Using Observed and Modelled Heat Fluxes for Improved Extrapolation of Wind Distributions

Abstract

Modelling the horizontal and vertical variation of wind speed is crucial for wind energy applications. A model frequently used for this purpose is part of the Wind Atlas Analysis and Application program (WAsP). Here, we modify the model in WAsP to account for local atmospheric stability parameters. Atmospheric stability effects are treated by using the impact of a temperature scale on the geostrophic drag law and the diabatic logarithmic wind profile. Using this approach, wind-direction dependent mean and standard deviation of a surface-layer temperature scale and a mean boundary-layer height scale can be obtained from either numerical weather prediction model output or observations to improve vertical extrapolations of Weibull wind speed distribution parameters. The modified atmospheric stability model is validated at six flat sites in northwestern Europe. The surface-layer temperature scale is available from sonic anemometer measurements at three of the sites. At all sites the temperature scale is also estimated from reanalysis data and from mesoscale model output. The modified model improves the aggregated estimations of power density distributions when extrapolating to nearby locations from 5.2 to 3%, when using the temperature scale derived from either observations or mesoscale/reanalysis output compared to the current model.

FLOW-Alaiz benchmark for coupled terrain and array interaction flow models. Baseline Results

“…PyWAsP [10] is the Python API to the Wind Atlas Analysis and Applications Program (WAsP) [11]. WAsP consists of linearized flow models for orographic speed-ups [12], internal boundarylayer effects [13], geostrophic drag-law [11], and stability [14].…”

“…The wind climate is corrected according to the local flow effects at the target locations of the extrapolation. For both generalization and application, the ERA5-based stability parameters were used according to [14]. WAsP can include the effects of mean geostrophic shear at any location based on ERA5 data [15].…”

Investigating Wind Characteristics and Temporal Variations in the Lower Troposphere over the Northeastern Qinghai–Tibet Plateau Using a Doppler LiDAR

“…Observing and studying wind fields in the lower troposphere are crucial for understanding the intricate land-atmosphere physical processes and improving the accuracy of numerical models in simulating and forecasting the climate, the weather, and air pollution [1][2][3]. Additionally, they play an essential role in bridge design, aviation flight safety support, and wind power utilization [4][5][6].…”