Articles | Volume 24, issue 10
https://doi.org/10.5194/acp-24-5737-2024
© Author(s) 2024. 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-24-5737-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 May 2024
Research article |
| 21 May 2024
| 21 May 2024
Simulations of the impact of cloud condensation nuclei and ice-nucleating particles perturbations on the microphysics and radar reflectivity factor of stratiform mixed-phase clouds
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
Patric Seifert
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
Tempei Hashino
School of Environmental Science and Engineering, Kochi University of Technology, Kami, Japan
Maximilian Maahn
Leipzig Institute of Meteorology (LIM), Leipzig University, Leipzig, Germany
Fabian Senf
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
Oswald Knoth
Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
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Filippo Emilio Scarsi, Alessandro Battaglia, Maximilian Maahn, and Stef Lhermitte
The Cryosphere, 19, 4875–4892, https://doi.org/10.5194/tc-19-4875-2025, https://doi.org/10.5194/tc-19-4875-2025, 2025
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Snowfall measurements at high latitudes are crucial for estimating ice sheet mass balance. Spaceborne radar and radiometer missions help estimate snowfall but face uncertainties. This work evaluates uncertainties in snowfall estimates from a fixed near-nadir radar (CloudSat-like) and a conically scanning radar (WIVERN-like), showing that a WIVERN-like radar will provide better estimates than a CloudSat-like radar at smaller spatial and temporal scales.
Cristofer Jimenez, Albert Ansmann, Kevin Ohneiser, Hannes Griesche, Ronny Engelmann, Martin Radenz, Julian Hofer, Dietrich Althausen, Daniel A. Knopf, Sandro Dahlke, Johannes Bühl, Holger Baars, Patric Seifert, and Ulla Wandinger
Atmos. Chem. Phys., 25, 12955–12981, https://doi.org/10.5194/acp-25-12955-2025, https://doi.org/10.5194/acp-25-12955-2025, 2025
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We studied the water and ice phases of Arctic mixed-phase clouds (MPCs) using dual FOV polarization lidar and Doppler radar on board Polarstern during the MOSAiC expedition. Two long-lasting Arctic MPCs and year-round statistics show persistent droplet activation and dominant immersion freezing, indicating well-filled cloud condensation nuclei and ice-nucleating particle reservoirs. These findings help explain MPC longevity and may improve cloud life cycle representation in weather and climate models.
Majid Hajipour, Patric Seifert, Hannes Griesche, Kevin Ohneiser, and Martin Radenz
Atmos. Meas. Tech., 18, 5199–5222, https://doi.org/10.5194/amt-18-5199-2025, https://doi.org/10.5194/amt-18-5199-2025, 2025
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This study presents an approach that enables the detection of the shape and orientation of multiple types of co-located hydrometeors in mixed-phase cloud systems. This information is key for improving the understanding of these clouds, as they do contain ice and liquid water simultaneously, making them relevant for the precipitation budget and radiative balance of the Earth's atmosphere. The retrieval is based on elevation scans of polarimetric cloud radars and can therefore be flexibly applied.
Christopher Fuchs, Fabiola Ramelli, Anna J. Miller, Nadja Omanovic, Robert Spirig, Huiying Zhang, Patric Seifert, Kevin Ohneiser, Ulrike Lohmann, and Jan Henneberger
Atmos. Chem. Phys., 25, 12177–12196, https://doi.org/10.5194/acp-25-12177-2025, https://doi.org/10.5194/acp-25-12177-2025, 2025
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We quantify diffusional ice crystal growth in natural clouds using cloud seeding experiments. We report growth rates for 14 experiments between −5.1 °C and −8.3 °C and observe strong variations depending on the cloud characteristics. Comparing our growth rates to laboratory data, we found similar temperature-dependent trends, but the laboratory rates are higher. These data fill the gap in quantitative in situ observation of ice crystal growth, helping to validate models and laboratory experiments.
Anton Kötsche, Maximilian Maahn, Veronika Ettrichrätz, and Heike Kalesse-Los
EGUsphere, https://doi.org/10.5194/egusphere-2025-4517, https://doi.org/10.5194/egusphere-2025-4517, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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We studied how turbulence affects snowfall in the Colorado Rockies, focusing on a turbulent layer behind Gothic Mountain. Using radar and surface observations, we found turbulence enhances snow growth by causing snowflakes to stick and form extra ice via collisions. Liquid water at cold temperatures further boosts snow formation. This work shows how turbulence shapes mountain snowfall and demonstrates radar-based methods to study precipitation processes.
Tom Gaudek, Cristofer Jimenez, Kevin Ohneiser, Christopher Fuchs, Jan Henneberger, Johannes Bühl, Andi Klamt, Albert Ansmann, Ronny Engelmann, and Patric Seifert
EGUsphere, https://doi.org/10.5194/egusphere-2025-4105, https://doi.org/10.5194/egusphere-2025-4105, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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This study introduces the maximum diameter (Dmax) of precipitation particles measured by a two-dimensional video disdrometer (2DVD) as a novel parameter. Dmax is applied in a cloud seeding study during the Cloudlab campaign and, for the first time, in a MOSAiC case to evaluate the LIRAS-ice remote-sensing retrieval of in-cloud ice crystal size and number. Both quantities agreed well with the 2DVD measurements under ideal conditions, highlighting the potential of Dmax for precipitation studies.
