Articles | Volume 21, issue 22
https://doi.org/10.5194/acp-21-17133-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-17133-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Nov 2021
Research article |
| 25 Nov 2021
| 25 Nov 2021
Improving the representation of aggregation in a two-moment microphysical scheme with statistics of multi-frequency Doppler radar observations
Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Axel Seifert
Deutscher Wetterdienst, Offenbach, Germany
Davide Ori
Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Stefan Kneifel
Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
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Paul Ockenfuß, Michael Frech, Mathias Gergely, and Stefan Kneifel
Atmos. Meas. Tech., 19, 2125–2147, https://doi.org/10.5194/amt-19-2125-2026, https://doi.org/10.5194/amt-19-2125-2026, 2026
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The 17 operational German weather radars regularly point 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.
Gholam Ali Hoshyaripour, Andreas Baer, Sascha Bierbauer, Julia Bruckert, Dominik Brunner, Jochen Förstner, Arash Hamzehloo, Valentin Hanft, Corina Keller, Martina Klose, Pankaj Kumar, Patrick Ludwig, Enrico Metzner, Lisa Muth, Andreas Pauling, Nikolas Porz, Maryam Ramezani Ziarani, Thomas Reddmann, Luca Reißig, Roland Ruhnke, Khompat Satitkovitchai, Axel Seifert, Miriam Sinnhuber, Michael Steiner, Stefan Versick, Heike Vogel, Michael Weimer, Sven Werchner, and Corinna Hoose
Geosci. Model Dev., 19, 1645–1681, https://doi.org/10.5194/gmd-19-1645-2026, https://doi.org/10.5194/gmd-19-1645-2026, 2026
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This paper presents recent advances in ICON-ART, a modeling system that simulates atmospheric composition – such as gases and particles – and their interactions with weather and climate. By integrating updated chemistry, emissions, and aerosol processes, ICON-ART enables detailed, scale-spanning simulations. It supports both scientific research and operational forecasts, contributing to improved air quality, weather and climate predictions.
Leonie von Terzi, Davide Ori, and Stefan Kneifel
Geosci. Model Dev., 19, 887–910, https://doi.org/10.5194/gmd-19-887-2026, https://doi.org/10.5194/gmd-19-887-2026, 2026
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We present a new database of radar-relevant optical properties for a wide range of ice particle shapes, computed using the Discrete Dipole Approximation (DDA) at 5.6, 9.6, 35.6, and 94 GHz. The database is designed to support habit-evolving microphysical schemes, which predict continuous changes in ice particle properties rather than the traditionally assumed fixed categories. It includes over 2600 individual crystals and 450 aggregates with varying riming degrees and morphology.
Paul Ockenfuß, Gregor Köcher, Matthias Bauer-Pfundstein, and Stefan Kneifel
EGUsphere, https://doi.org/10.5194/egusphere-2025-5690, https://doi.org/10.5194/egusphere-2025-5690, 2026
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The pointing of scanning weather and cloud radars must be very accurate, to within 0.1°. We present a general method to calibrate the pointing of a scanning radar with an elevation and an azimuth axis. Using the Sun as a reference, we can determine the specific misalignments of the radar pedestal, axes and antenna. Once the misalignments are known, we can correct for them by steering the scanner motors accordingly. We provide recommendations and code to apply this method to any scanning radar.
Ulrich Schumann and Axel Seifert
Atmos. Chem. Phys., 25, 18571–18597, https://doi.org/10.5194/acp-25-18571-2025, https://doi.org/10.5194/acp-25-18571-2025, 2025
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Contrails caused by aircraft flying in cold and humid air masses, impact weather and climate. For reduction of the climate impact, one should avoid flights forming warming contrails. This requires good weather forecast of contrail formation conditions and contrail effects. We present a two-way coupling of the Contrail Cirrus Prediction model (CoCiP) with the global Icosahedral Non-hydrostatic numerical weather model (ICON). We find that contrails are predictable – but only for a finite period.
Maleen Hanst, Carmen G. Köhler, Axel Seifert, and Linda Schlemmer
Atmos. Chem. Phys., 25, 17253–17274, https://doi.org/10.5194/acp-25-17253-2025, https://doi.org/10.5194/acp-25-17253-2025, 2025
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Persistent condensation trails typically occur due to aircraft flying through certain cold and humid regions. In most cases, these contrails have a warming impact on the climate. Predicting these regions in advance allows flight planners to re-route airplanes. We show that an adaptation of the ice microphysics scheme in a certain weather forecast model improves the prediction of these regions. Running an ensemble of simulations with this scheme improves the prediction quality even further.
