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

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
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. Discuss., https://doi.org/10.5194/amt-2024-2,https://doi.org/10.5194/amt-2024-2, 2024
Preprint under review for AMT
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
Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observations
Martin Lainer, Jordi Figueras i Ventura, Zaira Schauwecker, Marco Gabella, Montserrat F.-Bolaños, Reto Pauli, and Jacopo Grazioli
Atmos. Meas. Tech., 14, 3541–3560, https://doi.org/10.5194/amt-14-3541-2021,https://doi.org/10.5194/amt-14-3541-2021, 2021
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Estimating the refractivity bias of FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) radio occultation in the deep troposphere
Gia Huan Pham, Shu-Chih Yang, Chih-Chien Chang, Shu-Ya Chen, and Cheng Yung Huang
Atmos. Meas. Tech., 17, 3605–3623, https://doi.org/10.5194/amt-17-3605-2024,https://doi.org/10.5194/amt-17-3605-2024, 2024
Short summary
High Spectral Resolution Lidar – generation 2 (HSRL-2) retrievals of ocean surface wind speed: methodology and evaluation
Sanja Dmitrovic, Johnathan W. Hair, Brian L. Collister, Ewan Crosbie, Marta A. Fenn, Richard A. Ferrare, David B. Harper, Chris A. Hostetler, Yongxiang Hu, John A. Reagan, Claire E. Robinson, Shane T. Seaman, Taylor J. Shingler, Kenneth L. Thornhill, Holger Vömel, Xubin Zeng, and Armin Sorooshian
Atmos. Meas. Tech., 17, 3515–3532, https://doi.org/10.5194/amt-17-3515-2024,https://doi.org/10.5194/amt-17-3515-2024, 2024
Short summary
Dual adaptive differential threshold method for automated detection of faint and strong echo features in radar observations of winter storms
Laura M. Tomkins, Sandra E. Yuter, and Matthew A. Miller
Atmos. Meas. Tech., 17, 3377–3399, https://doi.org/10.5194/amt-17-3377-2024,https://doi.org/10.5194/amt-17-3377-2024, 2024
Short summary
Noise filtering options for conically scanning Doppler lidar measurements with low pulse accumulation
Eileen Päschke and Carola Detring
Atmos. Meas. Tech., 17, 3187–3217, https://doi.org/10.5194/amt-17-3187-2024,https://doi.org/10.5194/amt-17-3187-2024, 2024
Short summary
Measuring rainfall using microwave links: the influence of temporal sampling
Luuk D. van der Valk, Miriam Coenders-Gerrits, Rolf W. Hut, Aart Overeem, Bas Walraven, and Remko Uijlenhoet
Atmos. Meas. Tech., 17, 2811–2832, https://doi.org/10.5194/amt-17-2811-2024,https://doi.org/10.5194/amt-17-2811-2024, 2024
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.