Articles | Volume 15, issue 24
https://doi.org/10.5194/amt-15-7315-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-7315-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Dec 2022
Research article |
| 20 Dec 2022
| 20 Dec 2022
Doppler spectra from DWD's operational C-band radar birdbath scan: sampling strategy, spectral postprocessing, and multimodal analysis for the retrieval of precipitation processes
Mathias Gergely
CORRESPONDING AUTHOR
German Meteorological Service (Deutscher Wetterdienst, DWD), Observatorium Hohenpeißenberg, Hohenpeißenberg, Germany
Maximilian Schaper
German Meteorological Service (Deutscher Wetterdienst, DWD), Observatorium Hohenpeißenberg, Hohenpeißenberg, Germany
Matthias Toussaint
GAMIC Weather Radar and Signal Processing, Aachen, Germany
Michael Frech
German Meteorological Service (Deutscher Wetterdienst, DWD), Observatorium Hohenpeißenberg, Hohenpeißenberg, Germany
Related authors
Armin Blanke, Mathias Gergely, and Silke Trömel
Atmos. Chem. Phys., 25, 4167–4184, https://doi.org/10.5194/acp-25-4167-2025, https://doi.org/10.5194/acp-25-4167-2025, 2025
Short summary
Short summary
The area-wide radar-based distinction between riming and aggregation is crucial for model microphysics and data assimilation. This study introduces a discrimination algorithm based on polarimetric radar networks only. Exploiting the unique opportunity to link fall velocities from Doppler spectra to polarimetric variables in an operational setting enables us to set up and evaluate a well-performing machine learning algorithm.
Mireia Papke Chica, Valerian Hahn, Tiziana Braeuer, Elena de la Torre Castro, Florian Ewald, Mathias Gergely, Simon Kirschler, Luca Bugliaro Goggia, Stefanie Knobloch, Martina Kraemer, Johannes Lucke, Johanna Mayer, Raphael Maerkl, Manuel Moser, Laura Tomsche, Tina Jurkat-Witschas, Martin Zoeger, Christian von Savigny, and Christiane Voigt
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-255, https://doi.org/10.5194/acp-2022-255, 2022
Preprint withdrawn
Short summary
Short summary
The mixed-phase temperature regime in convective clouds challenges our understanding of microphysical and radiative cloud properties. We provide a rare and unique dataset of aircraft in situ measurements in a strong mid-latitude convective system. We find that mechanisms initiating ice nucleation and growth strongly depend on temperature, relative humidity, and vertical velocity and variate within the measured system, resulting in altitude dependent changes of the cloud liquid and ice fraction.
Silke Trömel, Clemens Simmer, Ulrich Blahak, Armin Blanke, Sabine Doktorowski, Florian Ewald, Michael Frech, Mathias Gergely, Martin Hagen, Tijana Janjic, Heike Kalesse-Los, Stefan Kneifel, Christoph Knote, Jana Mendrok, Manuel Moser, Gregor Köcher, Kai Mühlbauer, Alexander Myagkov, Velibor Pejcic, Patric Seifert, Prabhakar Shrestha, Audrey Teisseire, Leonie von Terzi, Eleni Tetoni, Teresa Vogl, Christiane Voigt, Yuefei Zeng, Tobias Zinner, and Johannes Quaas
Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, https://doi.org/10.5194/acp-21-17291-2021, 2021
Short summary
Short summary
The article introduces the ACP readership to ongoing research in Germany on cloud- and precipitation-related process information inherent in polarimetric radar measurements, outlines pathways to inform atmospheric models with radar-based information, and points to remaining challenges towards an improved fusion of radar polarimetry and atmospheric modelling.
Michael Frech, Annette M. Boehm, and Patrick Tracksdorf
EGUsphere, https://doi.org/10.5194/egusphere-2025-4957, https://doi.org/10.5194/egusphere-2025-4957, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Wind turbine clutter (WTC) reduces the accuracy of weather radar measurements, as turbines may now be built closer to radar sites. A dynamic algorithm reliably detects WTC when wind turbine rotor speeds exceed 5 rpm. Beamblockage due to WT in the 5km range is significant. A wind turbine-free 5 km radius is recommended.