Audrey Teisseire, Patric Seifert, Kevin Ohneiser, Maximilian Maahn, Robert Spirig, and Jan Henneberger
EGUsphere, https://doi.org/10.5194/egusphere-2025-3923, https://doi.org/10.5194/egusphere-2025-3923, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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This study evaluates the vertical distribution of particle shape (VDPS) method for identifying particle shapes and riming/aggregation using in-situ and multi-frequency radar data from the CLOUDLAB campaign. Despite non-Rayleigh scattering effects, results show that the VDPS method using a Ka-band scanning cloud radar in slanted depolarization ratio (SLDR) mode is effective for hydrometeor classification.
Kevin Ohneiser, Markus Hartmann, Heike Wex, Patric Seifert, Anja Hardt, Anna Miller, Katharina Baudrexl, Werner Thomas, Veronika Ettrichrätz, Maximilian Maahn, Tom Gaudek, Willi Schimmel, Fabian Senf, Hannes Griesche, Martin Radenz, and Jan Henneberger
EGUsphere, https://doi.org/10.5194/egusphere-2025-3675, https://doi.org/10.5194/egusphere-2025-3675, 2025
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This study highlights the efficiency of supercooled stratus clouds to remove ice-nucleating particles (INPs). In our measurement scenarios within the planetary boundary layer lower concentrations of INP under supercooled stratus conditions were found than with temperatures above freezing. Within the free troposphere a lot more INPs were found to be available which means that the free troposphere must be taken into account as an important source of INPs.
Zhaolong Wu, Patric Seifert, Yun He, Holger Baars, Haoran Li, Cristofer Jimenez, Chengcai Li, and Albert Ansmann
Atmos. Meas. Tech., 18, 3611–3634, https://doi.org/10.5194/amt-18-3611-2025, https://doi.org/10.5194/amt-18-3611-2025, 2025
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This study introduces a novel method to detect horizontally oriented ice crystals (HOICs) using two ground-based polarization lidars at different zenith angles, based on a yearlong dataset collected in Beijing. Combined with cloud radar and reanalysis data, the fine categorization results reveal HOICs occur in calm winds and moderately cold temperatures and are influenced by turbulence near cloud bases. The results enhance our understanding of cloud processes and improve atmospheric models.
Hossein Panahifar, Maria Poutli, George Kotsias, Argyro Nisantzi, Silas Michaelides, Diofantos Hadjimitsis, Patric Seifert, Albert Ansmann, and Rodanthi-Elisavet Mamouri
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 1153–1158, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1153-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1153-2025, 2025
Nina Maherndl, Alessandro Battaglia, Anton Kötsche, and Maximilian Maahn
Atmos. Meas. Tech., 18, 3287–3304, https://doi.org/10.5194/amt-18-3287-2025, https://doi.org/10.5194/amt-18-3287-2025, 2025
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Accurate measurements of ice water content (IWC) and snowfall rate (SR) are challenging due to high spatial variability and limitations of our measurement techniques. Here, we present a novel method to derive IWC and SR from W-band cloud radar observations, considering the degree of riming. We also investigate the use of the liquid water path (LWP) as a proxy for the occurrence of riming. LWP is easier to measure, so that the method can be applied to both ground-based and space-based instruments.
Kevin Ohneiser, Patric Seifert, Willi Schimmel, Fabian Senf, Tom Gaudek, Martin Radenz, Audrey Teisseire, Veronika Ettrichrätz, Teresa Vogl, Nina Maherndl, Nils Pfeifer, Jan Henneberger, Anna J. Miller, Nadja Omanovic, Christopher Fuchs, Huiying Zhang, Fabiola Ramelli, Robert Spirig, Anton Kötsche, Heike Kalesse-Los, Maximilian Maahn, Heather Corden, Alexis Berne, Majid Hajipour, Hannes Griesche, Julian Hofer, Ronny Engelmann, Annett Skupin, Albert Ansmann, and Holger Baars
EGUsphere, https://doi.org/10.5194/egusphere-2025-2482, https://doi.org/10.5194/egusphere-2025-2482, 2025
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This study focuses on a seeder-feeder cloud system on 8 Jan 2024 in Eriswil, Switzerland. It is shown how the interaction of these cloud systems changes the cloud microphysical properties and the precipitation patterns. A big set of advanced remote-sensing techniques and retrieval algorithms are applied, so that a detailed view on the seeder-feeder cloud system is available. The gained knowledge can be used to improve weather models and weather forecasts.
Albert Ansmann, Cristofer Jimenez, Johanna Roschke, Johannes Bühl, Kevin Ohneiser, Ronny Engelmann, Martin Radenz, Hannes Griesche, Julian Hofer, Dietrich Althausen, Daniel A. Knopf, Sandro Dahlke, Tom Gaudek, Patric Seifert, and Ulla Wandinger
Atmos. Chem. Phys., 25, 4847–4866, https://doi.org/10.5194/acp-25-4847-2025, https://doi.org/10.5194/acp-25-4847-2025, 2025
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In this study, we focus on the potential impact of wildfire smoke on cirrus formation. For the first time, state-of-the-art aerosol and cirrus observations with lidar and radar, presented in this paper (Part 1 of a series of two articles), are closely linked to the comprehensive modeling of gravity-wave-induced ice nucleation in cirrus evolution processes, presented in a companion paper (Part 2). We found a clear impact of wildfire smoke on cirrus evolution.
