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

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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
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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.
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