Paul Ockenfuß, Michael Frech, Mathias Gergely, and Stefan Kneifel
EGUsphere, https://doi.org/10.5194/egusphere-2025-4679, https://doi.org/10.5194/egusphere-2025-4679, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
The 17 operational German weather radars regularly look vertical for calibration. We proof that this data also contains valuable scientific information. To demonstrate this, we use it to detect the melting level in clouds and strong snowflake riming. Riming is the collision of a snowflake with liquid droplets, which can create precipitation. We analyze the frequency and temperature dependence of riming for all German weather radar sites and relate it to the local precipitation climatology.
Armin Blanke, Mathias Gergely, and Silke Trömel
Atmos. Chem. Phys., 25, 4167–4184, https://doi.org/10.5194/acp-25-4167-2025, https://doi.org/10.5194/acp-25-4167-2025, 2025
Short summary
Short summary
The area-wide radar-based distinction between riming and aggregation is crucial for model microphysics and data assimilation. This study introduces a discrimination algorithm based on polarimetric radar networks only. Exploiting the unique opportunity to link fall velocities from Doppler spectra to polarimetric variables in an operational setting enables us to set up and evaluate a well-performing machine learning algorithm.
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech., 18, 1621–1640, https://doi.org/10.5194/amt-18-1621-2025, https://doi.org/10.5194/amt-18-1621-2025, 2025
Short summary
Short summary
The study examines the use of the spheroidal shape approximation for calculating cloud radar observables in rain and identifies some limitations. To address these, it introduces the empirical scattering model (ESM) based on high-quality Doppler spectra from a 94 GHz radar. The ESM offers improved accuracy and directly incorporates natural rain's microphysical effects. This new model can enhance retrieval and calibration methods, benefiting cloud radar polarimetry experts and scattering modelers.
Cornelius Hald, Maximilian Schaper, Annette Böhm, Michael Frech, Jan Petersen, Bertram Lange, and Benjamin Rohrdantz
Atmos. Meas. Tech., 17, 4695–4707, https://doi.org/10.5194/amt-17-4695-2024, https://doi.org/10.5194/amt-17-4695-2024, 2024
Short summary
Short summary
Weather radars should use lightning protection to be safe from damage, but the rods can reduce the quality of the radar measurements. This study presents three new solutions for lightning protection for weather radars and evaluates their influence on data quality. The results are compared to the current system. All tested ones have very little effect on data, and a new lightning protection system with four rods is recommended for the German Meteorological Service.
Michael Frech, Cornelius Hald, Maximilian Schaper, Bertram Lange, and Benjamin Rohrdantz
Atmos. Meas. Tech., 16, 295–309, https://doi.org/10.5194/amt-16-295-2023, https://doi.org/10.5194/amt-16-295-2023, 2023
Short summary
Short summary
Weather radar data are the backbone of a lot of meteorological products. In order to obtain a better low-level coverage with radar data, additional systems have to be included. The frequency range in which radars are allowed to operate is limited. A potential radar-to-radar interference has to be avoided. The paper derives guidelines on how additional radars can be included into a C-band weather radar network and how interferences can be avoided.
Maximilian Schaper, Michael Frech, David Michaelis, Cornelius Hald, and Benjamin Rohrdantz
Atmos. Meas. Tech., 15, 6625–6642, https://doi.org/10.5194/amt-15-6625-2022, https://doi.org/10.5194/amt-15-6625-2022, 2022
Short summary
Short summary
C-band weather radar data are commonly compromised by radio frequency interference (RFI) from external sources. It is not possible to separate a superimposed interference signal from the radar data. Therefore, the best course of action is to shut down RFI sources as quickly as possible. An automated RFI detection algorithm has been developed. Since its implementation, persistent RFI sources are eliminated much more quickly, while the number of short-lived RFI sources keeps steadily increasing.