Levin Rug, Willi Schimmel, Fabian Hoffmann, and Oswald Knoth
EGUsphere, https://doi.org/10.5194/egusphere-2025-380, https://doi.org/10.5194/egusphere-2025-380, 2025
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We present the Chemical Mechanism Integrator (Cminor) v1.0, a tool to predict concentrations of chemical compounds undergoing arbitrary reactions. Cminor is an advanced, open-source solver to model either combustion chemistry, or atmospheric chemistry and its direct influence on condensation of cloud droplets and the subsequent processing of aerosol. It uses the superdroplet idea, making it particularly feasible for coupling with such models, which is part of future work.
Dennis Niedermeier, Rasmus Hoffmann, Silvio Schmalfuss, Wiebke Frey, Fabian Senf, Olaf Hellmuth, Mira Pöhlker, and Frank Stratmann
Aerosol Research, 3, 219–230, https://doi.org/10.5194/ar-3-219-2025, https://doi.org/10.5194/ar-3-219-2025, 2025
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This study examines the deliquescence behavior of NaCl particles in a turbulent humidity field using the wind tunnel LACIS-T (Turbulent Leipzig Aerosol Cloud Interaction Simulator). The results show that turbulent relative humidity (RH) fluctuations affect the number of deliquesced particles, depending on the mean RH, strength of humidity fluctuations, and particle residence time. It turns out that, in addition to the mean RH, it is essential to consider humidity fluctuations and particle history when determining the phase state of the deliquescent particles.
Benedikt Gast, Cristofer Jimenez, Albert Ansmann, Moritz Haarig, Ronny Engelmann, Felix Fritzsch, Athena A. Floutsi, Hannes Griesche, Kevin Ohneiser, Julian Hofer, Martin Radenz, Holger Baars, Patric Seifert, and Ulla Wandinger
Atmos. Chem. Phys., 25, 3995–4011, https://doi.org/10.5194/acp-25-3995-2025, https://doi.org/10.5194/acp-25-3995-2025, 2025
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In this study, we discuss the enhanced detection capabilities of a fluorescence lidar in the case of optically thin aerosol layers in the upper troposphere and lower stratosphere (UTLS) region. Our results suggest that such thin aerosol layers are not so rare in the UTLS and can potentially trigger and impact cirrus cloud formation through heterogeneous ice nucleation. By altering the microphysical cloud properties, this could affect clouds' evolution and lifetime and thus their climate effect.
André Ehrlich, Susanne Crewell, Andreas Herber, Marcus Klingebiel, Christof Lüpkes, Mario Mech, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Matthias Buschmann, Hans-Christian Clemen, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Andreas Giez, Sarah Grawe, Christophe Gourbeyre, Jörg Hartmann, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsófia Jurányi, Benjamin Kirbus, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Christian Mallaun, Johanna Mayer, Stephan Mertes, Guillaume Mioche, Manuel Moser, Hanno Müller, Veronika Pörtge, Nils Risse, Greg Roberts, Sophie Rosenburg, Johannes Röttenbacher, Michael Schäfer, Jonas Schaefer, Andreas Schäfler, Imke Schirmacher, Johannes Schneider, Sabrina Schnitt, Frank Stratmann, Christian Tatzelt, Christiane Voigt, Andreas Walbröl, Anna Weber, Bruno Wetzel, Martin Wirth, and Manfred Wendisch
Earth Syst. Sci. Data, 17, 1295–1328, https://doi.org/10.5194/essd-17-1295-2025, https://doi.org/10.5194/essd-17-1295-2025, 2025
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This paper provides an overview of the HALO–(AC)3 aircraft campaign data sets, the campaign-specific instrument operation, data processing, and data quality. The data set comprises in situ and remote sensing observations from three research aircraft: HALO, Polar 5, and Polar 6. All data are published in the PANGAEA database by instrument-separated data subsets. It is highlighted how the scientific analysis of the HALO–(AC)3 data benefits from the coordinated operation of three aircraft.
Audrey Teisseire, Anne-Claire Billault-Roux, Teresa Vogl, and Patric Seifert
Atmos. Meas. Tech., 18, 1499–1517, https://doi.org/10.5194/amt-18-1499-2025, https://doi.org/10.5194/amt-18-1499-2025, 2025
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This study demonstrates the ability of a new method delivering the vertical distribution of particle shape to highlight riming and aggregation processes, identifying graupel and aggregates, respectively, as isometric particles. The distinction between these processes can be achieved using lidar or spectral techniques, as demonstrated in the case studies. The capability of the new method to identify rimed particles and aggregates without differentiating them can simplify statistical work.
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
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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.
Nina Maherndl, Manuel Moser, Imke Schirmacher, Aaron Bansemer, Johannes Lucke, Christiane Voigt, and Maximilian Maahn
Atmos. Chem. Phys., 24, 13935–13960, https://doi.org/10.5194/acp-24-13935-2024, https://doi.org/10.5194/acp-24-13935-2024, 2024
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It is not clear why ice crystals in clouds occur in clusters. Here, airborne measurements of clouds in mid-latitudes and high latitudes are used to study the spatial variability of ice. Further, we investigate the influence of riming, which occurs when liquid droplets freeze onto ice crystals. We find that riming enhances the occurrence of ice clusters. In the Arctic, riming leads to ice clustering at spatial scales of 3–5 km. This is due to updrafts and not higher amounts of liquid water.
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.
