Articles | Volume 13, issue 8
https://doi.org/10.5194/amt-13-4141-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-4141-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Aug 2020
Research article |
| 04 Aug 2020
| 04 Aug 2020
Towards improved turbulence estimation with Doppler wind lidar velocity-azimuth display (VAD) scans
Deutsches Zentrum für Luft- und Raumfahrt e.V., Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Eileen Päschke
DWD, Meteorologisches Observatorium Lindenberg – Richard-Aßmann-Observatorium, Lindenberg, Germany
Anke Roiger
Deutsches Zentrum für Luft- und Raumfahrt e.V., Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Christian Mallaun
Deutsches Zentrum für Luft- und Raumfahrt e.V., Flugexperimente, Oberpfaffenhofen, Germany
Viewed
Total article views: 4,613 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Feb 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,173 | 1,298 | 142 | 4,613 | 183 | 197 |
- HTML: 3,173
- PDF: 1,298
- XML: 142
- Total: 4,613
- BibTeX: 183
- EndNote: 197
Total article views: 3,846 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 04 Aug 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,758 | 976 | 112 | 3,846 | 153 | 162 |
- HTML: 2,758
- PDF: 976
- XML: 112
- Total: 3,846
- BibTeX: 153
- EndNote: 162
Total article views: 767 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 05 Feb 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 415 | 322 | 30 | 767 | 30 | 35 |
- HTML: 415
- PDF: 322
- XML: 30
- Total: 767
- BibTeX: 30
- EndNote: 35
Viewed (geographical distribution)
Total article views: 4,613 (including HTML, PDF, and XML)
Thereof 4,257 with geography defined
and 356 with unknown origin.
Total article views: 3,846 (including HTML, PDF, and XML)
Thereof 3,597 with geography defined
and 249 with unknown origin.
Total article views: 767 (including HTML, PDF, and XML)
Thereof 660 with geography defined
and 107 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
26 citations as recorded by crossref.
- Applicability Assessment of Coherent Doppler Wind LiDAR for Monitoring during Dusty Weather at the Northern Edge of the Tibetan Plateau M. Song et al. https://doi.org/10.3390/rs14205264
- Analyses of Spatial Correlation and Coherence in ABL Flow with a Fleet of UAS T. Wetz et al. https://doi.org/10.1007/s10546-023-00791-4
- Harmonised boundary layer wind profile dataset from six ground-based Doppler wind lidars in a transect across Paris, France W. Morrison et al. https://doi.org/10.5194/essd-17-6507-2025
- Estimating CH4, CO2 and CO emissions from coal mining and industrial activities in the Upper Silesian Coal Basin using an aircraft-based mass balance approach A. Fiehn et al. https://doi.org/10.5194/acp-20-12675-2020
- Beyond Raw Backscatter: Multiscale Feature Extraction from Elastic Lidar Observations F. Cairo et al. https://doi.org/10.3390/rs18132086
- Quantification of methane emissions in Hamburg using a network of FTIR spectrometers and an inverse modeling approach A. Forstmaier et al. https://doi.org/10.5194/acp-23-6897-2023
- How do convective cold pools influence the atmospheric boundary layer near two wind turbines in northern Germany? J. Thayer et al. https://doi.org/10.5194/wes-10-2237-2025
- Estimating Upper Silesian coal mine methane emissions from airborne in situ observations and dispersion modeling J. Kostinek et al. https://doi.org/10.5194/acp-21-8791-2021
- Quantification of CH4 coal mining emissions in Upper Silesia by passive airborne remote sensing observations with the Methane Airborne MAPper (MAMAP) instrument during the CO2 and Methane (CoMet) campaign S. Krautwurst et al. https://doi.org/10.5194/acp-21-17345-2021
- Data assimilation of generic boundary layer flows for wind turbine applications – an LES study L. Wrba et al. https://doi.org/10.5194/wes-10-2217-2025
- Gust-detection from velocity azimuth display Doppler wind lidar profiling at the WiValdi research park. N. Wildmann et al. https://doi.org/10.1088/1742-6596/2767/9/092076
- Vertical Structure of Air Pollutant Transport Flux as Determined by Ground-Based Remote Sensing Observations in Fen-Wei Plain, China X. Ji et al. https://doi.org/10.3390/rs13183664
- Enhanced resource assessment and atmospheric monitoring of the research wind farm WiValdi N. Wildmann et al. https://doi.org/10.1088/1742-6596/2265/2/022029
- Seasonal Changes in Boundary-Layer Flow Over a Forested Escarpment Measured by an Uncrewed Aircraft System K. zum Berge et al. https://doi.org/10.1007/s10546-022-00743-4
- Atmospheric turbulence and wind turbine wakes measured with Doppler wind lidar at WiValdi J. Menken et al. https://doi.org/10.1051/epjconf/202636215004
- Evaluation of Turbulent Energy Dissipation Rate Estimation from Doppler Lidar: Impact of Techniques and Scanning Strategies S. Baek et al. https://doi.org/10.3390/rs17050939
- Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region M. Tran et al. https://doi.org/10.3390/en13246559
- Determination of the emission rates of CO2 point sources with airborne lidar S. Wolff et al. https://doi.org/10.5194/amt-14-2717-2021
- A Machine Learning-Based Model for Flight Turbulence Identification Using LiDAR Data Z. Zhuang et al. https://doi.org/10.3390/atmos14050797
- Suppression of precipitation bias in wind velocities from continuous-wave Doppler lidars L. Jin et al. https://doi.org/10.5194/amt-16-6007-2023
- The Prediction of Low-Level Jet Using Machine Learning Based on Turbulence Observations and Remote Sensing M. Chen et al. https://doi.org/10.3390/rs18030470
- Detection and quantification of CH4 plumes using the WFM-DOAS retrieval on AVIRIS-NG hyperspectral data J. Borchardt et al. https://doi.org/10.5194/amt-14-1267-2021
- Multivariate Autoregressive State-Space modelling (MARSS) to explain daily CO variability in Campo Grande-MS (2003–2016) F. Pérez et al. https://doi.org/10.1080/2150704X.2026.2696997
- Dual-lidar profilers for measuring atmospheric turbulence M. Thiébaut & N. Luxcey https://doi.org/10.5194/wes-11-825-2026
- Nacelle-based lidar measurements of wind farm turbulence and validation with a fleet of multicopter drones N. Wildmann et al. https://doi.org/10.1088/1742-6596/3224/2/022031
- Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network A. Luther et al. https://doi.org/10.5194/acp-22-5859-2022
26 citations as recorded by crossref.
