Articles | Volume 12, issue 8
https://doi.org/10.5194/amt-12-4591-2019
https://doi.org/10.5194/amt-12-4591-2019
Research article
 | 
30 Aug 2019
Research article |  | 30 Aug 2019

Development and validation of a supervised machine learning radar Doppler spectra peak-finding algorithm

Heike Kalesse, Teresa Vogl, Cosmin Paduraru, and Edward Luke

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Cited articles

Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, 2016. 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, https://doi.org/10.5194/acp-16-10609-2016, 2016. 
Cornman, L. B., Goodrich, R. K., Morse, C. S., and Ecklund, W. L.: A Fuzzy Logic Method for Improved Moment Estimation from Doppler Spectra, J. Atmos. Ocean. Tech., 15, 1287–1305, https://doi.org/10.1175/1520-0426(1998)015<1287:AFLMFI>2.0.CO;2, 1998. a
Ermold, B., Eloranta, E., Michelsen, H., Garcia, J., Goldsmith, J., and Bambha, R.: High Spectral Resolution Lidar (HSRL), data set, https://doi.org/10.5439/1025200, 2014. 
Hildebrand, P. H. and Sekhon, R. S.: Objective Determination of the Noise Level in Doppler Spectra, J. Appl. Meteor., 13, 808–811, https://doi.org/10.1175/1520-0450(1974)013<0808:odotnl>2.0.co;2, 1974. a, b, c, d
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Short summary
In a cloud, different particles like liquid water droplets and ice particles can exist simultaneously. To study the evolution of cloud particles from cloud top to bottom one has to find out how many different types of particles with different fall velocities are present. This can be done by analyzing the number of peaks in upward-looking cloud radar Doppler spectra. A new machine-learning algorithm (named PEAKO) that determines the number of peaks is introduced and compared to existing methods.