Wind ramps, or rapid changes in wind speed, are a crucial aspect of atmospheric dynamics and
have significant implications for various wind energy applications. For example, wind ramps tend
to increase uncertainty in power output predictions. Furthermore, they also induce fatigue damage
to wind turbines.

In a recent study, DeMarco and Basu (2018; Wind Energy) used long-term observational
data from four geographical locations to characterize the tails of the wind ramp probability
distribution functions (pdfs). They showed that the pdfs from these various sites (ranging from
offshore to complex terrain) portray quasi-universal behavior. The tails of the pdfs are much
heavier than the Gaussian pdf and decay faster with increasing time increments. The tail-index
statistics, computed via the so-called Hill plots, exhibited minimal height dependency up to
approximately one hundred meters above the land or sea surface level. However, wind ramp
statistics at higher altitudes at Cabauw (the Netherlands) were quite distinct.

In the present study, we investigate if state-of-the-art reanalysis datasets capture the
intrinsic traits of wind ramp pdfs. Specifically, we make use of the newly released Copernicus
European Regional ReAnalysis (CERRA) dataset in conjunction with the popular fifth-generation
ECMWF reanalysis (ERA5) dataset. These datasets allow us to describe the characteristics of wind
ramp pdfs at high altitudes (up to 500 m). Given the disparity of the spatial resolution of CERRA
(~5.5 km) and ERA5 (~32 km) datasets, we are also able to demonstrate the impact of spatial
resolution on simulated tail index characteristics. Lastly, the influence of natural climate patterns
such as El-Nino and La-Nina on wind ramp pdfs are examined.