Articles | Volume 12, issue 9
https://doi.org/10.5194/amt-12-4813-2019
© Author(s) 2019. 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-12-4813-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Sep 2019
Research article |
| 10 Sep 2019
| 10 Sep 2019
peakTree: a framework for structure-preserving radar Doppler spectra analysis
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
Johannes Bühl
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
Patric Seifert
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
Hannes Griesche
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
Ronny Engelmann
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
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17 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
- 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
- Thin disks falling in air A. Tinklenberg et al. 10.1017/jfm.2023.209
- Using cloud radar to investigate the effect of rainfall on migratory insect flight C. Wainwright et al. 10.1111/2041-210X.14023
- Cloud micro- and macrophysical properties from ground-based remote sensing during the MOSAiC drift experiment H. Griesche et al. 10.1038/s41597-024-03325-w
- PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations T. Vogl et al. 10.5194/amt-17-6547-2024
- Distinct secondary ice production processes observed in radar Doppler spectra: insights from a case study A. Billault-Roux et al. 10.5194/acp-23-10207-2023
- Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks W. Schimmel et al. 10.5194/amt-15-5343-2022
- Melting Behavior of Rimed and Unrimed Snowflakes Investigated With Statistics of Triple‐Frequency Doppler Radar Observations M. Karrer et al. 10.1029/2021JD035907
- Microphysical investigation of the seeder and feeder region of an Alpine mixed-phase cloud F. Ramelli et al. 10.5194/acp-21-6681-2021
- Attribution of riming and aggregation processes by application of the vertical distribution of particle shape (VDPS) and spectral retrieval techniques to cloud radar observations A. Teisseire et al. 10.5194/amt-18-1499-2025
- Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar A. Teisseire et al. 10.5194/amt-17-999-2024
- 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
- Identification of Concurrent Clear-Air and Precipitation Doppler Profiles for VHF Radar and an Incorporating Study of Strongly Convective Precipitation with Dual-Polarized Microwave Radiometer S. Tsai et al. 10.3390/atmos13040557
- Turbulence effect on disk settling dynamics A. Tinklenberg et al. 10.1017/jfm.2024.534
1 citations as recorded by crossref.
Latest update: 11 Jul 2025
Short summary
Clouds may be composed of more than one particle population even at the smallest scales. Cloud radar observations can contain information on multiple particle species, showing up as distinct peaks and subpeaks in the Doppler spectrum. We propose the use of binary tree structures to recursively structure these peaks. Two case studies from different locations and instruments illustrate how this approach can be used to disentangle particle populations in multilayered mixed-phase clouds.
Clouds may be composed of more than one particle population even at the smallest scales. Cloud...
