Wind observations from a forested hill: Relating turbulence statistics to surface characteristics in hilly and patchy terrain

Pauscher, Lukas; Callies, Doron; Klaas, Tobias; Foken, Thomas

doi:10.1127/metz/2017/0863

Cited by 10 publications

(4 citation statements)

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“…For this reason, both turbulence and standard deviation of the horizontal wind speed provide classification variables. It is noted that vertical profiling wind lidars are known to not measure the standard deviation of wind speed accurately when compared to cup or sonic measurements [25,26]. In this study, however, the standard deviation of the wind speed is only used as a classification variable and, while present, these errors are expected to be of minor importance.…”

Section: Selection Of Classification Variables and Classification Proceduresmentioning

confidence: 90%

Improving Vertical Wind Speed Extrapolation Using Short-Term Lidar Measurements

2020

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This study investigates how short-term lidar measurements can be used in combination with a mast measurement to improve vertical extrapolation of wind speed. Several methods are developed and analyzed for their performance in estimating the mean wind speed, the wind speed distribution, and the energy yield of an idealized wind turbine at the target height of the extrapolation. These methods range from directly using the wind shear of the short-term measurement to a classification approach based on commonly available environmental parameters using linear regression. The extrapolation strategies are assessed using data of ten wind profiles up to 200 m measured at different sites in Germany. Different mast heights and extrapolation distances are investigated. The results show that, using an appropriate extrapolation strategy, even a very short-term lidar measurement can significantly reduce the uncertainty in the vertical extrapolation of wind speed. This observation was made for short as well as for very large extrapolation distances. Among the investigated methods, the linear regression approach yielded better results than the other methods. Integrating environmental variables into the extrapolation procedure further increased the performance of the linear regression approach. Overall, the extrapolation error in (theoretical) energy yield was decreased by around 50% to 70% on average for a lidar measurement of approximately one to two months depending on the extrapolation height and distance. The analysis of seasonal patterns revealed that appropriate extrapolation strategies can also significantly reduce the seasonal bias that is connected to the season during which the short-term measurement is performed.

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Section: Selection Of Classification Variables and Classification Proceduresmentioning

confidence: 90%

Improving Vertical Wind Speed Extrapolation Using Short-Term Lidar Measurements

2020

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“…Table 1 presents an overview of the measurement campaigns used in this study. All sites are located in Germany; the complexity of the sites ranges from flat agricultural areas to the hilly low mountain ranges in Central Germany (one of the complex sites is described in Pauscher et al, 2018). For all sites a time series of an entire year for a height level between 100 and 140 m is available, representing typical hub heights of modern wind turbines.…”

Section: Measurement and Reanalysis Data Used In This Studymentioning

confidence: 99%

Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data

et al. 2021

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Abstract. Measure–correlate–predict (MCP) approaches are often used to correct wind measurements to the long-term wind conditions on-site. This paper investigates systematic errors in MCP-based long-term corrections which occur if the measurement on-site covers only a few months (seasonal biases). In this context, two common linear MCP methods are tested and compared with regard to accuracy in mean, variance, and turbine energy production – namely, variance ratio (VR) and linear regression with residuals (LR). Wind measurement data from 18 sites with different terrain complexity in Germany are used (measurement heights between 100 and 140 m). Six different reanalysis data sets serve as the reference (long-term) wind data in the MCP calculations. All these reanalysis data sets showed an overpronounced annual course of wind speed (i.e., wind speeds too high in winter and too low in summer). However, despite the mathematical similarity of the two MCP methods, these errors in the data resulted in very different seasonal biases when either the VR or LR methods were used for the MCP calculations. In general, the VR method produced overestimations of the mean wind speed when measuring in summer and underestimations in the case of winter measurements. The LR method, in contrast, predominantly led to opposite results. An analysis of the bias in variance did not show such a clear seasonal variation. Overall, the variance error plays only a minor role for the accuracy in energy compared to the error in mean wind speed. Besides the experimental analysis, a theoretical framework is presented which explains these phenomena. This framework enables us to trace the seasonal biases to the mechanics of the methods and the properties of the reanalysis data sets. In summary, three aspects are identified as the main influential factors for the seasonal biases in mean wind speed: (1) the (dis-)similarity of the real wind conditions on-site in correlation and correction period (representativeness of the measurement period), (2) the capability of the reference data to reproduce the seasonal course of wind speed, and (3) the regression parameter β1 (slope) of the linear MCP method. This theoretical framework can also be considered valid for different measurement durations, other reference data sets, and other regions of the world.

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“…One of those locations is the area centered at Rödeser Berg, near Wolfhagen, 25 km west of Kassel, Germany (51.36 • N, 9.18 • E [19]). The characteristics of the turbulence measurements taken at a tall tower at the top of Rödeser Berg have been analyzed in [20], where they had to consider the effects of terrain variability in the upwind direction.…”

Section: The Measuring Sitementioning

confidence: 99%

Spatial Variability of the Lower Atmospheric Boundary Layer over Hilly Terrain as Observed with an RPAS

et al. 2019

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The operation of a Remotely Piloted Aircraft System (RPAS) over a hilly area in northern Germany allows inspection of the variability of the profiles of temperature, humidity, and wind speed next to a small hill. Four cases in nearly stationary conditions are analyzed. Two events are windy, one overcast and the other with clear skies, whereas the two other cases have weak winds, one overcast, and one with clear skies and dissipating mist. The profiles are made at five locations surrounding the hill, separated by a distance from each other of 5 km at most, sampling up to 130 m above the ground. The average profiles and their standard deviations indicate that the variability in the windy cases is approximately constant with height, likely linked to the turbulent flow itself, whereas, for the weak wind cases, the variability diminishes with height, and it is probably linked to the surface variability. The variability between soundings is large. The computation of the root mean square error with respect to the average of the soundings for each case shows that the site closest to the average is the one over open terrain and low vegetation, whereas the site in the forest is the farthest from average. Comparison with the profiles to the nearest grid point of the European Centre for Medium-Range Weather Forecasts (ECMWF) model shows that the closest values are provided by the average of the soundings and by the site closest to the average. Despite the small dataset collected during this exercise, the methodology developed here can be used for more cases and locations with the aim to characterize better the local variability in the lower atmosphere. In this sense, a non-dimensional heterogeneity index is proposed to quantify the topographically and thermally induced variability in complex terrain.

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Wind observations from a forested hill: Relating turbulence statistics to surface characteristics in hilly and patchy terrain

Cited by 10 publications

References 0 publications

Improving Vertical Wind Speed Extrapolation Using Short-Term Lidar Measurements

Improving Vertical Wind Speed Extrapolation Using Short-Term Lidar Measurements

Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data

Spatial Variability of the Lower Atmospheric Boundary Layer over Hilly Terrain as Observed with an RPAS

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