Mireia Papke Chica, Valerian Hahn, Tiziana Braeuer, Elena de la Torre Castro, Florian Ewald, Mathias Gergely, Simon Kirschler, Luca Bugliaro Goggia, Stefanie Knobloch, Martina Kraemer, Johannes Lucke, Johanna Mayer, Raphael Maerkl, Manuel Moser, Laura Tomsche, Tina Jurkat-Witschas, Martin Zoeger, Christian von Savigny, and Christiane Voigt
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-255, https://doi.org/10.5194/acp-2022-255, 2022
Preprint withdrawn
Short summary
Short summary
The mixed-phase temperature regime in convective clouds challenges our understanding of microphysical and radiative cloud properties. We provide a rare and unique dataset of aircraft in situ measurements in a strong mid-latitude convective system. We find that mechanisms initiating ice nucleation and growth strongly depend on temperature, relative humidity, and vertical velocity and variate within the measured system, resulting in altitude dependent changes of the cloud liquid and ice fraction.
Silke Trömel, Clemens Simmer, Ulrich Blahak, Armin Blanke, Sabine Doktorowski, Florian Ewald, Michael Frech, Mathias Gergely, Martin Hagen, Tijana Janjic, Heike Kalesse-Los, Stefan Kneifel, Christoph Knote, Jana Mendrok, Manuel Moser, Gregor Köcher, Kai Mühlbauer, Alexander Myagkov, Velibor Pejcic, Patric Seifert, Prabhakar Shrestha, Audrey Teisseire, Leonie von Terzi, Eleni Tetoni, Teresa Vogl, Christiane Voigt, Yuefei Zeng, Tobias Zinner, and Johannes Quaas
Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, https://doi.org/10.5194/acp-21-17291-2021, 2021
Short summary
Short summary
The article introduces the ACP readership to ongoing research in Germany on cloud- and precipitation-related process information inherent in polarimetric radar measurements, outlines pathways to inform atmospheric models with radar-based information, and points to remaining challenges towards an improved fusion of radar polarimetry and atmospheric modelling.
Cited articles
Al-Sakka, H., Boumahmoud, A.-A., Fradon, B., Frasier, S. J., and Tabary, P.: A new fuzzy logic hydrometeor classification scheme applied to the French X-, C-, and S-band polarimetric radars, J. Appl. Meteorol. Clim., 52, 2328–2344, https://doi.org/10.1175/JAMC-D-12-0236.1, 2013. a
Bechini, R. and Chandrasekar, V.: A semisupervised robust hydrometeor
classification method for dual-polarization radar applications, J. Atmos.
Ocean. Tech., 32, 22–47, https://doi.org/10.1175/JTECH-D-14-00097.1, 2014. a
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, b
Bringi, V. N. and Chandrasekar, V.: Polarimetric Doppler weather radar: principles and applications, Cambridge University Press, ISBN 0-521-62384-7, 2001. a
Bukovcic, P., Ryzhkov, A., Zrnic, D., and Zhang, G.: Polarimetric radar
relations for quantification of snow based on disdrometer data, J. Appl.
