Articles | Volume 15, issue 2
https://doi.org/10.5194/amt-15-365-2022
© 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-15-365-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jan 2022
Research article |
| 24 Jan 2022
| 24 Jan 2022
Using artificial neural networks to predict riming from Doppler cloud radar observations
Institute for Meteorology, University of Leipzig, Leipzig, Germany
Maximilian Maahn
Institute for Meteorology, University of Leipzig, Leipzig, Germany
Stefan Kneifel
Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Willi Schimmel
Institute for Meteorology, University of Leipzig, Leipzig, Germany
Dmitri Moisseev
Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Helsinki, Finland
Finnish Meteorological Institute, Helsinki, Finland
Heike Kalesse-Los
Institute for Meteorology, University of Leipzig, Leipzig, Germany
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Total article views: 2,450 (including HTML, PDF, and XML)
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Total article views: 890 (including HTML, PDF, and XML)
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Cited
14 citations as recorded by crossref.
- 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
- A riming‐dependent parameterization of scattering by snowflakes using the self‐similar Rayleigh–Gans approximation N. Maherndl et al. 10.1002/qj.4573
- Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach A. Billault-Roux et al. 10.5194/amt-16-911-2023
- 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
- Cloud and Precipitation Particle Identification Using Cloud Radar and Lidar Measurements: Retrieval Technique and Validation U. Romatschke & J. Vivekanandan 10.1029/2022EA002299
- Cloud and Precipitation Profiling Radars: The First Combined W- and K-Band Radar Profiler Measurements in Italy M. Montopoli et al. 10.3390/s23125524
- 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
- Quantifying riming from airborne data during the HALO-(AC)3campaign N. Maherndl et al. 10.5194/amt-17-1475-2024
- W-band S–Z relationships for rimed snow particles: observational evidence from combined airborne and ground-based observations S. Fuller et al. 10.5194/amt-16-6123-2023
- 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
- Introducing the Video In Situ Snowfall Sensor (VISSS) M. Maahn et al. 10.5194/amt-17-899-2024
- The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations H. Kalesse-Los et al. 10.5194/amt-16-1683-2023
- 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
- 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
13 citations as recorded by crossref.
- 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
- A riming‐dependent parameterization of scattering by snowflakes using the self‐similar Rayleigh–Gans approximation N. Maherndl et al. 10.1002/qj.4573
- Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach A. Billault-Roux et al. 10.5194/amt-16-911-2023
- 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
- Cloud and Precipitation Particle Identification Using Cloud Radar and Lidar Measurements: Retrieval Technique and Validation U. Romatschke & J. Vivekanandan 10.1029/2022EA002299
- Cloud and Precipitation Profiling Radars: The First Combined W- and K-Band Radar Profiler Measurements in Italy M. Montopoli et al. 10.3390/s23125524
- 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
- Quantifying riming from airborne data during the HALO-(AC)3campaign N. Maherndl et al. 10.5194/amt-17-1475-2024
- W-band S–Z relationships for rimed snow particles: observational evidence from combined airborne and ground-based observations S. Fuller et al. 10.5194/amt-16-6123-2023
- 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
- Introducing the Video In Situ Snowfall Sensor (VISSS) M. Maahn et al. 10.5194/amt-17-899-2024
- The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations H. Kalesse-Los et al. 10.5194/amt-16-1683-2023
- 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
Latest update: 10 Jul 2025
Short summary
We are using machine learning techniques, a type of artificial intelligence, to detect graupel formation in clouds. The measurements used as input to the machine learning framework were performed by cloud radars. Cloud radars are instruments located at the ground, emitting radiation with wavelenghts of a few millimeters vertically into the cloud and measuring the back-scattered signal. Our novel technique can be applied to different radar systems and different weather conditions.
We are using machine learning techniques, a type of artificial intelligence, to detect graupel...
