Articles | Volume 15, issue 18
https://doi.org/10.5194/amt-15-5343-2022
https://doi.org/10.5194/amt-15-5343-2022
Research article
 | 
21 Sep 2022
Research article |  | 21 Sep 2022

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

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Cited articles

Alexander, S. P., Sato, K., Watanabe, S., Kawatani, Y., and Murphy, D. J.: Southern Hemisphere Extratropical Gravity Wave Sources and Intermittency Revealed by a Middle-Atmosphere General Circulation Model, J. Atmos. Sci., 73, 1335–1349, https://doi.org/10.1175/JAS-D-15-0149.1, 2016. a
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Barrientos Velasco, C., Deneke, H., Griesche, H., Seifert, P., Engelmann, R., and Macke, A.: Spatiotemporal variability of solar radiation introduced by clouds over Arctic sea ice, Atmos. Meas. Tech., 13, 1757–1775, https://doi.org/10.5194/amt-13-1757-2020, 2020. a
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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.
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