Meteorol. Clim., 57, 103–120, https://doi.org/10.1175/JAMC-D-17-0090.1, 2018. a
Campello, R. J. G. B., Moulavi, D., and Sander, J.: Density-based clustering based on hierarchical density estimates, in: Advances in Knowledge Discovery and Data Mining. PAKDD 2013, edited by: Pei, J., Tseng, V. S., Cao, L., Motoda, H., and Xu, G., Lectue Notes in Computer Science, Springer Berlin Heidelberg, 160–172, https://doi.org/10.1007/978-3-642-37456-2_14, 2013. a
Chase, R. J., Finlon, J. A., Borque, P., McFarquhar, G. M., Nesbitt, S. W.,
Tanelli, S., Sy, O. O., Durden, S. L., and Poellot, M. R.: Evaluation of
triple-frequency radar retrieval of snowfall properties using coincident
airborne in situ observations during OLYMPEX, Geophys. Res. Lett., 45,
5752–5760, https://doi.org/10.1029/2018GL077997, 2018. a
Dolan, B. and Rutledge, S. A.: A theory-based hydrometeor identification
algorithm for X-band polarimetric radars, J. Atmos. Ocean. Tech., 26,
2071–2088, https://doi.org/10.1175/2009JTECHA1208.1, 2009. a
Dolan, B., Rutledge, S. A., Lim, S., Chandrasekar, V., and Thurai, M.: A robust C-band hydrometeor identification algorithm and application to a long-term polarimetric radar dataset, J. Appl. Meteorol. Clim., 52, 2162–2186, https://doi.org/10.1175/JAMC-D-12-0275.1, 2013. a
Frech, M. and Hubbert, J.: Monitoring the differential reflectivity and receiver calibration of the German polarimetric weather radar network, Atmos. Meas. Tech., 13, 1051–1069, https://doi.org/10.5194/amt-13-1051-2020, 2020. a, b
Frech, M. and Steinert, J.: Polarimetric radar observations during an orographic rain event, Hydrol. Earth Syst. Sci., 19, 1141–1152, https://doi.org/10.5194/hess-19-1141-2015, 2015. a, b
Frech, M., Lange, B., Mammen, T., Seltmann, J., Morehead, C., and Rowan, J.:
Influence of a radome on antenna performance, J. Atmos. Ocean. Tech., 30,
313–324, https://doi.org/10.1175/JTECH-D-12-00033.1, 2013. a
Frech, M., Hagen, M., and Mammen, T.: Monitoring the absolute calibration of a polarimetric weather radar, J. Atmos. Ocean. Tech., 34, 599–615,
https://doi.org/10.1175/JTECH-D-16-0076.1, 2017. a, b
Frech, M., Mammen, T., and Lange, B.: Pointing accuracy of an operational polarimetric weather radar, Remote Sens., 11, 1115, https://doi.org/10.3390/rs11091115,
2019. a
Garrett, T. J., Fallgatter, C., Shkurko, K., and Howlett, D.: Fall speed measurement and high-resolution multi-angle photography of hydrometeors in free fall, Atmos. Meas. Tech., 5, 2625–2633, https://doi.org/10.5194/amt-5-2625-2012, 2012. a
Gergely, M.: Sensitivity of snowfall radar reflectivity to maximum snowflake
size and implications for snowfall retrievals, J. Quant. Spectrosc. Radiat.
Transfer, 236, 106605, https://doi.org/10.1016/j.jqsrt.2019.106605, 2019. a
Gergely, M., Cooper, S. J., and Garrett, T. J.: Using snowflake surface-area-to-volume ratio to model and interpret snowfall triple-frequency radar signatures, Atmos. Chem. Phys., 17, 12011–12030, https://doi.org/10.5194/acp-17-12011-2017, 2017. a
Grazioli, J., Tuia, D., and Berne, A.: Hydrometeor classification from polarimetric radar measurements: a clustering approach, Atmos. Meas. Tech., 8, 149–170, https://doi.org/10.5194/amt-8-149-2015, 2015. a
Griffin, E. M., Schuur, T. J., and Ryzhkov, A. V.: A polarimetric analysis of ice microphysical processes in snow, using quasi-vertical profiles, J. Appl.
