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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-48
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-48
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 28 Mar 2019

Research article | 28 Mar 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

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

Heike Kalesse1,2, Teresa Vogl1,2, Cosmin Paduraru3, and Edward Luke4 Heike Kalesse et al.
  • 1Leibniz Institute for Tropospheric Research, Leipzig, Germany
  • 2Institute for Meteorology, Universität Leipzig, Leipzig, Germany
  • 3Department of Mining and Materials Engineering, McGill University, Montreal, Canada
  • 4Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, New York

Abstract. In many types of clouds, multiple hydrometeor populations can be present at the same time and height. Studying the evolution of these different hydrometeors in a time-height perspective can give valuable information on cloud particle composition and microphysical growth processes. However, as a prerequisite, the number of different hydrometeor types in a certain cloud volume needs to be quantified. This can be accomplished using cloud radar Doppler velocity spectra from profiling cloud radars if the different hydrometeor types have sufficiently different terminal fall velocities to produce individual Doppler spectrum peaks. Here we present a newly developed supervised machine learning radar Doppler spectra peak finding algorithm (named PEAKO). In this approach, three adjustable parameters (spectrum smoothing span, prominence threshold, and minimum peak width at half-height) are varied to obtain the set of parameters which yields the best agreement of user-classified and machine-marked peaks. The algorithm was developed for Ka-band ARM Zenith-pointing Radar (KAZR) observations obtained in thick snow fall systems during the Atmospheric Radiation Measurement Program’s (ARM) mobile facility AMF2 deployment at Hyytiälä, Finland, during the Biogenic Aerosols – Effects on Clouds and Climate (BAECC) field campaign. The performance of PEAKO is evaluated by comparing its results to existing Doppler peak finding algorithms. The new algorithm is found to perform well. Its advantage is that the detected features are less noisy and more consistent in time and height than the peak finding results of other algorithms. In the future, the PEAKO algorithm will be adapted to other cloud radars and other types of clouds consisting of multiple hydrometeors in the same cloud volume.

Heike Kalesse et al.
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Heike Kalesse et al.
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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 analysing 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.
In a cloud, different particles like liquid water droplets and ice particles can exist...
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