Manfred Wendisch, Benjamin Kirbus, Davide Ori, Matthew D. Shupe, Susanne Crewell, Harald Sodemann, and Vera Schemann
Atmos. Chem. Phys., 25, 15047–15076, https://doi.org/10.5194/acp-25-15047-2025, https://doi.org/10.5194/acp-25-15047-2025, 2025
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Aircraft observations of air parcels moving into and out of the Arctic are reported. From the data, heating and cooling as well as drying and moistening of the air masses along their way into and out of the Arctic could be measured for the first time. These data are used to evaluate if weather prediction models are able to accurately represent these air mass transformations. This work helps to model the future Arctic climate changes, which may have an impact for mid-latitude weather as well.
Anton Kötsche, Alexander Myagkov, Leonie von Terzi, Maximilian Maahn, Veronika Ettrichrätz, Teresa Vogl, Alexander Ryzhkov, Petar Bukovcic, Davide Ori, and Heike Kalesse-Los
Atmos. Chem. Phys., 25, 14045–14070, https://doi.org/10.5194/acp-25-14045-2025, https://doi.org/10.5194/acp-25-14045-2025, 2025
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Our study combines radar observations of snow with snowfall camera observations on the ground to enhance our understanding of radar variables and snowfall properties. We found that values of an important radar variable (KDP) can be related to many different snow particle properties and number concentrations. We were able to constrain which particle sizes contribute to KDP by using computer models of snowflakes and showed which microphysical processes during snow formation can influence KDP.
Henning Dorff, Florian Ewald, Heike Konow, Mario Mech, Davide Ori, Vera Schemann, Andreas Walbröl, Manfred Wendisch, and Felix Ament
Atmos. Chem. Phys., 25, 8329–8354, https://doi.org/10.5194/acp-25-8329-2025, https://doi.org/10.5194/acp-25-8329-2025, 2025
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Using observations of an Arctic atmospheric river (AR) from a long-range research aircraft, we analyse how moisture transported into the Arctic by the AR is transformed and how it interacts with the Arctic environment. The moisture transport divergence is the main driver of local moisture change over time. Surface precipitation and evaporation are rather weak when averaged over extended AR sectors, although considerable heterogeneity of precipitation within the AR is observed.
Tim Lüttmer, Peter Spichtinger, and Axel Seifert
Atmos. Chem. Phys., 25, 4505–4529, https://doi.org/10.5194/acp-25-4505-2025, https://doi.org/10.5194/acp-25-4505-2025, 2025
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We investigate ice formation pathways in idealized convective clouds using a novel microphysics scheme that distinguishes between five ice classes each with their own unique formation mechanism. Ice crystals from rime splintering form the lowermost layer of ice crystals around the updraft core. The majority of ice crystals in the anvil of the convective cloud stems from frozen droplets. Ice stemming from homogeneous and deposition nucleation was only relevant in the overshoot.
Theresa Kiszler, Davide Ori, and Vera Schemann
Atmos. Chem. Phys., 24, 10039–10053, https://doi.org/10.5194/acp-24-10039-2024, https://doi.org/10.5194/acp-24-10039-2024, 2024
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Microphysical processes impact the phase-partitioning of clouds. In this study we evaluate these processes while focusing on low-level Arctic clouds. To achieve this we used an extensive simulation set in combination with a new diagnostic tool. This study presents our findings on the relevance of these processes and their behaviour under different thermodynamic regimes.
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona M. Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schaefer, Vera Schemann, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
Atmos. Chem. Phys., 24, 8865–8892, https://doi.org/10.5194/acp-24-8865-2024, https://doi.org/10.5194/acp-24-8865-2024, 2024
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The Arctic is warming faster than the rest of the globe. Warm-air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold-air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to mid-latitude weather. In our article, we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented with state-of-the-art remote-sensing and in situ measurement devices.
Giovanni Chellini, Rosa Gierens, Kerstin Ebell, Theresa Kiszler, Pavel Krobot, Alexander Myagkov, Vera Schemann, and Stefan Kneifel
Earth Syst. Sci. Data, 15, 5427–5448, https://doi.org/10.5194/essd-15-5427-2023, https://doi.org/10.5194/essd-15-5427-2023, 2023
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We present a comprehensive quality-controlled dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region, and substantially warm the surface. However, our understanding of microphysical processes in these clouds is incomplete. This dataset includes a comprehensive set of variables which allow for extensive investigation of such processes in LLMPCs at the site.
