land cover [60]. Finally, the median to bad AgCFSR, AgMERRA, and NASA/POWER performance to estimate RH is related to the fact that the former two provide RH at the time of maximum daily temperature, which is not the daily average, which resulted in MAE between 14 and 17% in the assessed regions.…”

The future of wind energy in California: Future projections with the Variable-Resolution CESM

“…The few studies that have examined the 25 impact of climate change on wind resources over California using global and/or regional climate models [7] have been largely inconclusive. These prior stud-2 ies have shown sensitivity to model setup, including choice of physics scheme, downscaling method, and number of models used [8,9,10,11,12,13]. Furthermore, the spatial variability of wind energy resources and its sensitivity to 30 model settings emphasizes the benefit of higher resolution models and multiple model inter-comparisons [7].…”

Elevation‐dependent reductions in wind speed over and around the Tibetan Plateau

“…Numerous studies have examined the trends of W s over different regions including North America (Klink, 1999;Turner et al, 2005;Yu et al, 2015), Europe (Pirazzoli and Tomasin, 2003), Austria (Roderick et al, 2007;Mcvicar et al, 2008), China (Xu et al, 2006a(Xu et al, , 2006bLin et al, 2013), and the Tibetan Plateau (TP) (Chen et al, 2006;Zhang et al, 2007). Widespread reductions in near-surface W s ranging from 0.04 m s −1 decade −1 to 0.17 m s −1 decade −1 have been observed for mid-latitude regions over the last several decades, whereas an increase of about 0.05 m s −1 decade −1 has been reported in high-latitude regions such as Antarctica (Aristidi et al, 2005;Turner et al, 2005) and Alaska (Lynch et al, 2004).…”