G. Alexander Sokolowsky, Sean W. Freeman, William K. Jones, Julia Kukulies, Fabian Senf, Peter J. Marinescu, Max Heikenfeld, Kelcy N. Brunner, Eric C. Bruning, Scott M. Collis, Robert C. Jackson, Gabrielle R. Leung, Nils Pfeifer, Bhupendra A. Raut, Stephen M. Saleeby, Philip Stier, and Susan C. van den Heever
Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, https://doi.org/10.5194/gmd-17-5309-2024, 2024
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Building on previous analysis tools developed for atmospheric science, the original release of the Tracking and Object-Based Analysis (tobac) Python package, v1.2, was open-source, modular, and insensitive to the type of gridded input data. Here, we present the latest version of tobac, v1.5, which substantially improves scientific capabilities and computational efficiency from the previous version. These enhancements permit new uses for tobac in atmospheric science and potentially other fields.
Nadja Omanovic, Sylvaine Ferrachat, Christopher Fuchs, Jan Henneberger, Anna J. Miller, Kevin Ohneiser, Fabiola Ramelli, Patric Seifert, Robert Spirig, Huiying Zhang, and Ulrike Lohmann
Atmos. Chem. Phys., 24, 6825–6844, https://doi.org/10.5194/acp-24-6825-2024, https://doi.org/10.5194/acp-24-6825-2024, 2024
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We present simulations with a high-resolution numerical weather prediction model to study the growth of ice crystals in low clouds following glaciogenic seeding. We show that the simulated ice crystals grow slower than observed and do not consume as many cloud droplets as measured in the field. This may have implications for forecasting precipitation, as the ice phase is crucial for precipitation at middle and high latitudes.
Nina Maherndl, Manuel Moser, Johannes Lucke, Mario Mech, Nils Risse, Imke Schirmacher, and Maximilian Maahn
Atmos. Meas. Tech., 17, 1475–1495, https://doi.org/10.5194/amt-17-1475-2024, https://doi.org/10.5194/amt-17-1475-2024, 2024
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In some clouds, liquid water droplets can freeze onto ice crystals (riming). Riming leads to the formation of snowflakes. We show two ways to quantify riming using aircraft data collected in the Arctic. One aircraft had a cloud radar, while the other aircraft was measuring directly in cloud. The first method compares radar and direct observations. The second looks at snowflake shape. Both methods agree, except when there were gaps in the cloud. This improves our ability to understand riming.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
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The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Maximilian Maahn, Dmitri Moisseev, Isabelle Steinke, Nina Maherndl, and Matthew D. Shupe
Atmos. Meas. Tech., 17, 899–919, https://doi.org/10.5194/amt-17-899-2024, https://doi.org/10.5194/amt-17-899-2024, 2024
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The open-source Video In Situ Snowfall Sensor (VISSS) is a novel instrument for characterizing particle shape, size, and sedimentation velocity in snowfall. It combines a large observation volume with relatively high resolution and a design that limits wind perturbations. The open-source nature of the VISSS hardware and software invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.
Julian Hofer, Patric Seifert, J. Ben Liley, Martin Radenz, Osamu Uchino, Isamu Morino, Tetsu Sakai, Tomohiro Nagai, and Albert Ansmann
Atmos. Chem. Phys., 24, 1265–1280, https://doi.org/10.5194/acp-24-1265-2024, https://doi.org/10.5194/acp-24-1265-2024, 2024
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An 11-year dataset of polarization lidar observations from Lauder, New Zealand / Aotearoa, was used to distinguish the thermodynamic phase of natural clouds. The cloud dataset was separated to assess the impact of air mass origin on the frequency of heterogeneous ice formation. Ice formation efficiency in clouds above Lauder was found to be lower than in the polluted Northern Hemisphere midlatitudes but higher than in very clean and pristine environments, such as Punta Arenas in southern Chile.
Hannes Jascha Griesche, Carola Barrientos-Velasco, Hartwig Deneke, Anja Hünerbein, Patric Seifert, and Andreas Macke
Atmos. Chem. Phys., 24, 597–612, https://doi.org/10.5194/acp-24-597-2024, https://doi.org/10.5194/acp-24-597-2024, 2024
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The Arctic is strongly affected by climate change and the role of clouds therein is not yet completely understood. Measurements from the Arctic expedition PS106 were used to simulate radiative fluxes with and without clouds at very low altitudes (below 165 m), and their radiative effect was calculated to be 54 Wm-2. The low heights of these clouds make them hard to observe. This study shows the importance of accurate measurements and simulations of clouds and gives suggestions for improvements.
Rodanthi-Elisavet Mamouri, Albert Ansmann, Kevin Ohneiser, Daniel A. Knopf, Argyro Nisantzi, Johannes Bühl, Ronny Engelmann, Annett Skupin, Patric Seifert, Holger Baars, Dragos Ene, Ulla Wandinger, and Diofantos Hadjimitsis
Atmos. Chem. Phys., 23, 14097–14114, https://doi.org/10.5194/acp-23-14097-2023, https://doi.org/10.5194/acp-23-14097-2023, 2023
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For the first time, rather clear evidence is found that wildfire smoke particles can trigger strong cirrus formation. This finding is of importance because intensive and large wildfires may occur increasingly often in the future as climate change proceeds. Based on lidar observations in Cyprus in autumn 2020, we provide detailed insight into the cirrus formation at the tropopause in the presence of aged wildfire smoke (here, 8–9 day old Californian wildfire smoke).