Axel Seifert, Vanessa Bachmann, Florian Filipitsch, Jochen Förstner, Christian M. Grams, Gholam Ali Hoshyaripour, Julian Quinting, Anika Rohde, Heike Vogel, Annette Wagner, and Bernhard Vogel
Atmos. Chem. Phys., 23, 6409–6430, https://doi.org/10.5194/acp-23-6409-2023, https://doi.org/10.5194/acp-23-6409-2023, 2023
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We investigate how mineral dust can lead to the formation of cirrus clouds. Dusty cirrus clouds lead to a reduction in solar radiation at the surface and, hence, a reduced photovoltaic power generation. Current weather prediction systems are not able to predict this interaction between mineral dust and cirrus clouds. We have developed a new physical description of the formation of dusty cirrus clouds. Overall we can show a considerable improvement in the forecast quality of clouds and radiation.
Frederic Tridon, Israel Silber, Alessandro Battaglia, Stefan Kneifel, Ann Fridlind, Petros Kalogeras, and Ranvir Dhillon
Atmos. Chem. Phys., 22, 12467–12491, https://doi.org/10.5194/acp-22-12467-2022, https://doi.org/10.5194/acp-22-12467-2022, 2022
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The role of ice precipitation in the Earth water budget is not well known because ice particles are complex, and their formation involves intricate processes. Riming of ice crystals by supercooled water droplets is an efficient process, but little is known about its importance at high latitudes. In this work, by exploiting the deployment of an unprecedented number of remote sensing systems in Antarctica, we find that riming occurs at much lower temperatures compared with the mid-latitudes.
Leonie von Terzi, José Dias Neto, Davide Ori, Alexander Myagkov, and Stefan Kneifel
Atmos. Chem. Phys., 22, 11795–11821, https://doi.org/10.5194/acp-22-11795-2022, https://doi.org/10.5194/acp-22-11795-2022, 2022
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We present a statistical analysis of ice microphysical processes (IMP) in mid-latitude clouds. Combining various radar approaches, we find that the IMP active at −20 to −10 °C seems to be the main driver of ice particle size, shape and concentration. The strength of aggregation at −20 to −10 °C correlates with the increase in concentration and aspect ratio of locally formed ice particles. Despite ongoing aggregation, the concentration of ice particles stays enhanced until −4 °C.
Alexander Myagkov and Davide Ori
Atmos. Meas. Tech., 15, 1333–1354, https://doi.org/10.5194/amt-15-1333-2022, https://doi.org/10.5194/amt-15-1333-2022, 2022
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This study provides equations to characterize random errors of spectral polarimetric observations from cloud radars. The results can be used for a broad spectrum of applications. For instance, accurate error characterization is essential for advanced retrievals of microphysical properties of clouds and precipitation. Moreover, error characterization allows for the use of measurements from polarimetric cloud radars to potentially improve weather forecasts.
Teresa Vogl, Maximilian Maahn, Stefan Kneifel, Willi Schimmel, Dmitri Moisseev, and Heike Kalesse-Los
Atmos. Meas. Tech., 15, 365–381, https://doi.org/10.5194/amt-15-365-2022, https://doi.org/10.5194/amt-15-365-2022, 2022
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We are using machine learning techniques, a type of artificial intelligence, to detect graupel formation in clouds. The measurements used as input to the machine learning framework were performed by cloud radars. Cloud radars are instruments located at the ground, emitting radiation with wavelenghts of a few millimeters vertically into the cloud and measuring the back-scattered signal. Our novel technique can be applied to different radar systems and different weather conditions.
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
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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
Andrić, J., Kumjian, M. R., Zrnić, D. S., Straka, J. M., and
Melnikov, V. M.: Polarimetric signatures above the melting layer in winter
storms: An observational and modeling study, J. Appl. Meteorol. Clim., 52, 682–700, https://doi.org/10.1175/JAMC-D-12-028.1, 2013. a
Barthazy, E. and Schefold, R.: Fall velocity of snowflakes of different riming
degree and crystal types, Atmos. Res., 82, 391–398,
https://doi.org/10.1016/j.atmosres.2005.12.009, 2006. a
Battaglia, A., Westbrook, C. D., Kneifel, S., Kollias, P., Humpage, N., Löhnert, U., Tyynelä, J., and Petty, G. W.: G band atmospheric radars: new frontiers in cloud physics, Atmos. Meas. Tech., 7, 1527–1546, https://doi.org/10.5194/amt-7-1527-2014, 2014. a
Battaglia, A., Tanelli, S., Tridon, F., Kneifel, S., Leinonen, J., and Kollias,
P.: Triple-Frequency Radar Retrievals, Adv. Glob. Change Res.,
67, 211–229, https://doi.org/10.1007/978-3-030-24568-9_13, 2020. a
Böhm, J. P.: A general hydrodynamic theory for mixed-phase microphysics.