Meteorol. Climatol., 57, 31–50, https://doi.org/10.1175/JAMC-D-17-0033.1, 2018. a
Harris, F. J.: On the use of windows for harmonic analysis with the discrete Fourier transform, Proc. IEEE, 66, 51–83, https://doi.org/10.1109/PROC.1978.10837,
1978. a
Hartigan, J. A. and Hartigan, P. M.: The dip test of unimodality, Ann. Stat., 13, 70–84, https://www.jstor.org/stable/2241144 (last access: 15 December 2022), 1985. a
Illingworth, A. J., Hogan, R. J., O'Connor, E. J., Bouniol, D., Brooks, M. E., Delanoé, J., Donovan, D. P., Eastment, J. D., Gaussiat, N., Goddard, J. W. F., Haeffelin, M., Baltink, H. K., Krasnov, O. A., Pelon, J., Piriou, J.-M., Protat, A., Russchenberg, H. W. J., Seifert, A., Tompkins, A. M., van Zadelhoff, G.-J., Vinit, F., Willén, U., Wilson, D. R., and Wrench, C. L.: CloudNet: continuous evaluation of cloud profiles in seven operational models using ground-based observations, Bull. Am. Meteorol. Soc., 88, 883–898, https://doi.org/10.1175/BAMS-88-6-883, 2007. a
JCGM 100:2008: Evaluation of measurement data – Guide to the expression of uncertainty in measurement, Tech. Rep. 100:2008, Joint Committee for Guides in Metrology, https://www.bipm.org/en/committees/jc/jcgm/publications (last access: 15 December 2022), 2008. a
Kalesse, H., Szyrmer, W., Kneifel, S., Kollias, P., and Luke, E.: Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling, Atmos. Chem. Phys., 16, 2997–3012, https://doi.org/10.5194/acp-16-2997-2016, 2016. a, b
Kaltenboeck, R. and Ryzhkov, A.: A freezing rain storm explored with a C-band polarimetric weather radar using the QVP methodology, Meteorol. Z., 26, 207–222, https://doi.org/10.1127/metz/2016/0807, 2016. a
Kleine, J., Voigt, C., Sauer, D., Schlager, H., Scheibe, M., Kaufmann, S., Jurkat-Witschas, T., Kärcher, B., and Anderson, B.: In situ observations
of ice particle losses in a young persistent contrail, Geophys. Res. Lett.,
45, 13553–13561, https://doi.org/10.1029/2018GL079390, 2018. a
Kneifel, S., von Lerber, A., Tiira, J., Moisseev, D., Kollias, P., and
Leinonen, J.: Observed relations between snowfall microphysics and
triple-frequency radar measurements, J. Geophys. Res.-Atmos., 120,
6034–6055, https://doi.org/10.1002/2015JD023156, 2015. a
Kollias, P., Clothiaux, E. E., Miller, M. A., Luke, E. P., Johnson, K. L., Moran, K. P., Widener, K. B., and Albrecht, B. A.: The Atmospheric Radiation Measurement Program cloud profiling radars: second-generation
sampling strategies, processing, and cloud data products, J. Atmos. Ocean.
Tech., 24, 1199–1214, https://doi.org/10.1175/JTECH2033.1, 2007. a, b, c, d, e
Kumjian, M. R. and Lombardo, K. A.: Insights into the evolving microphysical
and kinematic structure of northeastern U.S. winter storms from
dual-polarization Doppler radar, Mon. Weather Rev., 145, 1033–1061,
https://doi.org/10.1175/MWR-D-15-0451.1, 2017. a
Kumjian, M. R., Rutledge, S. A., Rasmussen, R. M., Kennedy, P. C., and Dixon, M.: High-resolution polarimetric radar observations of snow-generating cells,
J. Appl. Meteorol. Clim., 53, 1636–1658, https://doi.org/10.1175/JAMC-D-13-0312.1,
2014. a
Kumjian, M. R., Mishra, S., Giangrande, S. E., Toto, T., Ryzhkov, A. V., and
Bansemer, A.: Polarimetric radar and aircraft observations of saggy bright
bands during MC3E, J. Geophys. Res.-Atmos., 121, 3584–3607,
https://doi.org/10.1002/2015JD024446, 2016. a
Kumjian, M. R., Tobin, D. M., Oue, M., and Kollias, P.: Microphysical insights into ice pellet formation revealed by fully polarimetric Ka-band Doppler radar, J. Appl. Meteorol. Clim., 59, 1557–1580,
https://doi.org/10.1175/JAMC-D-20-0054.1, 2020. a
Li, H., Moisseev, D., and von Lerber, A.: How does riming affect dual-polarization radar observations and snowflake shape?, J. Geophys. Res.-Atmos., 123, 6070–6081, https://doi.org/10.1029/2017JD028186, 2018. a
Luke, E. P., Kollias, P., and Shupe, M. D.: Detection of supercooled liquid in mixed-phase clouds using radar Doppler spectra, J. Geophys. Res., 115,
D19201, https://doi.org/10.1029/2009JD012884, 2010. a
Maahn, M. and Kollias, P.: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing, Atmos. Meas. Tech., 5, 2661–2673, https://doi.org/10.5194/amt-5-2661-2012, 2012. a
Maahn, M. and Löhnert, U.: Potential of higher-order moments and slopes of the radar Doppler spectrum for retrieving microphysical and kinematic
properties of Arctic ice clouds, J. Appl. Meteorol. Clim., 56,
263–282, https://doi.org/10.1175/JAMC-D-16-0020.1, 2017. a, b, c
Marzano, F. S., Scaranari, D., and Vulpiani, G.: Supervised fuzzy-logic
classification of hydrometeors using C-band weather radars, IEEE T.