Albert Ansmann, Kevin Ohneiser, Ronny Engelmann, Martin Radenz, Hannes Griesche, Julian Hofer, Dietrich Althausen, Jessie M. Creamean, Matthew C. Boyer, Daniel A. Knopf, Sandro Dahlke, Marion Maturilli, Henriette Gebauer, Johannes Bühl, Cristofer Jimenez, Patric Seifert, and Ulla Wandinger
Atmos. Chem. Phys., 23, 12821–12849, https://doi.org/10.5194/acp-23-12821-2023, https://doi.org/10.5194/acp-23-12821-2023, 2023
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The 1-year MOSAiC (2019–2020) expedition with the German ice breaker Polarstern was the largest polar field campaign ever conducted. The Polarstern, with our lidar aboard, drifted with the pack ice north of 85° N for more than 7 months (October 2019 to mid-May 2020). We measured the full annual cycle of aerosol conditions in terms of aerosol optical and cloud-process-relevant properties. We observed a strong contrast between polluted winter and clean summer aerosol conditions.
Holger Baars, Joshua Walchester, Elizaveta Basharova, Henriette Gebauer, Martin Radenz, Johannes Bühl, Boris Barja, Ulla Wandinger, and Patric Seifert
Atmos. Meas. Tech., 16, 3809–3834, https://doi.org/10.5194/amt-16-3809-2023, https://doi.org/10.5194/amt-16-3809-2023, 2023
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In 2018, the Aeolus satellite of the European Space Agency (ESA) was launched to improve weather forecasts through global measurements of wind profiles. Given the novel lidar technique onboard, extensive validation efforts have been needed to verify the observations. For this reason, we performed long-term validation measurements in Germany and Chile. We found significant improvement in the data products due to a new algorithm version and can confirm the general validity of Aeolus observations.
Fabian Senf, Bernd Heinold, Anne Kubin, Jason Müller, Roland Schrödner, and Ina Tegen
Atmos. Chem. Phys., 23, 8939–8958, https://doi.org/10.5194/acp-23-8939-2023, https://doi.org/10.5194/acp-23-8939-2023, 2023
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Wildfire smoke is a significant source of airborne atmospheric particles that can absorb sunlight. Extreme fires in particular, such as those during the 2019–2020 Australian wildfire season (Black Summer fires), can considerably affect our climate system. In the present study, we investigate the various effects of Australian smoke using a global climate model to clarify how the Earth's atmosphere, including its circulation systems, adjusted to the extraordinary amount of Australian smoke.
Woosub Roh, Masaki Satoh, Tempei Hashino, Shuhei Matsugishi, Tomoe Nasuno, and Takuji Kubota
Atmos. Meas. Tech., 16, 3331–3344, https://doi.org/10.5194/amt-16-3331-2023, https://doi.org/10.5194/amt-16-3331-2023, 2023
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JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM (Nonhydrostatic ICosahedral
Atmospheric Model) simulation with 3.5 km horizontal resolution and the Joint-Simulator. JAXA L1 data are intended to support the development of JAXA retrieval algorithms for the EarthCARE sensor before launch of the satellite. The expected orbit of EarthCARE and horizontal sampling of each sensor were used to simulate the signals.
Athena Augusta Floutsi, Holger Baars, Ronny Engelmann, Dietrich Althausen, Albert Ansmann, Stephanie Bohlmann, Birgit Heese, Julian Hofer, Thomas Kanitz, Moritz Haarig, Kevin Ohneiser, Martin Radenz, Patric Seifert, Annett Skupin, Zhenping Yin, Sabur F. Abdullaev, Mika Komppula, Maria Filioglou, Elina Giannakaki, Iwona S. Stachlewska, Lucja Janicka, Daniele Bortoli, Eleni Marinou, Vassilis Amiridis, Anna Gialitaki, Rodanthi-Elisavet Mamouri, Boris Barja, and Ulla Wandinger
Atmos. Meas. Tech., 16, 2353–2379, https://doi.org/10.5194/amt-16-2353-2023, https://doi.org/10.5194/amt-16-2353-2023, 2023
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DeLiAn is a collection of lidar-derived aerosol intensive optical properties for several aerosol types, namely the particle linear depolarization ratio, the extinction-to-backscatter ratio (lidar ratio) and the Ångström exponent. The data collection is based on globally distributed, long-term, ground-based, multiwavelength, Raman and polarization lidar measurements and currently covers two wavelengths, 355 and 532 nm, for 13 aerosol categories ranging from basic aerosol types to mixtures.
Samuel Kwakye, Heike Kalesse-Los, Maximilian Maahn, Patric Seifert, Roel van Klink, Christian Wirth, and Johannes Quaas
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-69, https://doi.org/10.5194/amt-2023-69, 2023
Publication in AMT not foreseen
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Insect numbers in the atmosphere can be calculated using polarimetric weather radar but they have to be identified and separated from other echoes, especially weather phenomena. Here, the separation is demonstrated using three machine-learning algorithms and insect count data from suction traps and the nature of radar measurements of different radar echoes is revealed. Random forest is the best separating algorithm and insect echoes radar measurements are distinct.
Kevin Ohneiser, Albert Ansmann, Jonas Witthuhn, Hartwig Deneke, Alexandra Chudnovsky, Gregor Walter, and Fabian Senf
Atmos. Chem. Phys., 23, 2901–2925, https://doi.org/10.5194/acp-23-2901-2023, https://doi.org/10.5194/acp-23-2901-2023, 2023
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This study shows that smoke layers can reach the tropopause via the self-lofting effect within 3–7 d in the absence of pyrocumulonimbus convection if the
aerosol optical thickness is larger than approximately 2 for a longer time period. When reaching the stratosphere, wildfire smoke can sensitively influence the stratospheric composition on a hemispheric scale and thus can affect the Earth’s climate and the ozone layer.