Part I: drag and fall speed of hydrometeors, Atmos. Res., 27,
253–274, https://doi.org/10.1016/0169-8095(92)90035-9, 1992. a
Connolly, P. J., Emersic, C., and Field, P. R.: A laboratory investigation into the aggregation efficiency of small ice crystals, Atmos. Chem. Phys., 12, 2055–2076, https://doi.org/10.5194/acp-12-2055-2012, 2012. a, b, c
Dias Neto, J., Kneifel, S., Ori, D., Trömel, S., Handwerker, J., Bohn, B., Hermes, N., Mühlbauer, K., Lenefer, M., and Simmer, C.: The TRIple-frequency and Polarimetric radar Experiment for improving process observations of winter precipitation, Earth Syst. Sci. Data, 11, 845–863, https://doi.org/10.5194/essd-11-845-2019, 2019. a
Dipankar, A., Stevens, B., Heinze, R., Moseley, C., Zängl, G., Giorgetta,
M., and Brdar, S.: Large eddy simulation using the general circulation model
ICON, J. Adv. Model. Earth Sys., 7, 963–986,
https://doi.org/10.1002/2015MS000431, 2015. a
Dunnavan, E. L.: How snow aggregate ellipsoid shape and orientation
variability affects fall speed and self-aggregation rates, J. Atmos. Sci., 78, 51–73, https://doi.org/10.1175/JAS-D-20-0128.1, 2021. a
Field, P. R.: Bimodal ice spectra in frontal clouds, Q. J. Roy. Meteor. Soc., 126, 379–392, https://doi.org/10.1002/qj.49712656302,
2000. a
Field, P. R., Hogan, R. J., Brown, P. R., Illingworth, A. J., Choularton,
T. W., and Cotton, R. J.: Parametrization of ice-particle size distributions
for mid-latitude stratiform cloud, Q. J. Roy. Meteor. Soc., 131, 1997–2017, https://doi.org/10.1256/qj.04.134, 2005. a
Gillespie, D. T.: Three Models for the Coalescence Growth of Cloud Drops.,
J. Atmos. Sci., 32, 600–607,
https://doi.org/10.1175/1520-0469(1975)032<0600:TMFTCG>2.0.CO;2, 1975. a
Hashino, T. and Tripoli, G. J.: The Spectral Ice Habit Prediction System
(SHIPS). Part IV: Box model simulations of the habit-dependent aggregation
process, J. Atmos. Sci., 68, 1142–1161,
https://doi.org/10.1175/2011JAS3667.1, 2011. a
Heinze, R., Dipankar, A., Henken, C. C., Moseley, C., Sourdeval, O.,
Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C.,
Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S.,
Deneke, H., Di Girolamo, P., Evaristo, R., Fischer, J., Frank, C.,
Friederichs, P., Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen,
A., Hege, H. C., Hoose, C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S.,
Knippertz, P., Kuhn, A., van Laar, T., Macke, A., Maurer, V., Mayer, B.,
Meyer, C. I., Muppa, S. K., Neggers, R. A., Orlandi, E., Pantillon, F.,
Pospichal, B., Röber, N., Scheck, L., Seifert, A., Seifert, P., Senf,
F., Siligam, P., Simmer, C., Steinke, S., Stevens, B., Wapler, K., Weniger,
M., Wulfmeyer, V., Zängl, G., Zhang, D., and Quaas, J.: Large-eddy
simulations over Germany using ICON: a comprehensive evaluation, Q. J. Roy. Meteor. Soc., 143, 69–100,