Geosci. Remote, 45, 3784–3799, https://doi.org/10.1109/TGRS.2007.903399, 2007. a
Marzano, F. S., Scaranari, D., Montpoli, M., and Vulpiani, G.: Supervised
classification and estimation of hydrometeors from C-band dual-polarized
radars: a Bayesian approach, IEEE T. Geosci. Remote, 46, 85–98,
https://doi.org/10.1109/TGRS.2007.906476, 2008. a
Marzano, F. S., Botta, G., and Montopoli, M.: Iterative Bayesian retrieval of hydrometeor content from X-band polarimetric weather radar, IEEE T.
Geosci. Remote, 48, 3059–3074, https://doi.org/10.1109/TGRS.2010.2045231, 2010. a
Matrosov, S. Y., Reinking, R. F., and Djalalova, I. V.: Inferring fall
attitudes of pristine dendritic crystals from polarimetric radar data, J.
Atmos. Sci., 62, 241–250, https://doi.org/10.1175/JAS-3356.1, 2005. a
Maurus, S. and Plant, C.: Skinny-dip: clustering in a sea of noise, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'16, San Francisco, California, USA, 13–17 August 2016, Association for Computing Machinery, 1055–1064, https://doi.org/10.1145/2939672.2939740, 2016. a
McInnes, L., Healy, J., and Astels, S.: hdbscan: hierarchical density based
clustering, Journal of Open Source Software, 2, 205,
https://doi.org/10.21105/joss.00205, 2017. a
Melnikov, V.: Parameters of cloud ice particles retrieved from radar data, J.
Atmos. Ocean. Tech., 34, 717–728, https://doi.org/10.1175/JTECH-D-16-0123.1, 2017. a
Melnikov, V. and Straka, J. M.: Axis ratios and flutter angles of cloud ice
particles: retrievals from radar data, J. Atmos. Ocean. Tech., 30,
1691–1703, https://doi.org/10.1175/JTECH-D-12-00212.1, 2013. a
Moisseev, D., von Lerber, A., and Tiira, J.: Quantifying the effect of riming
on snowfall using ground-based observations, J. Geophys. Res.-Atmos., 122,
4019–4037, https://doi.org/10.1002/2016JD026272, 2017. a
Moisseev, D. N. and Chandrasekar, V.: Polarimetric spectral filter for adaptive clutter and noise suppression, J. Atmos. Ocean. Tech., 26, 215–228,
https://doi.org/10.1175/2008JTECHA1119.1, 2009. a, b
Moisseev, D. N., Lautaportti, S., Tyynela, J., and Lim, S.: Dual-polarization
radar signatures in snowstorms: role of snowflake aggregation, J. Geophys.