Willi Schimmel, Heike Kalesse-Los, Maximilian Maahn, Teresa Vogl, Andreas Foth, Pablo Saavedra Garfias, and Patric Seifert
Atmos. Meas. Tech., 15, 5343–5366, https://doi.org/10.5194/amt-15-5343-2022, https://doi.org/10.5194/amt-15-5343-2022, 2022
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This study introduces the novel Doppler radar spectra-based machine learning approach VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn). VOODOO is a powerful probability-based extension to the existing Cloudnet hydrometeor target classification, enabling the detection of liquid-bearing cloud layers beyond complete lidar attenuation via user-defined p* threshold. VOODOO performs best for (multi-layer) stratiform and deep mixed-phase clouds with liquid water path > 100 g m−2.
Albert Ansmann, Kevin Ohneiser, Alexandra Chudnovsky, Daniel A. Knopf, Edwin W. Eloranta, Diego Villanueva, Patric Seifert, Martin Radenz, Boris Barja, Félix Zamorano, Cristofer Jimenez, Ronny Engelmann, Holger Baars, Hannes Griesche, Julian Hofer, Dietrich Althausen, and Ulla Wandinger
Atmos. Chem. Phys., 22, 11701–11726, https://doi.org/10.5194/acp-22-11701-2022, https://doi.org/10.5194/acp-22-11701-2022, 2022
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For the first time we present a systematic study on the impact of wildfire smoke on ozone depletion in the Arctic (2020) and Antarctic stratosphere (2020, 2021). Two major fire events in Siberia and Australia were responsible for the observed record-breaking stratospheric smoke pollution. Our analyses were based on lidar observations of smoke parameters (Polarstern, Punta Arenas) and NDACC Arctic and Antarctic ozone profiles as well as on Antarctic OMI satellite observations of column ozone.
Xianda Gong, Martin Radenz, Heike Wex, Patric Seifert, Farnoush Ataei, Silvia Henning, Holger Baars, Boris Barja, Albert Ansmann, and Frank Stratmann
Atmos. Chem. Phys., 22, 10505–10525, https://doi.org/10.5194/acp-22-10505-2022, https://doi.org/10.5194/acp-22-10505-2022, 2022
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The sources of ice-nucleating particles (INPs) are poorly understood in the Southern Hemisphere (SH). We studied INPs in the boundary layer in the southern Patagonia region. No seasonal cycle of INP concentrations was observed. The majority of INPs are biogenic particles, likely from local continental sources. The INP concentrations are higher when strong precipitation occurs. While previous studies focused on marine INP sources in SH, we point out the importance of continental sources of INPs.
Bernd Heinold, Holger Baars, Boris Barja, Matthew Christensen, Anne Kubin, Kevin Ohneiser, Kerstin Schepanski, Nick Schutgens, Fabian Senf, Roland Schrödner, Diego Villanueva, and Ina Tegen
Atmos. Chem. Phys., 22, 9969–9985, https://doi.org/10.5194/acp-22-9969-2022, https://doi.org/10.5194/acp-22-9969-2022, 2022
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The extreme 2019–2020 Australian wildfires produced massive smoke plumes lofted into the lower stratosphere by pyrocumulonimbus convection. Most climate models do not adequately simulate the injection height of such intense fires. By combining aerosol-climate modeling with prescribed pyroconvective smoke injection and lidar observations, this study shows the importance of the representation of the most extreme wildfire events for estimating the atmospheric energy budget.
Jörg Wieder, Nikola Ihn, Claudia Mignani, Moritz Haarig, Johannes Bühl, Patric Seifert, Ronny Engelmann, Fabiola Ramelli, Zamin A. Kanji, Ulrike Lohmann, and Jan Henneberger
Atmos. Chem. Phys., 22, 9767–9797, https://doi.org/10.5194/acp-22-9767-2022, https://doi.org/10.5194/acp-22-9767-2022, 2022
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Ice formation and its evolution in mixed-phase clouds are still uncertain. We evaluate the lidar retrieval of ice-nucleating particle concentration in dust-dominated and continental air masses over the Swiss Alps with in situ observations. A calibration factor to improve the retrieval from continental air masses is proposed. Ice multiplication factors are obtained with a new method utilizing remote sensing. Our results indicate that secondary ice production occurs at temperatures down to −30 °C.
Carola Barrientos-Velasco, Hartwig Deneke, Anja Hünerbein, Hannes J. Griesche, Patric Seifert, and Andreas Macke
Atmos. Chem. Phys., 22, 9313–9348, https://doi.org/10.5194/acp-22-9313-2022, https://doi.org/10.5194/acp-22-9313-2022, 2022
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This article describes an intercomparison of radiative fluxes and cloud properties from satellite, shipborne observations, and 1D radiative transfer simulations. The analysis focuses on research for PS106 expedition aboard the German research vessel, Polarstern. The results are presented in detailed case studies, time series for the PS106 cruise and extended to the central Arctic region. The findings illustrate the main periods of agreement and discrepancies of both points of view.
