Articles | Volume 11, issue 9
https://doi.org/10.5194/amt-11-4963-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-11-4963-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Sep 2018
Research article |
| 03 Sep 2018
| 03 Sep 2018
Clutter mitigation, multiple peaks, and high-order spectral moments in 35 GHz vertically pointing radar velocity spectra
Christopher R. Williams
CORRESPONDING AUTHOR
Ann and H. J. Smead Aerospace Engineering Sciences Department,
University of Colorado Boulder, Boulder, CO 80309, USA
Maximilian Maahn
Cooperative Institute for Research in Environmental Science
(CIRES), University of Colorado Boulder, Boulder, CO 80309, USA
NOAA Earth System Research Laboratory (ESRL), Physical
Sciences Division, Boulder, CO 80305, USA
Joseph C. Hardin
Pacific Northwest National Laboratory (PNNL), Richland, WA 99354, USA
Gijs de Boer
Cooperative Institute for Research in Environmental Science
(CIRES), University of Colorado Boulder, Boulder, CO 80309, USA
NOAA Earth System Research Laboratory (ESRL), Physical
Sciences Division, Boulder, CO 80305, USA
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- Particle inertial effects on radar Doppler spectra simulation Z. Zhu et al. https://doi.org/10.5194/amt-16-3727-2023
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- Higher order spectral analysis of weather signals R. Enugonda et al. https://doi.org/10.1080/09205071.2022.2112762
- Multi-parameter Doppler Weather Radar analysis of an extreme rain event in central India: Precursors of convective intensification, sectoral variability and storm structure T. Sreekanth et al. https://doi.org/10.1016/j.atmosres.2026.108952
- A robust low-level cloud and clutter discrimination method for ground-based millimeter-wavelength cloud radar X. Hu et al. https://doi.org/10.5194/amt-14-1743-2021
- Rapid Scale Wind Profiling With Autoregressive Modeling and L-Band Doppler Radar B. Domps et al. https://doi.org/10.1109/TGRS.2022.3207362
- Wind-Speed Measurements over Land Using a Doppler Profiler with a 35-GHz Operating Range V. Sterlyadkin et al. https://doi.org/10.1134/S0001433821020109
- Using cloud radar to investigate the effect of rainfall on migratory insect flight C. Wainwright et al. https://doi.org/10.1111/2041-210X.14023
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- Calibrating radar wind profiler reflectivity factor using surface disdrometer observations C. Williams et al. https://doi.org/10.5194/amt-16-2381-2023
- Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: a comprehensive long-term dataset of remote sensing observations G. Chellini et al. https://doi.org/10.5194/essd-15-5427-2023
- Removal of Multiple-Radio-Frequency Interference in 1.29 GHz Wind Profiler Spectra K. Lee & B. Kwon https://doi.org/10.3390/atmos14061040
- Improved spectral processing for a multi-mode pulse compression Ka–Ku-band cloud radar system H. Ding et al. https://doi.org/10.5194/amt-15-6181-2022
- Monitoring Public Bird Roosts with Saliency-Constrained Multi-Peak Doppler Spectra from Weather Radar Z. Yan et al. https://doi.org/10.3390/rs18050725
- PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations T. Vogl et al. https://doi.org/10.5194/amt-17-6547-2024
- PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere M. Mech et al. https://doi.org/10.5194/gmd-13-4229-2020
- Ka-Band Cloud Radar Meteorological Echo Dataset With Complex Weather Coverage: Baseline Models for Deep Learning Applications W. Zou et al. https://doi.org/10.1109/TGRS.2025.3577556
Saved (final revised paper)
Latest update: 29 Jul 2026
Short summary
This study presents three signal-processing methods to improve estimates derived from a vertically pointing 35 GHz cloud radar deployed at Oliktok Point, Alaska. The first method removes ground clutter from the Doppler velocity spectra. The second method estimates multiple peaks and high-order moments from the improved spectra. The third method removes high-frequency variability in high-order moments by shifting original 2 s spectra to a common reference before averaging over a 15 s interval.
This study presents three signal-processing methods to improve estimates derived from a...
