The impact of the diurnal cycle of the atmospheric boundary layer on physical variables relevant for wind energy applications

Englberger, Antonia; Dörnbrack, Andreas

doi:10.5194/acp-2015-995

Cited by 5 publications

(6 citation statements)

References 38 publications

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“…This is different from the case in the atmospheric boundary layer (within the altitudes of a few hundred metres) where turbulence has a prominent diurnal variation reaching a maximum around midday (e.g. Yasuda, 1988;Englberger and Dörnbrack, 2016).…”

Section: Climatology Of Turbulence With Radarmentioning

confidence: 99%

Derivation of turbulent energy dissipation rate with the Middle Atmosphere Alomar Radar System (MAARSY) and radiosondes at Andøya, Norway

Rapp

Schrön

et al. 2016

Ann. Geophys.

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Abstract. We present the derivation of turbulent energy dissipation rate ε from a total of 522 days of observations with the Middle Atmosphere Alomar Radar SYstem (MAARSY) mesosphere-stratosphere-troposphere (MST) radar running tropospheric experiments during the period of 2010-2013 as well as with balloon-borne radiosondes based on a campaign in the summer 2013. Spectral widths are converted to ε after the removal of the broadening effects due to the finite beam width of the radar. With the simultaneous in situ measurements of ε with balloon-borne radiosondes at the MAARSY radar site, we compare the ε values derived from both techniques and reach an encouraging agreement between them. Using all the radar data available, we present a preliminary climatology of atmospheric turbulence in the UTLS (upper troposphere and lower stratosphere) region above the MAARSY site showing a variability of more than 5 orders of magnitude inherent in turbulent energy dissipation rates. The derived ε values reveal a log-normal distribution with a negative skewness, and the ε profiles show an increase with height which is also the case for each individual month. Atmospheric turbulence based on our radar measurements reveals a seasonal variation but no clear diurnal variation in the UTLS region. Comparison of ε with the gradient Richardson number Ri shows that only 1.7 % of all the data with turbulence occur under the condition of Ri < 1 and that the values of ε under the condition of Ri < 1 are significantly larger than those under Ri > 1. Further, there is a roughly negative correlation between ε and Ri that is independent of the scale dependence of Ri. Turbulence under active dynamical conditions (velocity of horizontal wind U > 10 m s −1 ) is significantly stronger than under quiet conditions (U < 10 m s −1 ). Last but not least, the derived ε values are compared with the corresponding vertical shears of background wind velocity showing a linear relation with a corresponding correlation coefficient r = 58 % well above the 99.9 % significance level. This implies that wind shears play an important role in the turbulence generation in the troposphere and lower stratosphere (through the Kelvin-Helmholtz instability).

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Section: Climatology Of Turbulence With Radarmentioning

confidence: 99%

Derivation of turbulent energy dissipation rate with the Middle Atmosphere Alomar Radar System (MAARSY) and radiosondes at Andøya, Norway

Rapp

Schrön

et al. 2016

Ann. Geophys.

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“…The sun's position in the sky appears to have a significant impact on the degree of atmospheric mixing within the atmospheric boundary layer (ABL; Englberger and Dornbrack, 2016). As the morning sun rises to its peak midday position, there is a marked increase in atmospheric mixing, resulting in an unstable ABL and a lower wind gradient.…”

Section: Position Of the Sun And Impact On Wind Gradientmentioning

confidence: 99%

Wind resource prospecting: Predicting winds aloft using surface winds and the position of the sun

Arbez

2020

Wind Engineering

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The sun’s position in the sky appears to strongly influence the vertical wind gradient within the atmospheric boundary layer. This study uses a quantitative research design that combines the position of the sun along with surface wind speeds to estimate the wind gradient. The model applies to uncluttered simple terrain and requires only 10 m wind speed data to extrapolate wind speeds and wind turbine energy production to heights of 60 m or more. The average daytime and nighttime model wind speed errors are 1.7% and 2.4%, respectively. The average daytime and nighttime model turbine energy production errors are 3.8% and 6.1%, respectively. The model offers a practical and low-cost alternative to tall tower systems to assess wind resources, especially for remote sites.

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“…5 concludes our study with a list of lessons learned and next steps for future work. The Perdigão 2017 experiment (Fernando et al, 2018) is part of a series of campaigns associated with the NEWA (New European Wind Atlas) project (Mann et al, 2017) and took place in central Portugal in late spring and early summer 2017. The main goal of the experiment is to understand the flow field over two mountain ridges, which are nearly parallel to each other, with a distance of 1.4 km between them and a height of more than 200 m over the surrounding valley (see Fig.…”

Section: N Wildmann Et Al: Automtically Adjusting Multi-doppler Lidarmentioning

confidence: 99%

“…An extended measurement period with a reduced amount of equipment lasted for a whole year, i.e., from December 2016 until December 2017. A detailed description of the instrumentation and the research goals of all involved institutions is given in Fernando et al (2018). On the southwest ridge of the Perdigão mountains, an Enercon E-82 wind turbine is installed.…”

Section: N Wildmann Et Al: Automtically Adjusting Multi-doppler Lidarmentioning

confidence: 99%

Wind turbine wake measurements with automatically adjusting scanning trajectories in a multi-Doppler lidar setup

2018

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Abstract. In the context of the Perdigão 2017 experiment, the German Aerospace Center (DLR) deployed three long-range scanning Doppler lidars with the dedicated purpose of investigating the wake of a single wind turbine at the experimental site. A novel method was tested for the first time to investigate wake properties with ground-based lidars over a wide range of wind directions. For this method, the three lidars, which were space- and time-synchronized using the WindScanner software, were programmed to measure with crossing beams at individual points up to 10 rotor diameters downstream of the wind turbine. Every half hour, the measurement points were adapted to the current wind direction to obtain a high availability of wake measurements in changing wind conditions. The linearly independent radial velocities where the lidar beams intersect allow the calculation of the wind vector at those points. Two approaches to estimating the prevailing wind direction were tested throughout the campaign. In the first approach, velocity azimuth display (VAD) scans of one of the lidars were used to calculate a 5 min average of wind speed and wind direction every half hour, whereas later in the experiment 5 min averages of sonic anemometer measurements of a meteorological mast close to the wind turbine became available in real time and were used for the scanning adjustment. Results of wind speed deficit measurements are presented for two measurement days with varying northwesterly winds, and it is evaluated how well the lidar beam intersection points match the actual wake location. The new method allowed wake measurements to be obtained over the whole measurement period, whereas a static scanning setup would only have captured short periods of wake occurrences.

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The impact of the diurnal cycle of the atmospheric boundary layer on physical variables relevant for wind energy applications

Cited by 5 publications

References 38 publications

Derivation of turbulent energy dissipation rate with the Middle Atmosphere Alomar Radar System (MAARSY) and radiosondes at Andøya, Norway

Derivation of turbulent energy dissipation rate with the Middle Atmosphere Alomar Radar System (MAARSY) and radiosondes at Andøya, Norway

Wind resource prospecting: Predicting winds aloft using surface winds and the position of the sun

Wind turbine wake measurements with automatically adjusting scanning trajectories in a multi-Doppler lidar setup

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