- Applicability Assessment of Coherent Doppler Wind LiDAR for Monitoring during Dusty Weather at the Northern Edge of the Tibetan Plateau M. Song et al. https://doi.org/10.3390/rs14205264
- Analyses of Spatial Correlation and Coherence in ABL Flow with a Fleet of UAS T. Wetz et al. https://doi.org/10.1007/s10546-023-00791-4
- Harmonised boundary layer wind profile dataset from six ground-based Doppler wind lidars in a transect across Paris, France W. Morrison et al. https://doi.org/10.5194/essd-17-6507-2025
- Estimating CH4, CO2 and CO emissions from coal mining and industrial activities in the Upper Silesian Coal Basin using an aircraft-based mass balance approach A. Fiehn et al. https://doi.org/10.5194/acp-20-12675-2020
- Beyond Raw Backscatter: Multiscale Feature Extraction from Elastic Lidar Observations F. Cairo et al. https://doi.org/10.3390/rs18132086
- Quantification of methane emissions in Hamburg using a network of FTIR spectrometers and an inverse modeling approach A. Forstmaier et al. https://doi.org/10.5194/acp-23-6897-2023
- How do convective cold pools influence the atmospheric boundary layer near two wind turbines in northern Germany? J. Thayer et al. https://doi.org/10.5194/wes-10-2237-2025
- Estimating Upper Silesian coal mine methane emissions from airborne in situ observations and dispersion modeling J. Kostinek et al. https://doi.org/10.5194/acp-21-8791-2021
- Quantification of CH4 coal mining emissions in Upper Silesia by passive airborne remote sensing observations with the Methane Airborne MAPper (MAMAP) instrument during the CO2 and Methane (CoMet) campaign S. Krautwurst et al. https://doi.org/10.5194/acp-21-17345-2021
- Data assimilation of generic boundary layer flows for wind turbine applications – an LES study L. Wrba et al. https://doi.org/10.5194/wes-10-2217-2025
- Gust-detection from velocity azimuth display Doppler wind lidar profiling at the WiValdi research park. N. Wildmann et al. https://doi.org/10.1088/1742-6596/2767/9/092076
- Vertical Structure of Air Pollutant Transport Flux as Determined by Ground-Based Remote Sensing Observations in Fen-Wei Plain, China X. Ji et al. https://doi.org/10.3390/rs13183664
- Enhanced resource assessment and atmospheric monitoring of the research wind farm WiValdi N. Wildmann et al. https://doi.org/10.1088/1742-6596/2265/2/022029
- Seasonal Changes in Boundary-Layer Flow Over a Forested Escarpment Measured by an Uncrewed Aircraft System K. zum Berge et al. https://doi.org/10.1007/s10546-022-00743-4
- Atmospheric turbulence and wind turbine wakes measured with Doppler wind lidar at WiValdi J. Menken et al. https://doi.org/10.1051/epjconf/202636215004
- Evaluation of Turbulent Energy Dissipation Rate Estimation from Doppler Lidar: Impact of Techniques and Scanning Strategies S. Baek et al. https://doi.org/10.3390/rs17050939
- Wind Farm Fault Detection by Monitoring Wind Speed in the Wake Region M. Tran et al. https://doi.org/10.3390/en13246559
- Determination of the emission rates of CO2 point sources with airborne lidar S. Wolff et al. https://doi.org/10.5194/amt-14-2717-2021
- A Machine Learning-Based Model for Flight Turbulence Identification Using LiDAR Data Z. Zhuang et al. https://doi.org/10.3390/atmos14050797
- Suppression of precipitation bias in wind velocities from continuous-wave Doppler lidars L. Jin et al. https://doi.org/10.5194/amt-16-6007-2023
- The Prediction of Low-Level Jet Using Machine Learning Based on Turbulence Observations and Remote Sensing M. Chen et al. https://doi.org/10.3390/rs18030470
- Detection and quantification of CH4 plumes using the WFM-DOAS retrieval on AVIRIS-NG hyperspectral data J. Borchardt et al. https://doi.org/10.5194/amt-14-1267-2021
- Multivariate Autoregressive State-Space modelling (MARSS) to explain daily CO variability in Campo Grande-MS (2003–2016) F. Pérez et al. https://doi.org/10.1080/2150704X.2026.2696997
- Dual-lidar profilers for measuring atmospheric turbulence M. Thiébaut & N. Luxcey https://doi.org/10.5194/wes-11-825-2026
- Nacelle-based lidar measurements of wind farm turbulence and validation with a fleet of multicopter drones N. Wildmann et al. https://doi.org/10.1088/1742-6596/3224/2/022031
- Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network A. Luther et al. https://doi.org/10.5194/acp-22-5859-2022
Saved (final revised paper)
Latest update: 12 Aug 2026