https://doi.org/10.1002/qj.2947, 2017. a
Heymsfield, A. J. and Westbrook, C. D.: Advances in the estimation of ice
particle fall speeds using laboratory and field measurements, J. Atmos. Sci., 67, 2469–2482, https://doi.org/10.1175/2010JAS3379.1, 2010. a
Heymsfield, A. J., Schmitt, C., Chen, C. C. J., Bansemer, A., Gettelman, A.,
Field, P. R., and Liu, C.: Contributions of the Liquid and Ice Phases to
Global Surface Precipitation: Observations and Global Climate Modeling,
J. Atmos. Sci., 77, 2629–2648,
https://doi.org/10.1175/JAS-D-19-0352.1, 2020. a
Hobbs, P. V., Chang, S., and Locatelli, J. D.: The dimensions and aggregation
of ice crystals in natural clouds, J. Geophys. Res., 79,
2199–2206, https://doi.org/10.1029/jc079i015p02199, 1974. a
Hogan, R. J. and Westbrook, C. D.: Equation for the microwave backscatter
cross section of aggregate snowflakes using the self-similar Rayleigh-Gans
approximation, J. Atmos. Sci., 71, 3292–3301,
https://doi.org/10.1175/JAS-D-13-0347.1, 2014. a
Hogan, R. J., Illingworth, A. J., and Sauvageot, H.: Measuring crystal size in
cirrus using 35- and 94-GHz radars, J. Atmos. Ocean. Tech., 17, 27–37,
https://doi.org/10.1175/1520-0426(2000)017<0027:MCSICU>2.0.CO;2, 2000. a
Hogan, R. J., Honeyager, R., Tyynelä, J., and Kneifel, S.: Calculating
the millimetre-wave scattering phase function of snowflakes using the
self-similar Rayleigh–Gans Approximation, Q. J. Roy. Meteor. Soc., 143, 834–844, https://doi.org/10.1002/qj.2968, 2017. a
Hosler, C. L. and Hallgren, R. E.: The aggregation of small ice crystals,
Discussions of the Faraday Society, 30, 200–207, https://doi.org/10.1039/DF9603000200,
1960. a
Illingworth, A. J., Hogan, R. J., O'Connor, E. J., Bouniol, D., Brooks, M. E.,
Delanoë, J., Donovan, D. P., Eastment, J. D., Gaussiat, N., Goddard,
J. W., Haeffelin, M., Klein Baltinik, H., Krasnov, O. A., Pelon, J.,
Piriou, J. M., Protat, A., Russchenberg, H. W., Seifert, A., Tompkins, A. M.,
van Zadelhoff, G. J., Vinit, F., Willen, U., Wilson, D. R., and Wrench,
C. L.: Cloudnet: Continuous evaluation of cloud profiles in seven
operational models using ground-based observations, B. Am. Meteorol. Soc., 88, 883–898, https://doi.org/10.1175/BAMS-88-6-883, 2007. a
Kajikawa, M. and Heymsfield, A. J.: Aggregation of ice crystals in cirrus,
https://doi.org/10.1175/1520-0469(1989)046<3108:AOICIC>2.0.CO;2, 1989. a, b
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
Kanji, Z. A., Ladino, L. A., Wex, H., Boose, Y., Burkert-Kohn, M., Cziczo,
D. J., and Krämer, M.: Overview of Ice Nucleating Particles,
Meteorol. Monogr., 58, 1–33,
https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0006.1, 2017. a
Karrer, M., Seifert, A., Siewert, C., Ori, D., von Lerber, A., and Kneifel, S.:
Ice Particle Properties Inferred From Aggregation Modelling, J.
