Articles | Volume 15, issue 2
https://doi.org/10.5194/amt-15-279-2022
© 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-15-279-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jan 2022
Research article |
| 20 Jan 2022
| 20 Jan 2022
Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network
Heike Kalesse-Los
CORRESPONDING AUTHOR
Institute for Meteorology, Universität Leipzig, Leipzig, Germany
Leibniz Institute for Tropospheric Research, Leipzig, Germany
Willi Schimmel
Institute for Meteorology, Universität Leipzig, Leipzig, Germany
Edward Luke
Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York, USA
Patric Seifert
Leibniz Institute for Tropospheric Research, Leipzig, Germany
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Cited
18 citations as recorded by crossref.
- 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 fields and aerosol classification with lidar using advanced AI approach Y. Peleg et al. https://doi.org/10.5194/amt-19-4415-2026
- 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
- Velocity Dealiasing for 94 GHz Vertically Pointing MMCR with Dual-PRF Technique H. Lin et al. https://doi.org/10.3390/rs15215234
- Autonomous method for selection or validation of training samples for large size hyperspectral images J. Alameddine et al. https://doi.org/10.1117/1.JRS.17.038501
- Cloud liquid water path detectability and retrieval accuracy from airborne passive microwave observations over Arctic sea ice N. Risse et al. https://doi.org/10.5194/amt-19-2197-2026
- 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
- A Machine Learning Model for FY-4A Cloud Detection Based on Physical Feature Fusion Y. Liang et al. https://doi.org/10.3390/rs18040536
- Cloud and Precipitation Particle Identification Using Cloud Radar and Lidar Measurements: Retrieval Technique and Validation U. Romatschke & J. Vivekanandan https://doi.org/10.1029/2022EA002299
- 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
- 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
- Derivation of Aerial Insect Concentration With Triple Frequency Cloud Radar Observations M. Lochmann et al. https://doi.org/10.1109/TGRS.2026.3709607
- Estimation of Weather Signal Parameters Based on Eigenvectors and Eigenvalues of Correlation Matrix of Input Influences D. Atamanskiy et al. https://doi.org/10.3103/S0735272725020050
- Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks W. Schimmel et al. https://doi.org/10.5194/amt-15-5343-2022
- 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
- Western Arctic wintertime mixed-phase clouds: Observed modulation by sea ice conditions, long-term variabilities and trends P. Saavedra Garfias & H. Kalesse-Los https://doi.org/10.5194/acp-26-14843-2026
- 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
- Evaluation of CanESM Cloudiness, Cloud Type and Cloud Radiative Forcing Climatologies Using the CALIPSO-GOCCP and CERES Datasets F. Boudala et al. https://doi.org/10.3390/rs14153668
18 citations as recorded by crossref.
- 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 fields and aerosol classification with lidar using advanced AI approach Y. Peleg et al. https://doi.org/10.5194/amt-19-4415-2026
- 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
- Velocity Dealiasing for 94 GHz Vertically Pointing MMCR with Dual-PRF Technique H. Lin et al. https://doi.org/10.3390/rs15215234
- Autonomous method for selection or validation of training samples for large size hyperspectral images J. Alameddine et al. https://doi.org/10.1117/1.JRS.17.038501
- Cloud liquid water path detectability and retrieval accuracy from airborne passive microwave observations over Arctic sea ice N. Risse et al. https://doi.org/10.5194/amt-19-2197-2026
- 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
- A Machine Learning Model for FY-4A Cloud Detection Based on Physical Feature Fusion Y. Liang et al. https://doi.org/10.3390/rs18040536
- Cloud and Precipitation Particle Identification Using Cloud Radar and Lidar Measurements: Retrieval Technique and Validation U. Romatschke & J. Vivekanandan https://doi.org/10.1029/2022EA002299
- 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
- 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
- Derivation of Aerial Insect Concentration With Triple Frequency Cloud Radar Observations M. Lochmann et al. https://doi.org/10.1109/TGRS.2026.3709607
- Estimation of Weather Signal Parameters Based on Eigenvectors and Eigenvalues of Correlation Matrix of Input Influences D. Atamanskiy et al. https://doi.org/10.3103/S0735272725020050
- Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks W. Schimmel et al. https://doi.org/10.5194/amt-15-5343-2022
- 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
- Western Arctic wintertime mixed-phase clouds: Observed modulation by sea ice conditions, long-term variabilities and trends P. Saavedra Garfias & H. Kalesse-Los https://doi.org/10.5194/acp-26-14843-2026
- 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
- Evaluation of CanESM Cloudiness, Cloud Type and Cloud Radiative Forcing Climatologies Using the CALIPSO-GOCCP and CERES Datasets F. Boudala et al. https://doi.org/10.3390/rs14153668
Saved (final revised paper)
Latest update: 27 Oct 2026
Short summary
It is important to detect the vertical distribution of cloud droplets and ice in mixed-phase clouds. Here, an artificial neural network (ANN) previously developed for Arctic clouds is applied to a mid-latitudinal cloud radar data set. The performance of this technique is contrasted to the Cloudnet target classification. For thick/multi-layer clouds, the machine learning technique is better at detecting liquid than Cloudnet, but if lidar data are available Cloudnet is at least as good as the ANN.
It is important to detect the vertical distribution of cloud droplets and ice in mixed-phase...
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