Res.-Atmos., 120, 12644–12655, https://doi.org/10.1002/2015JD023884, 2015. a, b
Myagkov, A., Seifert, P., Bauer-Pfundstein, M., and Wandinger, U.: Cloud radar with hybrid mode towards estimation of shape and orientation of ice crystals, Atmos. Meas. Tech., 9, 469–489, https://doi.org/10.5194/amt-9-469-2016, 2016. a
Nesbitt, S. W., Salio, P. V., Ávila, E., et al.: A storm safari in subtropical South America: proyecto RELAMPAGO, B. Am. Meteorol. Soc., 102, E1621–E1644, https://doi.org/10.1175/BAMS-D-20-0029.1, 2021. a
Oue, M., Kollias, P., Ryzhkov, A., and Luke, E. P.: Toward exploring the
synergy between cloud radar polarimetry and Doppler spectral analysis in
deep cold precipitating systems in the Arctic, J. Geophys. Res.-Atmos.,
123, 2797–2815, https://doi.org/10.1002/2017JD027717, 2018. a, b
Park, H., Ryzhkov, A. V., Zrnic, D. S., and Kim, K.-E.: The hydrometeor
classification algorithm for the polarimetric WSR-88D: description and
application to an MCS, Weather Forecast., 24, 730–748,
https://doi.org/10.1175/2008WAF2222205.1, 2009. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, https://scikit-learn.org/stable/ (last access: 15 December 2022), 2011. a
Petty, G. W. and Huang, W.: The modified Gamma size distribution applied to inhomogeneous and nonspherical particles: key relationships and conversions,
J. Atmos. Sci., 68, 1460–1473, https://doi.org/10.1175/2011JAS3645.1, 2011. a
Pucik, T., Castellano, C., Groenemeijer, P., Kühne, T., Rädler, A. T.,
Antonescu, B., and Faust, E.: Large hail incidence and its economic and
societal impacts across Europe, Mon. Weather Rev., 147, 3901–3916,
https://doi.org/10.1175/MWR-D-19-0204.1, 2019. a
Ryzhkov, A., Zhang, P., Reeves, H., Kumjian, M., Tschallener, T., Trömel,
S., and Simmer, C.: Quasi-vertical profiles – a new way to look at
polrimetric radar data, J. Atmos. Ocean. Tech., 33, 551–562,
https://doi.org/10.1175/JTECH-D-15-0020.1, 2016. a, b
Ryzhkov, A. V. and Zrnic, D. S.: Radar polarimetry for weather observations, Springer, Cham, ISBN 978-3-030-05093-1, https://doi.org/10.1007/978-3-030-05093-1, 2019. a, b, c
Savitzky, A. and Golay, M. J. E.: Smoothing and differentiation of data by
simplified least squares procedures, Anal. Chem., 36, 1627–1639,
https://doi.org/10.1021/ac60214a047, 1964. a
Shupe, M. D., Kollias, P., Matrosov, S. Y., and Schneider, T. L.: Deriving mixed-phase cloud properties from Doppler radar spectra, J. Atmos. Ocean. Tech., 21, 660–670, https://doi.org/10.1175/1520-0426(2004)021<0660:DMCPFD>2.0.CO;2, 2004. a
Siggia, A. D. and Passarelli, R. E.: Gaussian model adaptive processing (GMAP) for improved ground clutter cancellation and moment calculation, in: Proc. 3rd European Conf. on Radar Meteorology (ERAD), Visby, Sweden, 6–10 September 2004, 67–73, https://www.copernicus.org/erad/2004/online/ERAD04_P_67.pdf (last access: 15 December 2022), 2004. a
Singh, D. K., Donovan, S., Pardyjak, E. R., and Garrett, T. J.: A differential emissivity imaging technique for measuring hydrometeor mass and type, Atmos. Meas. Tech., 14, 6973–6990, https://doi.org/10.5194/amt-14-6973-2021, 2021. a
Steinert, J., Tracksdorf, P., and Heizenreder, D.: Hymec: surface precipitation type estimation at the German Weather Service, Weather Forecast., 36, 1611–1627, https://doi.org/10.1175/WAF-D-20-0232.1, 2021. a, b
Thompson, E. J., Rutledge, S. A., Dolan, B., Chandrasekar, V., and Cheong,
B. L.: A dual-polarization radar hydrometeor classification algorithm for
winter precipitation, J. Atmos. Ocean. Tech., 31, 1457–1481,
https://doi.org/10.1175/JTECH-D-13-00119.1, 2014. a
Trömel, S., Ryzhkov, A. V., Zhang, P., and Simmer, C.: Investigations of
backscatter differential phase in the melting layer, J. Appl. Meteorol.