Adv. Model. Earth Sys., 12, e2020MS002066, https://doi.org/10.1029/2020MS002066, 2020. a, b, c
Karrer, M.: ICON-LEM version used for the publication “Improving the Representation of Aggregation in a Two-moment Microphysical Scheme with Statistics of Multi-frequency Doppler Radar Observations”, Zenodo [code], https://doi.org/10.5281/zenodo.4740092, 2021. a
Keith, W. D. and Saunders, C. P.: The collection efficiency of a cylindrical
target for ice crystals, Atmos. Res., 23, 83–95,
https://doi.org/10.1016/0169-8095(89)90059-8, 1989. a
Kennedy, P. C. and Rutledge, S. A.: S-band dual-polarization radar
observations of winter storms, J. Appl. Meteorol. Clim., 50, 844–858, https://doi.org/10.1175/2010JAMC2558.1, 2011. a
Khain, A. P., Beheng, K. D., Heymsfield, A., Korolev, A., Krichak, S. O.,
Levin, Z., Pinsky, M., Phillips, V., Prabhakaran, T., Teller, A., Van Den
Heever, S. C., and Yano, J. I.: Representation of microphysical processes
in cloud-resolving models: Spectral (bin) microphysics versus bulk
parameterization, Rev. Geophys., 53, 247–322,
https://doi.org/10.1002/2014RG000468, 2015. a
Kienast-Sjögren, E., Spichtinger, P., and Gierens, K.: Formulation and test of an ice aggregation scheme for two-moment bulk microphysics schemes, Atmos. Chem. Phys., 13, 9021–9037, https://doi.org/10.5194/acp-13-9021-2013, 2013. a
Kneifel, S. and Moisseev, D. N.: Long-term statistics of riming in
nonconvective clouds derived from ground-based doppler cloud radar
observations, J. Atmos. Sci., 77, 3495–3508,
https://doi.org/10.1175/JAS-D-20-0007.1, 2020. a
Kneifel, S., Von Lerber, A., Tiira, J., Moisseev, D. N., Kollias, P., and
Leinonen, J.: Observed relations between snowfall microphysics and
triple-frequency radar measurements, J. Geophys. Res., 120,
6034–6055, https://doi.org/10.1002/2015JD023156, 2015. a, b, c
Kneifel, S., Leinonen, J., Tyynelä, J., Ori, D., and Battaglia, A.:
Scattering of Hydrometeors, in: Advances in Global Change Research,
Springer, 67, 249–276, https://doi.org/10.1007/978-3-030-24568-9_15, 2020. a, b
Korolev, A. and Leisner, T.: Review of experimental studies of secondary ice production, Atmos. Chem. Phys., 20, 11767–11797, https://doi.org/10.5194/acp-20-11767-2020, 2020. a, b
Kumjian, M. R. and Ryzhkov, A. V.: The impact of evaporation on polarimetric
characteristics of rain: Theoretical model and practical implications,
J. Appl. Meteorol. Clim., 49, 1247–1267,
https://doi.org/10.1175/2010JAMC2243.1, 2010. a
Lamb, D. and Verlinde, J.: Physics and chemistry of clouds, Cambridge
University Press, https://doi.org/10.1017/CBO9780511976377, 2011. a, b, c
Liao, L., Meneghini, R., Iguchi, T., and Detwiler, A.: Use of dual-wavelength
radar for snow parameter estimates, J. Atmos. Ocean. Tech., 22, 1494–1506, https://doi.org/10.1175/JTECH1808.1, 2005. a
Lin, Y. L., Farley, R. D., and Orville, H. D.: Bulk parameterization of the
snow field in a cloud model., J. Clim. Appl. Meteorol.,
22, 1065–1092, https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2, 1983. a, b
Lohmann, U., Lüönd, F., and Mahrt, F.: An introduction to clouds:
From the microscale to climate, Cambridge University Press,
https://doi.org/10.1017/CBO9781139087513, 2016. a
Löhnert, U., Schween, J. H., Acquistapace, C., Ebell, K., Maahn, M.,
Barrera-Verdejo, M., Hirsikko, A., Bohn, B., Knaps, A., O'Connor, E., Simmer,
C., Wahner, A., and Crewell, S.: JOYCE: Jülich Observatory for Cloud
Evolution, B. Am. Meteorol. Soc., 96, 1157–1174,
https://doi.org/10.1175/BAMS-D-14-00105.1, 2015. a
Marke, T., Crewell, S., Schemann, V., Schween, J. H., and Tuononen, M.:
Long-term observations and high-resolution modeling of midlatitude nocturnal
boundary layer processes connected to low-level jets, J. Appl. Meteorol. Clim., 57, 1155–1170, https://doi.org/10.1175/JAMC-D-17-0341.1,
2018. a
Mason, S. L., Hogan, R. J., Westbrook, C. D., Kneifel, S., Moisseev, D., and von Terzi, L.: The importance of particle size distribution and internal structure for triple-frequency radar retrievals of the morphology of snow, Atmos. Meas. Tech., 12, 4993–5018, https://doi.org/10.5194/amt-12-4993-2019, 2019. a
