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
Related authors
Davide Ori, Leonie von Terzi, Markus Karrer, and Stefan Kneifel
Geosci. Model Dev., 14, 1511–1531, https://doi.org/10.5194/gmd-14-1511-2021, https://doi.org/10.5194/gmd-14-1511-2021, 2021
Short summary
Short summary
Snowflakes have very complex shapes, and modeling their properties requires vast computing power. We produced a large number of realistic snowflakes and modeled their average properties by leveraging their fractal structure. Our approach allows modeling the properties of big ensembles of snowflakes, taking into account their natural variability, at a much lower cost. This enables the usage of remote sensing instruments, such as radars, to monitor the evolution of clouds and precipitation.
Kamil Mróz, Alessandro Battaglia, Stefan Kneifel, Leonie von Terzi, Markus Karrer, and Davide Ori
Atmos. Meas. Tech., 14, 511–529, https://doi.org/10.5194/amt-14-511-2021, https://doi.org/10.5194/amt-14-511-2021, 2021
Short summary
Short summary
The article examines the relationship between the characteristics of rain and the properties of the ice cloud from which the rain originated. Our results confirm the widely accepted assumption that the mass flux through the melting zone is well preserved with an exception of extreme aggregation and riming conditions. Moreover, it is shown that the mean (mass-weighted) size of particles above and below the melting zone is strongly linked, with the former being on average larger.
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
Short summary
Short summary
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.
Maleen Hanst, Carmen G. Köhler, Axel Seifert, and Linda Schlemmer
EGUsphere, https://doi.org/10.5194/egusphere-2025-3312, https://doi.org/10.5194/egusphere-2025-3312, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
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 the ICON weather prediction model improves the prediction of these regions. Running multiple simulations (an ensemble) 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
EGUsphere, https://doi.org/10.5194/egusphere-2025-2062, https://doi.org/10.5194/egusphere-2025-2062, 2025
Short summary
Short summary
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 enable to evaluate if numerical weather prediction models are able to accurately represent these air mass transformations. This work helps to model the future climate changes in the Arctic, which are important for mid-latitude weather.
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
Short summary
Short summary
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.
Anton Kötsche, Alexander Myagkov, Leonie von Terzi, Maximilian Maahn, Veronika Ettrichrätz, Teresa Vogl, Alexander Ryzhkov, Petar Bukovcic, Davide Ori, and Heike Kalesse-Los
EGUsphere, https://doi.org/10.5194/egusphere-2025-734, https://doi.org/10.5194/egusphere-2025-734, 2025
Short summary
Short summary
Our study combines radar observations of snowf 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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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.
Harald Rybka, Ulrike Burkhardt, Martin Köhler, Ioanna Arka, Luca Bugliaro, Ulrich Görsdorf, Ákos Horváth, Catrin I. Meyer, Jens Reichardt, Axel Seifert, and Johan Strandgren
Atmos. Chem. Phys., 21, 4285–4318, https://doi.org/10.5194/acp-21-4285-2021, https://doi.org/10.5194/acp-21-4285-2021, 2021
Short summary
Short summary
Estimating the impact of convection on the upper-tropospheric water budget remains a problem for models employing resolutions of several kilometers or more. A sub-kilometer high-resolution model is used to study summertime convection. The results suggest mostly close agreement with ground- and satellite-based observational data while slightly overestimating total frozen water path and anvil lifetime. The simulations are well suited to supplying information for parameterization development.
Davide Ori, Leonie von Terzi, Markus Karrer, and Stefan Kneifel
Geosci. Model Dev., 14, 1511–1531, https://doi.org/10.5194/gmd-14-1511-2021, https://doi.org/10.5194/gmd-14-1511-2021, 2021
Short summary
Short summary
Snowflakes have very complex shapes, and modeling their properties requires vast computing power. We produced a large number of realistic snowflakes and modeled their average properties by leveraging their fractal structure. Our approach allows modeling the properties of big ensembles of snowflakes, taking into account their natural variability, at a much lower cost. This enables the usage of remote sensing instruments, such as radars, to monitor the evolution of clouds and precipitation.