Clim., 53, 2344–2359, https://doi.org/10.1175/JAMC-D-14-0050.1, 2014. a
Trömel, S., Simmer, C., Blahak, U., Blanke, A., Doktorowski, S., Ewald, F., Frech, M., Gergely, M., Hagen, M., Janjic, T., Kalesse-Los, H., Kneifel, S., Knote, C., Mendrok, J., Moser, M., Köcher, G., Mühlbauer, K., Myagkov, A., Pejcic, V., Seifert, P., Shrestha, P., Teisseire, A., von Terzi, L., Tetoni, E., Vogl, T., Voigt, C., Zeng, Y., Zinner, T., and Quaas, J.: Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes, Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, 2021. a, b, c, d
Virtanen, P., Gommers, R., Oliphant, T. E., et al.: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020. a, b
Vivekanandan, J., Zhang, G., and Brandes, E.: Polarimetric radar estimators
based on a constrained gamma drop size distribution model, J. Appl.
Meteorol., 43, 217–230, 2004. a
Vogel, J. M. and Fabry, F.: Contrasting polarimetric observations of stratiform riming and nonriming events, J. Appl. Meteorol. Clim., 57, 457–476, https://doi.org/10.1175/JAMC-D-16-0370.1, 2018. a
Vogl, T., Maahn, M., Kneifel, S., Schimmel, W., Moisseev, D., and Kalesse-Los, H.: Using artificial neural networks to predict riming from Doppler cloud radar observations, Atmos. Meas. Tech., 15, 365–381, https://doi.org/10.5194/amt-15-365-2022, 2022. a
Voigt, C., Lelieveld, J., Schlager, H., Schneider, J., Sauer, D., Meerkötter, R., Pöhlker, M., Bugliaro, L., Curtius, J., Erbertseder, T., Hahn, V., Jöckel, P., Li, Q., Marsing, A., Mertens, M., Pöhlker, C., Pöschl, U., Pozzer, A., Tomsche, L., and Schumann, U.: Aerosol and Cloud Changes during the Corona Lockdown in 2020 – First highlights from the BLUESKY campaign, EGU General Assembly 2021, online, 19–30 April 2021, EGU21-13134, https://doi.org/10.5194/egusphere-egu21-13134, 2021.
a
Yu, T.-Y., Xiao, X., and Wang, Y.: Statistical quality of spectral polarimetric variables for weather radar, J. Atmos. Ocean. Tech., 29, 1221–1235, https://doi.org/10.1175/JTECH-D-11-00090.1, 2012. a
Yuter, S. E., Kingsmill, D. E., Nance, L. B., and Löffler-Mang, M.:
Observations of precipitation size and fall speed characteristics within
coexisting rain and wet snow, J. Appl. Meteorol. Clim., 45, 1450–1464,
2006. a
Zängl, G., Reinert, D., Ripodas, P., and Baldauf, M.: The ICON
(ICOsahedral Non-hydrostatic) modelling framework of DWD and MPI-M:
description of the non-hydrostatic dynamical core, Q. J. Roy. Meteor. Soc.,
141, 563–579, https://doi.org/10.1002/qj.2378, 2015. a, b
Zhang, C., Mapes, B. E., and Soden, B. J.: Bimodality in tropical water vapour, Q. J. Roy. Meteor. Soc., 129, 2847–2866, https://doi.org/10.1256/qj.02.166, 2003. a, b
Zipser, E. J., Cecil, D. J., Liu, C., Nesbitt, S. W., and Yorty, D. P.: Where are the most intense thunderstorms on Earth?, B. Am. Meteorol. Soc., 87,
1057–1072, https://doi.org/10.1175/BAMS-87-8-1057, 2006. a
Short summary
This study presents the new vertically pointing birdbath scan of the German C-band radar network, which provides high-resolution profiles of precipitating clouds above all DWD weather radars since the spring of 2021. Our AI-based postprocessing method for filtering and analyzing the recorded radar data offers a unique quantitative view into a wide range of precipitation events from snowfall over stratiform rain to intense frontal showers and will be used to complement DWD's operational services.
This study presents the new vertically pointing birdbath scan of the German C-band radar...
