Articles | Volume 15, issue 2
Atmos. Meas. Tech., 15, 365–381, 2022

Special issue: Fusion of radar polarimetry and numerical atmospheric modelling...

Atmos. Meas. Tech., 15, 365–381, 2022
Research article
24 Jan 2022
Research article | 24 Jan 2022

Using artificial neural networks to predict riming from Doppler cloud radar observations

Teresa Vogl et al.

Related authors

Identifying cloud droplets beyond lidar attenuation from vertically-pointing cloud radar observations using artificial neural networks
Willi Schimmel, Heike Kalesse-Los, Maximilian Maahn, Teresa Vogl, Andreas Foth, Pablo Saavedra Garfias, and Patric Seifert
Atmos. Meas. Tech. Discuss.,,, 2022
Revised manuscript accepted for AMT
Short summary
Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes
Silke Trömel, Clemens Simmer, Ulrich Blahak, Armin Blanke, Sabine Doktorowski, Florian Ewald, Michael Frech, Mathias Gergely, Martin Hagen, Tijana Janjic, Heike Kalesse-Los, Stefan Kneifel, Christoph Knote, Jana Mendrok, Manuel Moser, Gregor Köcher, Kai Mühlbauer, Alexander Myagkov, Velibor Pejcic, Patric Seifert, Prabhakar Shrestha, Audrey Teisseire, Leonie von Terzi, Eleni Tetoni, Teresa Vogl, Christiane Voigt, Yuefei Zeng, Tobias Zinner, and Johannes Quaas
Atmos. Chem. Phys., 21, 17291–17314,,, 2021
Short summary
Choosing an optimal β factor for relaxed eddy accumulation applications across vegetated and non-vegetated surfaces
Teresa Vogl, Amy Hrdina, and Christoph K. Thomas
Biogeosciences, 18, 5097–5115,,, 2021
Short summary
Terrestrial or marine – indications towards the origin of ice-nucleating particles during melt season in the European Arctic up to 83.7° N
Markus Hartmann, Xianda Gong, Simonas Kecorius, Manuela van Pinxteren, Teresa Vogl, André Welti, Heike Wex, Sebastian Zeppenfeld, Hartmut Herrmann, Alfred Wiedensohler, and Frank Stratmann
Atmos. Chem. Phys., 21, 11613–11636,,, 2021
Short summary
New particle formation and its effect on cloud condensation nuclei abundance in the summer Arctic: a case study in the Fram Strait and Barents Sea
Simonas Kecorius, Teresa Vogl, Pauli Paasonen, Janne Lampilahti, Daniel Rothenberg, Heike Wex, Sebastian Zeppenfeld, Manuela van Pinxteren, Markus Hartmann, Silvia Henning, Xianda Gong, Andre Welti, Markku Kulmala, Frank Stratmann, Hartmut Herrmann, and Alfred Wiedensohler
Atmos. Chem. Phys., 19, 14339–14364,,, 2019
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
A kriging-based analysis of cloud liquid water content using CloudSat data
Jean-Marie Lalande, Guillaume Bourmaud, Pierre Minvielle, and Jean-François Giovannelli
Atmos. Meas. Tech., 15, 4411–4429,,, 2022
Short summary
High-resolution satellite-based cloud detection for the analysis of land surface effects on boundary layer clouds
Julia Fuchs, Hendrik Andersen, Jan Cermak, Eva Pauli, and Rob Roebeling
Atmos. Meas. Tech., 15, 4257–4270,,, 2022
Short summary
Retrievals of ice microphysical properties using dual-wavelength polarimetric radar observations during stratiform precipitation events
Eleni Tetoni, Florian Ewald, Martin Hagen, Gregor Köcher, Tobias Zinner, and Silke Groß
Atmos. Meas. Tech., 15, 3969–3999,,, 2022
Short summary
The surface longwave cloud radiative effect derived from space lidar observations
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923,,, 2022
Short summary
Cloud phase and macrophysical properties over the Southern Ocean during the MARCUS field campaign
Baike Xi, Xiquan Dong, Xiaojian Zheng, and Peng Wu
Atmos. Meas. Tech., 15, 3761–3777,,, 2022
Short summary

Cited articles

Atmospheric Radiation Measurement (ARM) user facility: Ka ARM Zenith Radar (KAZRSPECCMASKMDCOPOL), 2014-02-21 to 2014-02-22, ARM Mobile Facility (TMP) U. of Helsinki Research Station (SMEAR II), Hyytiala, Finland; AMF2 (M1), compiled by: Lindenmaier, I., Bharadwaj, N., Johnson, K., Nelson, D., Matthews, A., Wendler, T., and Castro, V., ARM Data Center,, 2014a. a
Atmospheric Radiation Measurement (ARM) user facility: Microwave Radiometer (MWRLOS), 2014-02-21 to 2014-02-22, ARM Mobile Facility (TMP) U. of Helsinki Research Station (SMEAR II), Hyytiala, Finland; AMF2 (M1), compiled by: Cadeddu, M., ARM Data Center.,, 2014b. a
Atmospheric Radiation Measurement (ARM) user facility: Balloon-Borne Sounding System (SONDEWNPN), 2014-02-01 to 2014-03-20, ARM Mobile Facility (TMP) U. of Helsinki Research Station (SMEAR II), Hyytiala, Finland; AMF2 (M1), compiled by: Keeler, E., Coulter, R., and Kyrouac, J., ARM Data Center,, 2014c. a
Barrett, A. I., Westbrook, C. D., Nicol, J. C., and Stein, T. H. M.: Rapid ice aggregation process revealed through triple-wavelength Doppler spectrum radar analysis, Atmos. Chem. Phys., 19, 5753–5769,, 2019. a
Bühl, J., Seifert, P., Myagkov, A., and Ansmann, A.: Measuring ice- and liquid-water properties in mixed-phase cloud layers at the Leipzig Cloudnet station, Atmos. Chem. Phys., 16, 10609–10620,, 2016. a
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.