Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 3.400 IF 3.400
  • IF 5-year value: 3.841 IF 5-year
    3.841
  • CiteScore value: 3.71 CiteScore
    3.71
  • SNIP value: 1.472 SNIP 1.472
  • IPP value: 3.57 IPP 3.57
  • SJR value: 1.770 SJR 1.770
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 70 Scimago H
    index 70
  • h5-index value: 49 h5-index 49
Volume 10, issue 8
Atmos. Meas. Tech., 10, 2881–2896, 2017
https://doi.org/10.5194/amt-10-2881-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 10, 2881–2896, 2017
https://doi.org/10.5194/amt-10-2881-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 14 Aug 2017

Research article | 14 Aug 2017

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

Nicola Bodini et al.
Related authors  
Estimation of turbulence parameters from scanning lidars and in-situ instrumentation in the Perdigão 2017 campaign
Norman Wildmann, Nicola Bodini, Julie K. Lundquist, Ludovic Bariteau, and Johannes Wagner
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-171,https://doi.org/10.5194/amt-2019-171, 2019
Manuscript under review for AMT
Spatial and temporal variability of turbulence dissipation rate in complex terrain
Nicola Bodini, Julie K. Lundquist, Raghavendra Krishnamurthy, Mikhail Pekour, Larry K. Berg, and Aditya Choukulkar
Atmos. Chem. Phys., 19, 4367–4382, https://doi.org/10.5194/acp-19-4367-2019,https://doi.org/10.5194/acp-19-4367-2019, 2019
Short summary
Estimation of turbulence dissipation rate and its variability from sonic anemometer and wind Doppler lidar during the XPIA field campaign
Nicola Bodini, Julie K. Lundquist, and Rob K. Newsom
Atmos. Meas. Tech., 11, 4291–4308, https://doi.org/10.5194/amt-11-4291-2018,https://doi.org/10.5194/amt-11-4291-2018, 2018
Short summary
Year-to-year correlation, record length, and overconfidence in wind resource assessment
Nicola Bodini, Julie K. Lundquist, Dino Zardi, and Mark Handschy
Wind Energ. Sci., 1, 115–128, https://doi.org/10.5194/wes-1-115-2016,https://doi.org/10.5194/wes-1-115-2016, 2016
Short summary
Related subject area  
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Tropopause altitude determination from temperature profile measurements of reduced vertical resolution
Nils König, Peter Braesicke, and Thomas von Clarmann
Atmos. Meas. Tech., 12, 4113–4129, https://doi.org/10.5194/amt-12-4113-2019,https://doi.org/10.5194/amt-12-4113-2019, 2019
Short summary
A generalized simulation capability for rotating- beam scatterometers
Zhen Li, Ad Stoffelen, and Anton Verhoef
Atmos. Meas. Tech., 12, 3573–3594, https://doi.org/10.5194/amt-12-3573-2019,https://doi.org/10.5194/amt-12-3573-2019, 2019
Short summary
Automated wind turbine wake characterization in complex terrain
Rebecca J. Barthelmie and Sara C. Pryor
Atmos. Meas. Tech., 12, 3463–3484, https://doi.org/10.5194/amt-12-3463-2019,https://doi.org/10.5194/amt-12-3463-2019, 2019
Short summary
Polarimetric radar characteristics of lightning initiation and propagating channels
Jordi Figueras i Ventura, Nicolau Pineda, Nikola Besic, Jacopo Grazioli, Alessandro Hering, Oscar A. van der Velde, David Romero, Antonio Sunjerga, Amirhossein Mostajabi, Mohammad Azadifar, Marcos Rubinstein, Joan Montanyà, Urs Germann, and Farhad Rachidi
Atmos. Meas. Tech., 12, 2881–2911, https://doi.org/10.5194/amt-12-2881-2019,https://doi.org/10.5194/amt-12-2881-2019, 2019
Short summary
Processing and quality control of FY-3C GNOS data used in numerical weather prediction applications
Mi Liao, Sean Healy, and Peng Zhang
Atmos. Meas. Tech., 12, 2679–2692, https://doi.org/10.5194/amt-12-2679-2019,https://doi.org/10.5194/amt-12-2679-2019, 2019
Short summary
Cited articles  
Abkar, M. and Porté-Agel, F.: Influence of atmospheric stability on wind-turbine wakes: A large-eddy simulation study, Phys. Fluids, 27, 035104, https://doi.org/10.1088/1742-6596/524/1/012138, 2015.
Abkar, M., Sharifi, A., and Porté-Agel, F.: Large-eddy simulation of the diurnal variation of wake flows in a finite-size wind farm, J. Phys. Conf. Ser., 625, 012031, https://doi.org/10.1088/1742-6596/625/1/012031, 2015.
Abkar, M., Sharifi, A., and Porté-Agel, F.: Wake flow in a wind farm during a diurnal cycle, J. Turbul., 17, 420–441, https://doi.org/10.1080/14685248.2015.1127379, 2016.
Aitken, M. L. and Lundquist, J. K.: Utility-scale wind turbine wake characterization using nacelle-based long-range scanning lidar, J. Atmos. Ocean. Tech., 31, 1529–1539, https://doi.org/10.1175/JTECH-D-13-00218.1, 2014.
Aitken, M. L., Banta, R. M., Pichugina, Y. L., and Lundquist, J. K.: Quantifying wind turbine wake characteristics from scanning remote sensor data, J. Atmos. Ocean. Tech., 31, 765–787, https://doi.org/10.1175/JTECH-D-13-00104.1, 2014a.
Publications Copernicus
Download
Short summary
Wind turbine wakes have considerable impacts on downwind turbines in wind farms, given their slower wind speeds and increased turbulence. Based on lidar measurements, we apply a quantitative algorithm to assess wake parameters for wakes from a row of four turbines in CWEX-13 campaign. We describe how wake characteristics evolve, and for the first time we quantify the relation between wind veer and a stretching of the wake structures, and we highlight different results for inner and outer wakes.
Wind turbine wakes have considerable impacts on downwind turbines in wind farms, given their...
Citation