Mason, S. L., Chiu, C. J., Hogan, R. J., Moisseev, D. N., and Kneifel, S.:
Retrievals of Riming and Snow Density From Vertically Pointing Doppler
Radars, J. Geophys. Res.-Atmos., 123, 13807–13834,
https://doi.org/10.1029/2018JD028603, 2018. a, b
Matrosov, S. Y.: A dual-wavelength radar method to measure snowfall rate,
J. Appl. Meteorol., 37, 1510–1521,
https://doi.org/10.1175/1520-0450(1998)037<1510:ADWRMT>2.0.CO;2, 1998. a
Mcfarquhar, G. M., Hsieh, T. L., Freer, M., Mascio, J., and Jewett, B. F.: The
characterization of ice hydrometeor gamma size distributions as volumes in
N0-λ-μ phase space: Implications for microphysical process
modeling, J. Atmos. Sci., 72, 892–909,
https://doi.org/10.1175/JAS-D-14-0011.1, 2015. a
Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020. a
Milbrandt, J. A. and Morrison, H.: Parameterization of cloud microphysics
based on the prediction of bulk ice particle properties, Part III:
Introduction of multiple free categories, J. Atmos. Sci., 73, 975–995, https://doi.org/10.1175/JAS-D-15-0204.1, 2016. a
Mitchell, D. L.: Evolution of snow-size spectra in cyclonic storms. Part I:
snow growth by vapor deposition and aggregation, J. Atmos. Sci., 45, 3431–3451,
https://doi.org/10.1175/1520-0469(1988)045<3431:EOSSSI>2.0.CO;2, 1988. a, b
Mitchell, D. L.: Use of mass- and area-dimensional power laws for determining
precipitation particle terminal velocities, J. Atmos. Sci., 53, 1710–1723,
https://doi.org/10.1175/1520-0469(1996)053<1710:UOMAAD>2.0.CO;2, 1996. a
Mitchell, D. L. and Heymsfield, A. J.: Refinements in the treatment of ice
particle terminal velocities, highlighting aggregates, J. Atmos. Sci., 62, 1637–1644, https://doi.org/10.1175/JAS3413.1, 2005. a
Moisseev, D. N., Lautaportti, S., Tyynela, J., and Lim, S.: Dual-polarization
radar signatures in snowstorms: Role of snowflake aggregation, J. Geophys. Res., 120, 12644–12665, https://doi.org/10.1002/2015JD023884, 2015. a
Morrison, H. and Milbrandt, J. A.: Parameterization of cloud microphysics
based on the prediction of bulk ice particle properties. Part I: Scheme
description and idealized tests, J. Atmos. Sci., 72,
287–311, https://doi.org/10.1175/JAS-D-14-0065.1, 2015. a, b, c
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W.,
Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A.,
Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S. I., van
Diedenhoven, B., and Xue, L.: Confronting the Challenge of Modeling Cloud
and Precipitation Microphysics, J. Adv. Model. Earth
Sys., 12, 2019MS001689, https://doi.org/10.1029/2019MS001689, 2020. a, b
Mosimann, L.: An improved method for determining the degree of snow crystal
riming by vertical Doppler radar, Atmos. Res., 37, 305–323,
https://doi.org/10.1016/0169-8095(94)00050-N, 1995. a, b
Ori, D., Schemann, V., Karrer, M., Dias Neto, J., von Terzi, L., Seifert, A.,
and Kneifel, S.: Evaluation of ice particle growth in ICON using statistics
of multi-frequency Doppler cloud radar observations, Q. J. Roy. Meteor. Soc., 146, 3830–3849, https://doi.org/10.1002/qj.3875,
2020. a
Ori, D., von Terzi, L., Karrer, M., and Kneifel, S.: snowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximation, Geosci. Model Dev., 14, 1511–1531, https://doi.org/10.5194/gmd-14-1511-2021, 2021. a, b, c, d
Paukert, M., Fan, J., Rasch, P. J., Morrison, H., Milbrandt, J. A., Shpund, J.,
and Khain, A.: Three-Moment Representation of Rain in a Bulk Microphysics
Model, J. Adv. Model. Earth Sys., 11, 257–277,
https://doi.org/10.1029/2018MS001512, 2019. a
Pfitzenmaier, L., Unal, C. M. H., Dufournet, Y., and Russchenberg, H. W. J.: Observing ice particle growth along fall streaks in mixed-phase clouds using spectral polarimetric radar data, Atmos. Chem. Phys., 18, 7843–7862, https://doi.org/10.5194/acp-18-7843-2018, 2018. a, b
Phillips, V. T., Formenton, M., Bansemer, A., Kudzotsa, I., and Lienert, B.: A
