Articles | Volume 17, issue 23
https://doi.org/10.5194/amt-17-6913-2024
https://doi.org/10.5194/amt-17-6913-2024
Research article
 | 
10 Dec 2024
Research article |  | 10 Dec 2024

Analysis of the measurement uncertainty for a 3D wind lidar

Wolf Knöller, Gholamhossein Bagheri, Philipp von Olshausen, and Michael Wilczek

Related authors

Max Planck WinDarts: High-Resolution Atmospheric Boundary Layer Measurements with the Max Planck CloudKite platform and Ground Weather Station – A Data Overview
Venecia Chávez-Medina, Hossein Khodamoradi, Oliver Schlenczek, Freja Nordsiek, Claudia E. Brunner, Eberhard Bodenschatz, and Gholamhossein Bagheri
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-111,https://doi.org/10.5194/essd-2025-111, 2025
Preprint under review for ESSD
Short summary
Airborne measurements of turbulence and cloud microphysics during PaCE 2022 using the Advanced Max Planck CloudKite Instrument (MPCK+)
Oliver Schlenczek, Freja Nordsiek, Claudia E. Brunner, Venecia Chávez-Medina, Birte Thiede, Eberhard Bodenschatz, and Gholamhossein Bagheri
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-112,https://doi.org/10.5194/essd-2025-112, 2025
Preprint under review for ESSD
Short summary
The atmospheric settling of commercially sold microplastics
Alina Sylvia Waltraud Reininger, Daria Tatsii, Taraprasad Bhowmick, Gholamhossein Bagheri, and Andreas Stohl
EGUsphere, https://doi.org/10.5194/egusphere-2025-605,https://doi.org/10.5194/egusphere-2025-605, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
In-situ volcanic ash sampling and aerosol-gas analysis based on UAS technologies (AeroVolc)
Simon Thivet, Gholamhossein Bagheri, Przemyslaw M. Kornatowski, Allan Fries, Jonathan Lemus, Riccardo Simionato, Carolina Díaz-Vecino, Eduardo Rossi, Taishi Yamada, Simona Scollo, and Costanza Bonadonna
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-162,https://doi.org/10.5194/amt-2024-162, 2024
Revised manuscript under review for AMT
Short summary
Estimating the turbulent kinetic energy dissipation rate from one-dimensional velocity measurements in time
Marcel Schröder, Tobias Bätge, Eberhard Bodenschatz, Michael Wilczek, and Gholamhossein Bagheri
Atmos. Meas. Tech., 17, 627–657, https://doi.org/10.5194/amt-17-627-2024,https://doi.org/10.5194/amt-17-627-2024, 2024
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Mid-Atlantic nocturnal low-level jet characteristics: a machine learning analysis of radar wind profiles
Maurice Roots, John T. Sullivan, and Belay Demoz
Atmos. Meas. Tech., 18, 1269–1282, https://doi.org/10.5194/amt-18-1269-2025,https://doi.org/10.5194/amt-18-1269-2025, 2025
Short summary
Mitigating radome-induced bias in X-band weather radar polarimetric moments using an adaptive discrete Fourier transform algorithm
Padmanabhan Thiruvengadam, Guillaume Lesage, Ambinintsoa Volatiana Ramanamahefa, and Joël Van Baelen
Atmos. Meas. Tech., 18, 1185–1191, https://doi.org/10.5194/amt-18-1185-2025,https://doi.org/10.5194/amt-18-1185-2025, 2025
Short summary
GNSS-RO residual ionospheric error (RIE): a new method and assessment
Dong L. Wu, Valery A. Yudin, Kyu-Myong Kim, Mohar Chattopadhyay, Lawrence Coy, Ruth S. Lieberman, C. C. Jude H. Salinas, Jae N. Lee, Jie Gong, and Guiping Liu
Atmos. Meas. Tech., 18, 843–863, https://doi.org/10.5194/amt-18-843-2025,https://doi.org/10.5194/amt-18-843-2025, 2025
Short summary
Benchmarking KDP in rainfall: a quantitative assessment of estimation algorithms using C-band weather radar observations
Miguel Aldana, Seppo Pulkkinen, Annakaisa von Lerber, Matthew R. Kumjian, and Dmitri Moisseev
Atmos. Meas. Tech., 18, 793–816, https://doi.org/10.5194/amt-18-793-2025,https://doi.org/10.5194/amt-18-793-2025, 2025
Short summary
Comparative experimental validation of microwave hyperspectral atmospheric soundings in clear-sky conditions
Lei Liu, Natalia Bliankinshtein, Yi Huang, John R. Gyakum, Philip M. Gabriel, Shiqi Xu, and Mengistu Wolde
Atmos. Meas. Tech., 18, 471–485, https://doi.org/10.5194/amt-18-471-2025,https://doi.org/10.5194/amt-18-471-2025, 2025
Short summary

Cited articles

Bagheri, G., Nordsiek, F., Schlenczek, O., and Bodenschatz, E.: Cloudkite: an airborne platform for resolving clouds, in: EGU General Assembly Conference Abstracts, EGU General Assembly Conference Abstracts, 8–13 April 2018. Vienna, Austria, EGU2018-8821, 2018. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a
Bertens, A. C. M.: Experimental investigation of Cloud droplet dynamics at the research station Schneefernerhaus, PhD thesis, Georg-August-Universität Göttingen, https://doi.org/10.53846/goediss-8925, 2021. a, b, c
Bingöl, F., Mann, J., and Foussekis, D.: Conically scanning lidar error in complex terrain, Meteorol. Z., 18, 189–195, https://doi.org/10.1127/0941-2948/2009/0368, 2009. a
Bodenschatz, E., Malinowski, S. P., Shaw, R. A., and Stratmann, F.: Can We Understand Clouds Without Turbulence?, Science, 327, 970–971, https://doi.org/10.1126/science.1185138, 2010. a
Download
Short summary
Three-dimensional (3D) wind velocity measurements are of major importance for the characterization of atmospheric turbulence. This paper presents a detailed study of the measurement uncertainty of a three-beam wind lidar designed for mounting on airborne platforms. Considering the geometrical constraints, the analysis provides quantitative estimates for the measurement uncertainty of all components of the 3D wind vector. As a result, we propose optimized post-processing for error reduction.
Share