Articles | Volume 13, issue 8
https://doi.org/10.5194/amt-13-4141-2020
https://doi.org/10.5194/amt-13-4141-2020
Research article
 | 
04 Aug 2020
Research article |  | 04 Aug 2020

Towards improved turbulence estimation with Doppler wind lidar velocity-azimuth display (VAD) scans

Norman Wildmann, Eileen Päschke, Anke Roiger, and Christian Mallaun

Related authors

Quantification of methane emissions in Hamburg using a network of FTIR spectrometers and an inverse modeling approach
Andreas Forstmaier, Jia Chen, Florian Dietrich, Juan Bettinelli, Hossein Maazallahi, Carsten Schneider, Dominik Winkler, Xinxu Zhao, Taylor Jones, Carina van der Veen, Norman Wildmann, Moritz Makowski, Aydin Uzun, Friedrich Klappenbach, Hugo Denier van der Gon, Stefan Schwietzke, and Thomas Röckmann
Atmos. Chem. Phys., 23, 6897–6922, https://doi.org/10.5194/acp-23-6897-2023,https://doi.org/10.5194/acp-23-6897-2023, 2023
Short summary
Multi-point in situ measurements of turbulent flow in a wind turbine wake and inflow with a fleet of uncrewed aerial systems
Tamino Wetz and Norman Wildmann
Wind Energ. Sci., 8, 515–534, https://doi.org/10.5194/wes-8-515-2023,https://doi.org/10.5194/wes-8-515-2023, 2023
Short summary
Towards vertical wind and turbulent flux estimation with multicopter uncrewed aircraft systems
Norman Wildmann and Tamino Wetz
Atmos. Meas. Tech., 15, 5465–5477, https://doi.org/10.5194/amt-15-5465-2022,https://doi.org/10.5194/amt-15-5465-2022, 2022
Short summary
Evaluation of a forest parameterization to improve boundary layer flow simulations over complex terrain. A case study using WRF-LES V4.0.1
Julian Quimbayo-Duarte, Johannes Wagner, Norman Wildmann, Thomas Gerz, and Juerg Schmidli
Geosci. Model Dev., 15, 5195–5209, https://doi.org/10.5194/gmd-15-5195-2022,https://doi.org/10.5194/gmd-15-5195-2022, 2022
Short summary
Observational constraints on methane emissions from Polish coal mines using a ground-based remote sensing network
Andreas Luther, Julian Kostinek, Ralph Kleinschek, Sara Defratyka, Mila Stanisavljević, Andreas Forstmaier, Alexandru Dandocsi, Leon Scheidweiler, Darko Dubravica, Norman Wildmann, Frank Hase, Matthias M. Frey, Jia Chen, Florian Dietrich, Jarosław Nȩcki, Justyna Swolkień, Christoph Knote, Sanam N. Vardag, Anke Roiger, and André Butz
Atmos. Chem. Phys., 22, 5859–5876, https://doi.org/10.5194/acp-22-5859-2022,https://doi.org/10.5194/acp-22-5859-2022, 2022
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Performance evaluation of three bio-optical models in aerosol and ocean color joint retrievals
Neranga K. Hannadige, Peng-Wang Zhai, Meng Gao, Yongxiang Hu, P. Jeremy Werdell, Kirk Knobelspiesse, and Brian Cairns
Atmos. Meas. Tech., 16, 5749–5770, https://doi.org/10.5194/amt-16-5749-2023,https://doi.org/10.5194/amt-16-5749-2023, 2023
Short summary
Observation of horizontal temperature variations by a spatial heterodyne interferometer using single-sided interferograms
Konstantin Ntokas, Jörn Ungermann, Martin Kaufmann, Tom Neubert, and Martin Riese
Atmos. Meas. Tech., 16, 5681–5696, https://doi.org/10.5194/amt-16-5681-2023,https://doi.org/10.5194/amt-16-5681-2023, 2023
Short summary
Version 8 IMK–IAA MIPAS temperatures from 12–15 µm spectra: Middle and Upper Atmosphere modes
Maya García-Comas, Bernd Funke, Manuel López-Puertas, Norbert Glatthor, Udo Grabowski, Sylvia Kellmann, Michael Kiefer, Andrea Linden, Belén Martínez-Mondéjar, Gabriele P. Stiller, and Thomas von Clarmann
Atmos. Meas. Tech., 16, 5357–5386, https://doi.org/10.5194/amt-16-5357-2023,https://doi.org/10.5194/amt-16-5357-2023, 2023
Short summary
GNSS radio occultation excess-phase processing for climate applications including uncertainty estimation
Josef Innerkofler, Gottfried Kirchengast, Marc Schwärz, Christian Marquardt, and Yago Andres
Atmos. Meas. Tech., 16, 5217–5247, https://doi.org/10.5194/amt-16-5217-2023,https://doi.org/10.5194/amt-16-5217-2023, 2023
Short summary
Impact analysis of processing strategies for long-term GPS zenith tropospheric delay (ZTD)
Jingna Bai, Yidong Lou, Weixing Zhang, Yaozong Zhou, Zhenyi Zhang, Chuang Shi, and Jingnan Liu
Atmos. Meas. Tech., 16, 5249–5259, https://doi.org/10.5194/amt-16-5249-2023,https://doi.org/10.5194/amt-16-5249-2023, 2023
Short summary

Cited articles

Banakh, V. and Smalikho, I.: Coherent Doppler Wind Lidars in a Turbulent Atmosphere, Radar, Artech House, Boston, MA, USA, 2013. a, b
Banakh, V. A. and Smalikho, I. N.: Lidar Estimates of the Anisotropy of Wind Turbulence in a Stable Atmospheric Boundary Layer, Remote Sens.-Basel, 11, 2115, https://doi.org/10.3390/rs11182115, 2019. a
Banakh, V. A., Smalikho, I. N., Köpp, F., and Werner, C.: Measurements of Turbulent Energy Dissipation Rate with a CW Doppler Lidar in the Atmospheric Boundary Layer, J. Atmos. Ocean Tech., 16, 1044–1061, https://doi.org/10.1175/1520-0426(1999)016<1044:MOTEDR>2.0.CO;2, 1999. a
Bange, J., Beyrich, F., and Engelbart, D. A. M.: Airborne Measurements of Turbulent Fluxes during LITFASS-98: A Case Study about Method and Significance, Theor. Appl. Climatol., 73, 35–51, 2002. a
Beyrich, F., Leps, J.-P., Mauder, M., Bange, J., Foken, T., Huneke, S., Lohse, H., Lüdi, A., Meijninger, W., Mironov, D., Weisensee, U., and Zittel, P.: Area-Averaged Surface Fluxes Over the Litfass Region Based on Eddy-Covariance Measurements, Bound.-Lay. Meteorol., 121, 33–65, https://doi.org/10.1007/s10546-006-9052-x, 2006. a