Articles | Volume 15, issue 18
https://doi.org/10.5194/amt-15-5343-2022
© Author(s) 2022. 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-15-5343-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Sep 2022
Research article |
| 21 Sep 2022
| 21 Sep 2022
Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks
Willi Schimmel
CORRESPONDING AUTHOR
Institute for Meteorology (LIM), Leipzig University, Leipzig, Germany
Heike Kalesse-Los
Institute for Meteorology (LIM), Leipzig University, Leipzig, Germany
Maximilian Maahn
Institute for Meteorology (LIM), Leipzig University, Leipzig, Germany
Teresa Vogl
Institute for Meteorology (LIM), Leipzig University, Leipzig, Germany
Andreas Foth
Institute for Meteorology (LIM), Leipzig University, Leipzig, Germany
Pablo Saavedra Garfias
Institute for Meteorology (LIM), Leipzig University, Leipzig, Germany
Patric Seifert
Remote Sensing and Atmospheric Processes, Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
Viewed
Total article views: 5,221 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 May 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 3,369 | 1,658 | 194 | 5,221 | 226 | 216 |
- HTML: 3,369
- PDF: 1,658
- XML: 194
- Total: 5,221
- BibTeX: 226
- EndNote: 216
Total article views: 4,209 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 21 Sep 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,780 | 1,258 | 171 | 4,209 | 204 | 203 |
- HTML: 2,780
- PDF: 1,258
- XML: 171
- Total: 4,209
- BibTeX: 204
- EndNote: 203
Total article views: 1,012 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 23 May 2022)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 589 | 400 | 23 | 1,012 | 22 | 13 |
- HTML: 589
- PDF: 400
- XML: 23
- Total: 1,012
- BibTeX: 22
- EndNote: 13
Viewed (geographical distribution)
Total article views: 5,221 (including HTML, PDF, and XML)
Thereof 5,054 with geography defined
and 167 with unknown origin.
Total article views: 4,209 (including HTML, PDF, and XML)
Thereof 4,078 with geography defined
and 131 with unknown origin.
Total article views: 1,012 (including HTML, PDF, and XML)
Thereof 976 with geography defined
and 36 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
22 citations as recorded by crossref.
- Classifying thermodynamic cloud phase using machine learning models L. Goldberger et al. https://doi.org/10.5194/amt-18-5393-2025
- Measurement of supercooled liquid water path in cold clouds based on a 183GHz airborne microwave radiometer W. Wang et al. https://doi.org/10.1016/j.atmosres.2023.106655
- Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar A. Teisseire et al. https://doi.org/10.5194/amt-17-999-2024
- Low-level Arctic clouds: a blind zone in our knowledge of the radiation budget H. Griesche et al. https://doi.org/10.5194/acp-24-597-2024
- Robust Lidar-Radar Composite Cloud Boundary Detection Method With Rainfall Pixels Removal W. Zou et al. https://doi.org/10.1109/TGRS.2024.3476127
- Remote sensing measurements during PaCE 2022 campaign S. Tukiainen et al. https://doi.org/10.5194/essd-17-3797-2025
- Multirotor UAV icing correlated to liquid water content measurements in natural supercooled clouds A. Miller et al. https://doi.org/10.1016/j.coldregions.2024.104262
- A Vertically Structured Machine Learning Approach for Cloud Liquid and Ice Water Content Profiling Z. Pan et al. https://doi.org/10.3390/rs18132177
- Cloud Occurrence, Phase, and Vertical Structure in a Dust-Influenced Eastern Mediterranean Region: Observations from Limassol, Cyprus G. Kotsias et al. https://doi.org/10.3390/rs18172976
- Assessment of horizontally oriented ice crystals with a combination of multiangle polarization lidar and cloud Doppler radar Z. Wu et al. https://doi.org/10.5194/amt-18-3611-2025
- The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations H. Kalesse-Los et al. https://doi.org/10.5194/amt-16-1683-2023
- 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
- 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
- Cloud and Precipitation Profiling Radars: The First Combined W- and K-Band Radar Profiler Measurements in Italy M. Montopoli et al. https://doi.org/10.3390/s23125524
- Cloud micro- and macrophysical properties from ground-based remote sensing during the MOSAiC drift experiment H. Griesche et al. https://doi.org/10.1038/s41597-024-03325-w
- Liquid water determination by airborne millimeter cloud radar and in-situ size distribution measurements D. Zuo et al. https://doi.org/10.1016/j.atmosres.2023.106607
- Impact of seeder-feeder cloud interaction on precipitation formation: a case study based on extensive remote-sensing, in situ and model data K. Ohneiser et al. https://doi.org/10.5194/acp-25-17363-2025
- Velocity Dealiasing for 94 GHz Vertically Pointing MMCR with Dual-PRF Technique H. Lin et al. https://doi.org/10.3390/rs15215234
- Asymmetries in cloud microphysical properties ascribed to sea ice leads via water vapour transport in the central Arctic P. Saavedra Garfias et al. https://doi.org/10.5194/acp-23-14521-2023
- Cloud fields and aerosol classification with lidar using advanced AI approach Y. Peleg et al. https://doi.org/10.5194/amt-19-4415-2026
- Derivation of Aerial Insect Concentration With Triple Frequency Cloud Radar Observations M. Lochmann et al. https://doi.org/10.1109/TGRS.2026.3709607
- Long-term cloud characterization at the AGORA ACTRIS-CCRES station using a novel classification algorithm M. Tolentino et al. https://doi.org/10.5194/amt-19-2079-2026
22 citations as recorded by crossref.