parameterization of sticking efficiency for collisions of snow and graupel
with ice crystals: Theory and comparison with observations, J. Atmos. Sci., 72, 4885–4902, https://doi.org/10.1175/JAS-D-14-0096.1, 2015. a
Pruppacher, H. and Klett, J.: Hydrodynamics of Single Cloud and Precipitation
Particles, PhD. thesis, https://doi.org/10.1007/978-0-306-48100-0_10, 2010. a
Pruppacher, H. R., Klett, J. D., and Wang, P. K.: Microphysics of Clouds and
Precipitation, Aerosol Sci. Technol., 28, 381–382,
https://doi.org/10.1080/02786829808965531, 1998. a, b
Reitter, S., Fröhlich, K., Seifert, A., Crewell, S., and Mech, M.: Evaluation of ice and snow content in the global numerical weather prediction model GME with CloudSat, Geosci. Model Dev., 4, 579–589, https://doi.org/10.5194/gmd-4-579-2011, 2011. a
Schemann, V. and Ebell, K.: Simulation of mixed-phase clouds with the ICON large-eddy model in the complex Arctic environment around Ny-Ålesund, Atmos. Chem. Phys., 20, 475–485, https://doi.org/10.5194/acp-20-475-2020, 2020. a
Schemann, V., Ebell, K., Pospichal, B., Neggers, R., Moseley, C., and Stevens,
B.: Linking Large-Eddy Simulations to Local Cloud Observations, J. Adv. Model. Earth Sys., 12, e2020MS002209,
https://doi.org/10.1029/2020MS002209, 2020. a
Schmidt, G. A., Bader, D., Donner, L. J., Elsaesser, G. S., Golaz, J.-C., Hannay, C., Molod, A., Neale, R. B., and Saha, S.: Practice and philosophy of climate model tuning across six US modeling centers, Geosci. Model Dev., 10, 3207–3223, https://doi.org/10.5194/gmd-10-3207-2017, 2017. a
Schrom, R. S. and Kumjian, M. R.: Connecting microphysical processes in
colorado winter storms with vertical profiles of radar observations, J. Appl. Meteorol. Clim., 55, 1771–1787,
https://doi.org/10.1175/JAMC-D-15-0338.1, 2016. a
Seifert, A.: On the parameterization of evaporation of raindrops as simulated
by a one-dimensional rainshaft model, J. Atmos. Sci.,
65, 3608–3619, https://doi.org/10.1175/2008JAS2586.1, 2008. a, b, c
Slater, B. and Michaelides, A.: Surface premelting of water ice, Nature Reviews Chemistry, 3, 172–188,
https://doi.org/10.1038/s41570-019-0080-8, 2019. a, b, c
Szyrmer, W. and Zawadzki, I.: Snow Studies. Part IV: Ensemble Retrieval of
Snow Microphysics from Dual-Wavelength Vertically Pointing Radars, J. Atmos. Sci., 71, 1158–1170, 2014. a
Tridon, F., Battaglia, A., and Kneifel, S.: Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars, Atmos. Meas. Tech., 13, 5065–5085, https://doi.org/10.5194/amt-13-5065-2020, 2020. a
Tsai, T. C. and Chen, J. P.: Multimoment ice bulk microphysics scheme with
consideration for particle shape and apparent density. Part I: Methodology
and idealized simulation, J. Atmos. Sci., 77,
1821–1850, https://doi.org/10.1175/JAS-D-19-0125.1, 2020. a
Verlinde, J., Flatau, P. J., and Cotton, W. R.: Analytical solutions to the
collection growth equation: comparison with approximate methods and
application to cloud microphysics parameterization schemes, J. Atmos. Sci., 47, 2871–2880,
https://doi.org/10.1175/1520-0469(1990)047<2871:ASTTCG>2.0.CO;2, 1990. a
Wang, P. K.: Physics and dynamics of clouds and precipitation, Vol.
9781107005, Cambridge University Press, https://doi.org/10.1017/CBO9780511794285, 2010. a
Zängl, G., Reinert, D., Rípodas, 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
Short summary
Modeling precipitation is of great relevance, e.g., for mitigating damage caused by extreme weather. A key component in accurate precipitation modeling is aggregation, i.e., sticking together of snowflakes. Simulating aggregation is difficult due to multiple parameters that are not well-known. Knowing how these parameters affect aggregation can help its simulation. We put new parameters in the model and select a combination of parameters with which the model can simulate observations better.
Modeling precipitation is of great relevance, e.g., for mitigating damage caused by extreme...
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