Yuefei Zeng, Alberto de Lozar, Tijana Janjic, and Axel Seifert
Geosci. Model Dev., 14, 1295–1307, https://doi.org/10.5194/gmd-14-1295-2021, https://doi.org/10.5194/gmd-14-1295-2021, 2021
Short summary
Short summary
A new integrated mass-flux adjustment filter is introduced and examined with an idealized setup for convective-scale radar data assimilation. It is found that the new filter slightly reduces the accuracy of background and analysis states; however, it preserves the main structure of cold pools and primary mesocyclone properties of supercells. More importantly, it successfully diminishes the imbalance in the analysis considerably and improves the forecasts.
Kamil Mróz, Alessandro Battaglia, Stefan Kneifel, Leonie von Terzi, Markus Karrer, and Davide Ori
Atmos. Meas. Tech., 14, 511–529, https://doi.org/10.5194/amt-14-511-2021, https://doi.org/10.5194/amt-14-511-2021, 2021
Short summary
Short summary
The article examines the relationship between the characteristics of rain and the properties of the ice cloud from which the rain originated. Our results confirm the widely accepted assumption that the mass flux through the melting zone is well preserved with an exception of extreme aggregation and riming conditions. Moreover, it is shown that the mean (mass-weighted) size of particles above and below the melting zone is strongly linked, with the former being on average larger.
Jie Gong, Xiping Zeng, Dong L. Wu, S. Joseph Munchak, Xiaowen Li, Stefan Kneifel, Davide Ori, Liang Liao, and Donifan Barahona
Atmos. Chem. Phys., 20, 12633–12653, https://doi.org/10.5194/acp-20-12633-2020, https://doi.org/10.5194/acp-20-12633-2020, 2020
Short summary
Short summary
This work provides a novel way of using polarized passive microwave measurements to study the interlinked cloud–convection–precipitation processes. The magnitude of differences between polarized radiances is found linked to ice microphysics (shape, size, orientation and density), mesoscale dynamic and thermodynamic structures, and surface precipitation. We conclude that passive sensors with multiple polarized channel pairs may serve as cheaper and useful substitutes for spaceborne radar sensors.
Alexander Myagkov, Stefan Kneifel, and Thomas Rose
Atmos. Meas. Tech., 13, 5799–5825, https://doi.org/10.5194/amt-13-5799-2020, https://doi.org/10.5194/amt-13-5799-2020, 2020
Short summary
Short summary
This study shows two methods for evaluating the reflectivity calibration of W-band cloud radars. Both methods use natural rain as a reference target. The first method is based on spectral polarimetric observations and requires a polarimetric cloud radar with a scanner. The second method utilizes disdrometer observations and can be applied to scanning and vertically pointed radars. Both methods show consistent results and can be applied for operational monitoring of measurement quality.
Frédéric Tridon, Alessandro Battaglia, and Stefan Kneifel
Atmos. Meas. Tech., 13, 5065–5085, https://doi.org/10.5194/amt-13-5065-2020, https://doi.org/10.5194/amt-13-5065-2020, 2020
Short summary
Short summary
The droplets and ice crystals composing clouds and precipitation interact with microwaves and can therefore be observed by radars, but they can also attenuate the signal they emit. By combining the observations made by two ground-based radars, this study describes an original approach for estimating such attenuation. As a result, the latter can be not only corrected in the radar observations but also exploited for providing an accurate characterization of droplet and ice crystal properties.
Mario Mech, Maximilian Maahn, Stefan Kneifel, Davide Ori, Emiliano Orlandi, Pavlos Kollias, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, https://doi.org/10.5194/gmd-13-4229-2020, 2020
Short summary
Short summary
The Passive and Active Microwave TRAnsfer tool (PAMTRA) is a public domain software package written in Python and Fortran for the simulation of microwave remote sensing observations. PAMTRA models the interaction of radiation with gases, clouds, precipitation, and the surface using either in situ observations or model output as input parameters. The wide range of applications is demonstrated for passive (radiometer) and active (radar) instruments on ground, airborne, and satellite platforms.
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...
Altmetrics
Final-revised paper
Preprint