Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 3.668
IF3.668
IF 5-year value: 3.707
IF 5-year
3.707
CiteScore value: 6.3
CiteScore
6.3
SNIP value: 1.383
SNIP1.383
IPP value: 3.75
IPP3.75
SJR value: 1.525
SJR1.525
Scimago H <br class='widget-line-break'>index value: 77
Scimago H
index
77
h5-index value: 49
h5-index49
Volume 10, issue 5
Atmos. Meas. Tech., 10, 1739–1753, 2017
https://doi.org/10.5194/amt-10-1739-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 10, 1739–1753, 2017
https://doi.org/10.5194/amt-10-1739-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 10 May 2017

Research article | 10 May 2017

Wind turbine impact on operational weather radar I/Q data: characterisation and filtering

Lars Norin

Related authors

The sensitivity of snowfall to weather states over Sweden
Lars Norin, Abhay Devasthale, and Tristan S. L'Ecuyer
Atmos. Meas. Tech., 10, 3249–3263, https://doi.org/10.5194/amt-10-3249-2017,https://doi.org/10.5194/amt-10-3249-2017, 2017
Short summary
Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden
L. Norin, A. Devasthale, T. S. L'Ecuyer, N. B. Wood, and M. Smalley
Atmos. Meas. Tech., 8, 5009–5021, https://doi.org/10.5194/amt-8-5009-2015,https://doi.org/10.5194/amt-8-5009-2015, 2015
Short summary
A quantitative analysis of the impact of wind turbines on operational Doppler weather radar data
L. Norin
Atmos. Meas. Tech., 8, 593–609, https://doi.org/10.5194/amt-8-593-2015,https://doi.org/10.5194/amt-8-593-2015, 2015
Short summary
The large-scale spatio-temporal variability of precipitation over Sweden observed from the weather radar network
A. Devasthale and L. Norin
Atmos. Meas. Tech., 7, 1605–1617, https://doi.org/10.5194/amt-7-1605-2014,https://doi.org/10.5194/amt-7-1605-2014, 2014

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Rain event detection in commercial microwave link attenuation data using convolutional neural networks
Julius Polz, Christian Chwala, Maximilian Graf, and Harald Kunstmann
Atmos. Meas. Tech., 13, 3835–3853, https://doi.org/10.5194/amt-13-3835-2020,https://doi.org/10.5194/amt-13-3835-2020, 2020
Short summary
Preliminary investigation of the relationship between differential phase shift and path-integrated attenuation at the X band frequency in an Alpine environment
Guy Delrieu, Anil Kumar Khanal, Nan Yu, Frédéric Cazenave, Brice Boudevillain, and Nicolas Gaussiat
Atmos. Meas. Tech., 13, 3731–3749, https://doi.org/10.5194/amt-13-3731-2020,https://doi.org/10.5194/amt-13-3731-2020, 2020
Observation of sensible and latent heat flux profiles with lidar
Andreas Behrendt, Volker Wulfmeyer, Christoph Senff, Shravan Kumar Muppa, Florian Späth, Diego Lange, Norbert Kalthoff, and Andreas Wieser
Atmos. Meas. Tech., 13, 3221–3233, https://doi.org/10.5194/amt-13-3221-2020,https://doi.org/10.5194/amt-13-3221-2020, 2020
Short summary
Methodology for deriving the telescope focus function and its uncertainty for a heterodyne pulsed Doppler lidar
Pyry Pentikäinen, Ewan James O'Connor, Antti Juhani Manninen, and Pablo Ortiz-Amezcua
Atmos. Meas. Tech., 13, 2849–2863, https://doi.org/10.5194/amt-13-2849-2020,https://doi.org/10.5194/amt-13-2849-2020, 2020
Short summary
Update of Infrared Atmospheric Sounding Interferometer (IASI) channel selection with correlated observation errors for numerical weather prediction (NWP)
Olivier Coopmann, Vincent Guidard, Nadia Fourrié, Béatrice Josse, and Virginie Marécal
Atmos. Meas. Tech., 13, 2659–2680, https://doi.org/10.5194/amt-13-2659-2020,https://doi.org/10.5194/amt-13-2659-2020, 2020
Short summary

Cited articles

Aarholt, E. and Jackson, C. A.: Wind farm Gapfiller concept solution, in: Proceedings of the seventh European Radar Conference, Paris, France, 236–239, 2010.
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.
Bachmann, S., Al-Rashid, Y., Bronecke, P., Palmer, R., and Isom, B.: Suppression of the windfarm contribution from the atmospheric radar returns, in: Proceedings of the 26th Conference on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, American Meteorological Society, Atlanta, GA, USA, 81–86, 2010a.
Bachmann, S., Al-Rashid, Y., Isom, B., and Palmer, R.: Radar and Windfarms – mitigating negative effects through signal processing, in: Proceedings of the sixth European Conference on Radar in Meteorology and Hydrology, Sibiu, Romania, 81–86, 2010b.
Bacon, D. F.: Fixed-link wind-turbine exclusion zone method, Tech. rep., Radiocommunications Agency, 2002.
Publications Copernicus
Download
Short summary
Wind turbines in the line of sight of a weather radar can have a negative impact on the quality of the radar's measurements. Wind turbine echoes have proven difficult to filter due to their complex and time-varying nature. In this work we present recordings of high-resolution low-level data from a Swedish weather radar. A characteristic and robust signature from wind turbines is found. A simple wind turbine filter is presented and applied to the recorded data.
Wind turbines in the line of sight of a weather radar can have a negative impact on the quality...
Citation