Articles | Volume 16, issue 5
https://doi.org/10.5194/acp-16-2997-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-16-2997-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
09 Mar 2016
Research article |
| 09 Mar 2016
Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling
McGill University Montreal, Montréal, QC, Canada
now at: Leibniz-Institute for Tropospheric Research,
Leipzig, Germany
Wanda Szyrmer
McGill University Montreal, Montréal, QC, Canada
Stefan Kneifel
McGill University Montreal, Montréal, QC, Canada
now at: University of Cologne, Cologne,
Germany
Pavlos Kollias
McGill University Montreal, Montréal, QC, Canada
now at: Stony Brook University, Stony Brook, NY, USA
Edward Luke
Brookhaven National Laboratory, Upton, NY,
USA
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- Precipitation Type Classification of Micro Rain Radar Data Using an Improved Doppler Spectral Processing Methodology A. Garcia-Benadi et al. 10.3390/rs12244113
49 citations as recorded by crossref.
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- Two-year statistics of columnar-ice production in stratiform clouds over Hyytiälä, Finland: environmental conditions and the relevance to secondary ice production H. Li et al. 10.5194/acp-21-14671-2021
- Retrievals of Riming and Snow Density From Vertically Pointing Doppler Radars S. Mason et al. 10.1029/2018JD028603
- Using artificial neural networks to predict riming from Doppler cloud radar observations T. Vogl et al. 10.5194/amt-15-365-2022
- Linking rain into ice microphysics across the melting layer in stratiform rain: a closure study K. Mróz et al. 10.5194/amt-14-511-2021
- Toward Exploring the Synergy Between Cloud Radar Polarimetry and Doppler Spectral Analysis in Deep Cold Precipitating Systems in the Arctic M. Oue et al. 10.1002/2017JD027717
- Improved spectral processing for a multi-mode pulse compression Ka–Ku-band cloud radar system H. Ding et al. 10.5194/amt-15-6181-2022
- Improving the representation of aggregation in a two-moment microphysical scheme with statistics of multi-frequency Doppler radar observations M. Karrer et al. 10.5194/acp-21-17133-2021
- Observing ice particle growth along fall streaks in mixed-phase clouds using spectral polarimetric radar data L. Pfitzenmaier et al. 10.5194/acp-18-7843-2018
- Remote Sensing J. Bühl et al. 10.1175/AMSMONOGRAPHS-D-16-0015.1
- The importance of particle size distribution and internal structure for triple-frequency radar retrievals of the morphology of snow S. Mason et al. 10.5194/amt-12-4993-2019
- Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm H. Kalesse et al. 10.5194/amt-12-4591-2019
- Evaluation of the reflectivity calibration of W-band radars based on observations in rain A. Myagkov et al. 10.5194/amt-13-5799-2020
- Doppler spectra from DWD's operational C-band radar birdbath scan: sampling strategy, spectral postprocessing, and multimodal analysis for the retrieval of precipitation processes M. Gergely et al. 10.5194/amt-15-7315-2022
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- Unraveling ice multiplication in winter orographic clouds via in-situ observations, remote sensing and modeling P. Georgakaki et al. 10.1038/s41612-024-00671-9
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- Indian West Coast's Extreme Rainfall: Sub-daily scale variability K. Sunilkumar et al. 10.1016/j.atmosres.2024.107518
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- peakTree: a framework for structure-preserving radar Doppler spectra analysis M. Radenz et al. 10.5194/amt-12-4813-2019
- Quantifying the effect of riming on snowfall using ground‐based observations D. Moisseev et al. 10.1002/2016JD026272
- Retrieving Fall Streaks within Cloud Systems Using Doppler Radar L. Pfitzenmaier et al. 10.1175/JTECH-D-16-0117.1
- McSnow: A Monte‐Carlo Particle Model for Riming and Aggregation of Ice Particles in a Multidimensional Microphysical Phase Space S. Brdar & A. Seifert 10.1002/2017MS001167
- Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars F. Tridon et al. 10.5194/amt-13-5065-2020
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- Classification of Cloud Phase Using Combined Ground-Based Polarization Lidar and Millimeter Cloud Radar Observations Over the Tibetan Plateau Y. Bian et al. 10.1109/TGRS.2023.3313798
- Application of the shipborne remote sensing supersite OCEANET for profiling of Arctic aerosols and clouds during Polarstern cruise PS106 H. Griesche et al. 10.5194/amt-13-5335-2020
- Supercooled liquid water and secondary ice production in Kelvin–Helmholtz instability as revealed by radar Doppler spectra observations H. Li et al. 10.5194/acp-21-13593-2021
- Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes S. Trömel et al. 10.5194/acp-21-17291-2021
- Highly supercooled riming and unusual triple-frequency radar signatures over McMurdo Station, Antarctica F. Tridon et al. 10.5194/acp-22-12467-2022
- Ice microphysical processes in the dendritic growth layer: a statistical analysis combining multi-frequency and polarimetric Doppler cloud radar observations L. von Terzi et al. 10.5194/acp-22-11795-2022
- Growth of ice particle mass and projected area during riming E. Erfani & D. Mitchell 10.5194/acp-17-1241-2017
- Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network H. Kalesse-Los et al. 10.5194/amt-15-279-2022
Saved (preprint)
Latest update: 09 Jul 2025
Short summary
Mixed-phase clouds are ubiquitous. Process-level understanding is needed to address the complexity of mixed-phase clouds and to improve their representation in models. This study illustrates steps to identify the impact of a microphysical process (riming) on cloud Doppler radar observations. It suggests that in situ observations of key ice properties are needed to complement radar observations before process-oriented studies can effectively evaluate ice microphysical parameterizations in models.
Mixed-phase clouds are ubiquitous. Process-level understanding is needed to address the...
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