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
AMT | Articles | Volume 12, issue 3
Atmos. Meas. Tech., 12, 1871-1888, 2019
https://doi.org/10.5194/amt-12-1871-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 12, 1871-1888, 2019
https://doi.org/10.5194/amt-12-1871-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 21 Mar 2019

Research article | 21 Mar 2019

Better turbulence spectra from velocity–azimuth display scanning wind lidar

Felix Kelberlau and Jakob Mann
Related authors  
Europe’s offshore winds assessed from SAR, ASCAT and WRF
Charlotte B. Hasager, Andrea N. Hahmann, Tobias Ahsbahs, Ioanna Karagali, Tija Sile, Merete Badger, and Jakob Mann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-38,https://doi.org/10.5194/wes-2019-38, 2019
Manuscript under review for WES
Short summary
Detection of wakes in the inflow of turbines using nacelle lidars
Dominique P. Held and Jakob Mann
Wind Energ. Sci., 4, 407-420, https://doi.org/10.5194/wes-4-407-2019,https://doi.org/10.5194/wes-4-407-2019, 2019
Short summary
Mitigating Impact of Spatial Variance of Turbulence in Wind Energy Applications
Jonas Kazda and Jakob Mann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-10,https://doi.org/10.5194/wes-2019-10, 2019
Manuscript under review for WES
Short summary
Characterization of flow recirculation zones at the Perdigão site using multi-lidar measurements
Robert Menke, Nikola Vasiljević, Jakob Mann, and Julie K. Lundquist
Atmos. Chem. Phys., 19, 2713-2723, https://doi.org/10.5194/acp-19-2713-2019,https://doi.org/10.5194/acp-19-2713-2019, 2019
Short summary
A method to assess the accuracy of sonic anemometer measurements
Alfredo Peña, Ebba Dellwik, and Jakob Mann
Atmos. Meas. Tech., 12, 237-252, https://doi.org/10.5194/amt-12-237-2019,https://doi.org/10.5194/amt-12-237-2019, 2019
Short summary
Related subject area  
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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
Neural network radiative transfer for imaging spectroscopy
Brian D. Bue, David R. Thompson, Shubhankar Deshpande, Michael Eastwood, Robert O. Green, Vijay Natraj, Terry Mullen, and Mario Parente
Atmos. Meas. Tech., 12, 2567-2578, https://doi.org/10.5194/amt-12-2567-2019,https://doi.org/10.5194/amt-12-2567-2019, 2019
Short summary
Cited articles  
Bardal, L. M. and Sætran, L. R.: Spatial correlation of atmospheric wind at scales relevant for large scale wind turbines, J. Phys. Conf. Ser., 753, 32–33, https://doi.org/10.1088/1742-6596/753/3/032033, 2016. a
Branlard, E., Pedersen, A. T., Mann, J., Angelou, N., Fischer, A., Mikkelsen, T., Harris, M., Slinger, C., and Montes, B. F.: Retrieving wind statistics from average spectrum of continuous-wave lidar, Atmos. Meas. Tech., 6, 1673–1683, https://doi.org/10.5194/amt-6-1673-2013, 2013. a
Browning, K. A. and Wexler, R.: The Determination of Kinematic Properties of a Wind Field Using Doppler Radar, J. Appl. Meteorol.Clim., 7, 105–113, https://doi.org/10.1175/1520-0450(1968)007<0105:TDOKPO>2.0.CO;2, 1968. a
Canadillas, B., Bégué, A., and Neumann, T.: Comparison of turbulence spectra derived from LiDAR and sonic measurements at the offshore platform FINO1, 10th German Wind Energy Conference (DEWEK 2010), 17–18 November 2010, Bremen, Germany, 2010. a
Chougule, A., Mann, J., Kelly, M., and Larsen, G.: Modeling Atmospheric Turbulence via Rapid Distortion Theory: Spectral Tensor of Velocity and Buoyancy, J. Atmos. Sci., 74, 949–974, https://doi.org/10.1175/JAS-D-16-0215.1, 2017. a
Publications Copernicus
Download
Short summary
Lidars are devices that can measure wind velocities remotely from the ground. Their estimates are very accurate in the mean but wind speed fluctuations lead to measurement errors. The presented data processing methods mitigate several of the error causes: first, by making use of knowledge about the mean wind direction and, second, by determining the location of air packages and sensing them in the best moment. Both methods can be applied to existing wind lidars and results are very promising.
Lidars are devices that can measure wind velocities remotely from the ground. Their estimates...
Citation