Kevin Ohneiser, Albert Ansmann, Bernd Kaifler, Alexandra Chudnovsky, Boris Barja, Daniel A. Knopf, Natalie Kaifler, Holger Baars, Patric Seifert, Diego Villanueva, Cristofer Jimenez, Martin Radenz, Ronny Engelmann, Igor Veselovskii, and Félix Zamorano
Atmos. Chem. Phys., 22, 7417–7442, https://doi.org/10.5194/acp-22-7417-2022, https://doi.org/10.5194/acp-22-7417-2022, 2022
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We present and discuss 2 years of long-term lidar observations of the largest stratospheric perturbation by wildfire smoke ever observed. The smoke originated from the record-breaking Australian fires in 2019–2020 and affects climate conditions and even the ozone layer in the Southern Hemisphere. The obvious link between dense smoke occurrence in the stratosphere and strong ozone depletion found in the Arctic and in the Antarctic in 2020 can be regarded as a new aspect of climate change.
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.
Heike Kalesse-Los, Willi Schimmel, Edward Luke, and Patric Seifert
Atmos. Meas. Tech., 15, 279–295, https://doi.org/10.5194/amt-15-279-2022, https://doi.org/10.5194/amt-15-279-2022, 2022
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It is important to detect the vertical distribution of cloud droplets and ice in mixed-phase clouds. Here, an artificial neural network (ANN) previously developed for Arctic clouds is applied to a mid-latitudinal cloud radar data set. The performance of this technique is contrasted to the Cloudnet target classification. For thick/multi-layer clouds, the machine learning technique is better at detecting liquid than Cloudnet, but if lidar data are available Cloudnet is at least as good as the ANN.
Martin Radenz, Johannes Bühl, Patric Seifert, Holger Baars, Ronny Engelmann, Boris Barja González, Rodanthi-Elisabeth Mamouri, Félix Zamorano, and Albert Ansmann
Atmos. Chem. Phys., 21, 17969–17994, https://doi.org/10.5194/acp-21-17969-2021, https://doi.org/10.5194/acp-21-17969-2021, 2021
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This study brings together long-term ground-based remote-sensing observations of mixed-phase clouds at three key locations of aerosol–cloud interactions in the Northern and Southern Hemisphere midlatitudes. The findings contribute several new aspects on the nature of the excess of supercooled liquid clouds in the Southern Hemisphere, such as a long-term lidar-based estimate of ice-nucleating particle profiles as well as the effects of boundary layer coupling and gravity waves on ice formation.
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.
Ronny Engelmann, Albert Ansmann, Kevin Ohneiser, Hannes Griesche, Martin Radenz, Julian Hofer, Dietrich Althausen, Sandro Dahlke, Marion Maturilli, Igor Veselovskii, Cristofer Jimenez, Robert Wiesen, Holger Baars, Johannes Bühl, Henriette Gebauer, Moritz Haarig, Patric Seifert, Ulla Wandinger, and Andreas Macke
Atmos. Chem. Phys., 21, 13397–13423, https://doi.org/10.5194/acp-21-13397-2021, https://doi.org/10.5194/acp-21-13397-2021, 2021
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A Raman lidar was operated aboard the icebreaker Polarstern during MOSAiC and monitored aerosol and cloud layers in the central Arctic up to 30 km height. The article provides an overview of the spectrum of aerosol profiling observations and shows aerosol–cloud interaction studies for liquid-water and ice clouds. A highlight was the detection of a 10 km deep wildfire smoke layer over the North Pole up to 17 km height from the fire season of 2019, which persisted over the whole winter period.
Hartwig Deneke, Carola Barrientos-Velasco, Sebastian Bley, Anja Hünerbein, Stephan Lenk, Andreas Macke, Jan Fokke Meirink, Marion Schroedter-Homscheidt, Fabian Senf, Ping Wang, Frank Werner, and Jonas Witthuhn
Atmos. Meas. Tech., 14, 5107–5126, https://doi.org/10.5194/amt-14-5107-2021, https://doi.org/10.5194/amt-14-5107-2021, 2021
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The SEVIRI instrument flown on the European geostationary Meteosat satellites acquires multi-spectral images at a relatively coarse pixel resolution of 3 × 3 km2, but it also has a broadband high-resolution visible channel with 1 × 1 km2 spatial resolution. In this study, the modification of an existing cloud property and solar irradiance retrieval to use this channel to improve the spatial resolution of its output products as well as the resulting benefits for applications are described.
Hannes J. Griesche, Kevin Ohneiser, Patric Seifert, Martin Radenz, Ronny Engelmann, and Albert Ansmann
Atmos. Chem. Phys., 21, 10357–10374, https://doi.org/10.5194/acp-21-10357-2021, https://doi.org/10.5194/acp-21-10357-2021, 2021
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Heterogeneous ice formation in Arctic mixed-phase clouds under consideration of their surface-coupling state is investigated. Cloud phase and macrophysical properties were determined by means of lidar and cloud radar measurements, the coupling state, and cloud minimum temperature by radiosonde profiles. Above −15 °C cloud minimum temperature, surface-coupled clouds are more likely to contain ice by a factor of 2–6. By means of a literature survey, causes of the observed effects are discussed.