- Classifying thermodynamic cloud phase using machine learning models L. Goldberger et al. https://doi.org/10.5194/amt-18-5393-2025
- Measurement of supercooled liquid water path in cold clouds based on a 183GHz airborne microwave radiometer W. Wang et al. https://doi.org/10.1016/j.atmosres.2023.106655
- Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar A. Teisseire et al. https://doi.org/10.5194/amt-17-999-2024
- Low-level Arctic clouds: a blind zone in our knowledge of the radiation budget H. Griesche et al. https://doi.org/10.5194/acp-24-597-2024
- Robust Lidar-Radar Composite Cloud Boundary Detection Method With Rainfall Pixels Removal W. Zou et al. https://doi.org/10.1109/TGRS.2024.3476127
- Remote sensing measurements during PaCE 2022 campaign S. Tukiainen et al. https://doi.org/10.5194/essd-17-3797-2025
- Multirotor UAV icing correlated to liquid water content measurements in natural supercooled clouds A. Miller et al. https://doi.org/10.1016/j.coldregions.2024.104262
- A Vertically Structured Machine Learning Approach for Cloud Liquid and Ice Water Content Profiling Z. Pan et al. https://doi.org/10.3390/rs18132177
- Cloud Occurrence, Phase, and Vertical Structure in a Dust-Influenced Eastern Mediterranean Region: Observations from Limassol, Cyprus G. Kotsias et al. https://doi.org/10.3390/rs18172976
- Assessment of horizontally oriented ice crystals with a combination of multiangle polarization lidar and cloud Doppler radar Z. Wu et al. https://doi.org/10.5194/amt-18-3611-2025
- The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations H. Kalesse-Los et al. https://doi.org/10.5194/amt-16-1683-2023
- 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
- 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
- Cloud and Precipitation Profiling Radars: The First Combined W- and K-Band Radar Profiler Measurements in Italy M. Montopoli et al. https://doi.org/10.3390/s23125524
- Cloud micro- and macrophysical properties from ground-based remote sensing during the MOSAiC drift experiment H. Griesche et al. https://doi.org/10.1038/s41597-024-03325-w
- Liquid water determination by airborne millimeter cloud radar and in-situ size distribution measurements D. Zuo et al. https://doi.org/10.1016/j.atmosres.2023.106607
- Impact of seeder-feeder cloud interaction on precipitation formation: a case study based on extensive remote-sensing, in situ and model data K. Ohneiser et al. https://doi.org/10.5194/acp-25-17363-2025
- Velocity Dealiasing for 94 GHz Vertically Pointing MMCR with Dual-PRF Technique H. Lin et al. https://doi.org/10.3390/rs15215234
- Asymmetries in cloud microphysical properties ascribed to sea ice leads via water vapour transport in the central Arctic P. Saavedra Garfias et al. https://doi.org/10.5194/acp-23-14521-2023
- Cloud fields and aerosol classification with lidar using advanced AI approach Y. Peleg et al. https://doi.org/10.5194/amt-19-4415-2026
- Derivation of Aerial Insect Concentration With Triple Frequency Cloud Radar Observations M. Lochmann et al. https://doi.org/10.1109/TGRS.2026.3709607
- Long-term cloud characterization at the AGORA ACTRIS-CCRES station using a novel classification algorithm M. Tolentino et al. https://doi.org/10.5194/amt-19-2079-2026
Saved (final revised paper)
Latest update: 13 Oct 2026
Short summary
This study introduces the novel Doppler radar spectra-based machine learning approach VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn). VOODOO is a powerful probability-based extension to the existing Cloudnet hydrometeor target classification, enabling the detection of liquid-bearing cloud layers beyond complete lidar attenuation via user-defined p* threshold. VOODOO performs best for (multi-layer) stratiform and deep mixed-phase clouds with liquid water path > 100 g m−2.
This study introduces the novel Doppler radar spectra-based machine learning approach VOODOO...
