Articles | Volume 17, issue 22
https://doi.org/10.5194/amt-17-6547-2024
https://doi.org/10.5194/amt-17-6547-2024
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

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Cited articles

Barrett, A. I., Hogan, R. J., and Forbes, R. M.: Why are mixed-phase altocumulus clouds poorly predicted by large-scale models? Part 1. Physical processes, J. Geophys. Res.-Atmos., 122, 9903–9926, https://doi.org/10.1002/2016JD026321, 2017. a
Barrientos-Velasco, C., Deneke, H., Hünerbein, A., Griesche, H. J., Seifert, P., and Macke, A.: Radiative closure and cloud effects on the radiation budget based on satellite and shipborne observations during the Arctic summer research cruise, PS106, Atmos. Chem. Phys., 22, 9313–9348, https://doi.org/10.5194/acp-22-9313-2022, 2022. a
Billault-Roux, A.-C., Georgakaki, P., Gehring, J., Jaffeux, L., Schwarzenboeck, A., Coutris, P., Nenes, A., and Berne, A.: Distinct secondary ice production processes observed in radar Doppler spectra: insights from a case study, Atmos. Chem. Phys., 23, 10207–10234, https://doi.org/10.5194/acp-23-10207-2023, 2023. a, b
Borque, P., Luke, E., and Kollias, P.: On the unified estimation of turbulence eddy dissipation rate using Doppler cloud radars and lidars, J. Geophys. Res.-Atmos., 121, 5972–5989, https://doi.org/10.1002/2015jd024543, 2016. a, b
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K., Sherwood, S., Stevens, B., and Zhang, X. Y.: Clouds and aerosols, Cambridge University Press, Cambridge, UK, https://doi.org/10.1017/CBO9781107415324.016, pp. 571–657, 2013. a
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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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