Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 22
Articles | Volume 17, issue 22
https://doi.org/10.5194/amt-17-6547-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-17-6547-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 22
Research article
 | 
15 Nov 2024
Research article |  | 15 Nov 2024

PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations

Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los

Data sets

Data from the NASCENT campaign used in the publications: ``Conditions favorable for secondary ice production in Arctic mixed-phase clouds'' and ``Understanding the history of two complex ice crystal habits deduced from a holographic imager J. T. Pasquier et al. https://doi.org/10.5281/zenodo.7402285

Cloud radar Doppler spectra measured with JOYRAD-94 at AWIPEV, Ny-{\AA}lesund (June 2019--December 2020) R. Gierens et al. https://doi.org/10.1594/PANGAEA.959914

Model code and software

PEAKO code Teresa Vogl and Heike Kalesse https://github.com/ti-vo/pyPEAKO/

peakTree code Martin Radenz https://github.com/martin-rdz/peakTree

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Short summary
In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
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Special issue
Fusion of radar polarimetry and numerical atmospheric modelling...