Articles | Volume 16, issue 19
https://doi.org/10.5194/amt-16-4409-2023
https://doi.org/10.5194/amt-16-4409-2023
Research article
 | 
09 Oct 2023
Research article |  | 09 Oct 2023

On the polarimetric backscatter by a still or quasi-still wind turbine

Marco Gabella, Martin Lainer, Daniel Wolfensberger, and Jacopo Grazioli

Related authors

Flash-flood Alert System using Ensemble Radar Prediction and Rainfall-runoff Simulation
Frédéric G. Jordan, Clément Cosson, Marco Gabella, Ioannis V. Sideris, Adrien Liernur, Alexis Berne, and Urs Germann
Abstr. Int. Cartogr. Assoc., 9, 19, https://doi.org/10.5194/ica-abs-9-19-2025,https://doi.org/10.5194/ica-abs-9-19-2025, 2025
Double-moment normalization of hail size number distributions over Switzerland
Alfonso Ferrone, Jérôme Kopp, Martin Lainer, Marco Gabella, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 17, 7143–7168, https://doi.org/10.5194/amt-17-7143-2024,https://doi.org/10.5194/amt-17-7143-2024, 2024
Short summary
A quest for precipitation attractors in weather radar archives
Loris Foresti, Bernat Puigdomènech Treserras, Daniele Nerini, Aitor Atencia, Marco Gabella, Ioannis V. Sideris, Urs Germann, and Isztar Zawadzki
Nonlin. Processes Geophys., 31, 259–286, https://doi.org/10.5194/npg-31-259-2024,https://doi.org/10.5194/npg-31-259-2024, 2024
Short summary
Spatio-temporal reconstruction of winter glacier mass balance in the Alps, Scandinavia, Central Asia and western Canada (1981–2019) using climate reanalyses and machine learning
Matteo Guidicelli, Matthias Huss, Marco Gabella, and Nadine Salzmann
The Cryosphere, 17, 977–1002, https://doi.org/10.5194/tc-17-977-2023,https://doi.org/10.5194/tc-17-977-2023, 2023
Short summary
A characterisation of Alpine mesocyclone occurrence
Monika Feldmann, Urs Germann, Marco Gabella, and Alexis Berne
Weather Clim. Dynam., 2, 1225–1244, https://doi.org/10.5194/wcd-2-1225-2021,https://doi.org/10.5194/wcd-2-1225-2021, 2021
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

Angulo, I., de la Vega, D., Cascon, I., Canizo, J., Wu, Y., Guerra, D., and Angueira, P.: Impact analysis of wind farms on telecommunication services, Renew. Sust. Energ. Rev., 32, 84–99, 2014. 
Angulo, I., Grande, O., Jenn, D., Guerra, D., and de la Vega, D.: Estimating reflectivity values from wind turbines for analyzing the potential impact on weather radar services, Atmos. Meas. Tech., 8, 2183–2193, https://doi.org/10.5194/amt-8-2183-2015, 2015. 
Anonymous referee: Interactive comment on “Insights into wind turbine reflectivity and RCS and their variability using X-band weather radar observations” by Martin Lainer et al., Referee comment 1, https://doi.org/10.5194/amt-2020-384-RC1, 2020. 
Bredemeyer, J., Schubert, K., Werner, J., Schrader, T., and Mihalachi, M.: Comparison of principles for measuring the reflectivity values from wind turbines, 20th International Radar Symposium (IRS), 26–28 June 2019, Ulm, Germany​​​​​​​, 1–10, https://doi.org/10.23919/IRS.2019.8768171, 2019.​​​​​​​ 
Brindley, G.: Financing and investment trends: The European wind industry in 2021, report, WindEurope, Brussels, Belgium, https://windeurope.org/intelligence-platform/product/financing-and-investment-trends-2021/​​​​​​​ (last access: 1 October 2023), 2022.​​​​​​​ 
Download
Short summary
A still wind turbine observed with a fixed-pointing radar antenna has shown distinctive polarimetric signatures: the correlation coefficient between the two orthogonal polarization states was persistently equal to 1. The differential reflectivity and the radar reflectivity factors were also stable in time. Over 2 min (2000 Hz, 128 pulses were used; consequently, the sampling time was 64 ms), the standard deviation of the differential backscattering phase shift was only a few degrees.
Share