Albert Ansmann, Kevin Ohneiser, Rodanthi-Elisavet Mamouri, Daniel A. Knopf, Igor Veselovskii, Holger Baars, Ronny Engelmann, Andreas Foth, Cristofer Jimenez, Patric Seifert, and Boris Barja
Atmos. Chem. Phys., 21, 9779–9807, https://doi.org/10.5194/acp-21-9779-2021, https://doi.org/10.5194/acp-21-9779-2021, 2021
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We present retrievals of tropospheric and stratospheric height profiles of particle mass, volume, surface area concentration of wildfire smoke layers, and related cloud condensation nuclei (CCN) and ice-nucleating particle (INP) concentrations. The new analysis scheme is applied to ground-based lidar observations of stratospheric Australian smoke over southern South America and to spaceborne lidar observations of tropospheric North American smoke.
Fabiola Ramelli, Jan Henneberger, Robert O. David, Johannes Bühl, Martin Radenz, Patric Seifert, Jörg Wieder, Annika Lauber, Julie T. Pasquier, Ronny Engelmann, Claudia Mignani, Maxime Hervo, and Ulrike Lohmann
Atmos. Chem. Phys., 21, 6681–6706, https://doi.org/10.5194/acp-21-6681-2021, https://doi.org/10.5194/acp-21-6681-2021, 2021
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Orographic mixed-phase clouds are an important source of precipitation, but the ice formation processes within them remain uncertain. Here we investigate the origin of ice crystals in a mixed-phase cloud in the Swiss Alps using aerosol and cloud data from in situ and remote sensing observations. We found that ice formation primarily occurs in cloud top generating cells. Our results indicate that secondary ice processes are active in the feeder region, which can enhance orographic precipitation.
Fabiola Ramelli, Jan Henneberger, Robert O. David, Annika Lauber, Julie T. Pasquier, Jörg Wieder, Johannes Bühl, Patric Seifert, Ronny Engelmann, Maxime Hervo, and Ulrike Lohmann
Atmos. Chem. Phys., 21, 5151–5172, https://doi.org/10.5194/acp-21-5151-2021, https://doi.org/10.5194/acp-21-5151-2021, 2021
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Interactions between dynamics, microphysics and orography can enhance precipitation. Yet the exact role of these interactions is still uncertain. Here we investigate the role of low-level blocking and turbulence for precipitation by combining remote sensing and in situ observations. The observations show that blocked flow can induce the formation of feeder clouds and that turbulence can enhance hydrometeor growth, demonstrating the importance of local flow effects for orographic precipitation.
Michael Weger, Oswald Knoth, and Bernd Heinold
Geosci. Model Dev., 14, 1469–1492, https://doi.org/10.5194/gmd-14-1469-2021, https://doi.org/10.5194/gmd-14-1469-2021, 2021
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A new numerical air-quality transport model for cities is presented, in which buildings are described diffusively. The used diffusive-obstacles approach helps to reduce the computational costs for high-resolution simulations as the grid spacing can be more coarse than in traditional approaches. The research which led to this model development was primarily motivated by the need for a computationally feasible downscaling tool for urban wind and pollution fields from meteorological model output.
Martin Radenz, Patric Seifert, Holger Baars, Athena Augusta Floutsi, Zhenping Yin, and Johannes Bühl
Atmos. Chem. Phys., 21, 3015–3033, https://doi.org/10.5194/acp-21-3015-2021, https://doi.org/10.5194/acp-21-3015-2021, 2021
Cristofer Jimenez, Albert Ansmann, Ronny Engelmann, David Donovan, Aleksey Malinka, Jörg Schmidt, Patric Seifert, and Ulla Wandinger
Atmos. Chem. Phys., 20, 15247–15263, https://doi.org/10.5194/acp-20-15247-2020, https://doi.org/10.5194/acp-20-15247-2020, 2020
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A novel lidar method to study cloud microphysical properties (of liquid water clouds) and to study aerosol–cloud interaction (ACI) is developed and presented in this paper. In Part 1, the theoretical framework including an error analysis is given together with an overview of the aerosol information that the same lidar system can obtain. The ACI concept based on aerosol and cloud information is also explained. Applications of the proposed approach to lidar measurements are presented in Part 2.
Cristofer Jimenez, Albert Ansmann, Ronny Engelmann, David Donovan, Aleksey Malinka, Patric Seifert, Robert Wiesen, Martin Radenz, Zhenping Yin, Johannes Bühl, Jörg Schmidt, Boris Barja, and Ulla Wandinger
Atmos. Chem. Phys., 20, 15265–15284, https://doi.org/10.5194/acp-20-15265-2020, https://doi.org/10.5194/acp-20-15265-2020, 2020
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Part 2 presents the application of the dual-FOV polarization lidar technique introduced in Part 1. A lidar system was upgraded with a second polarization telescope, and it was deployed at the southernmost tip of South America. A comparison with alternative remote sensing techniques and the evaluation of the aerosol–cloud–wind relation in a convective boundary layer in pristine marine conditions are presented in two case studies, demonstrating the potential of the approach for ACI studies.
Martin Bauch, Thomas Labbé, Annabell Engel, and Patric Seifert
Clim. Past, 16, 2343–2358, https://doi.org/10.5194/cp-16-2343-2020, https://doi.org/10.5194/cp-16-2343-2020, 2020
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The onset of Little Ice Age cooling around 1310 CE was preceded in Europe by a series of droughts in the first decade of the 14th century that were uniquely severe in the period 1200–1400. Based mainly on information from chronicles and other historical texts, we reconstructed the socioeconomic and cultural impact of these events but also a seesaw pattern of multiannual droughts in the Mediterranean and Europe north of the Alps that has remarkable resemblances to the 2018–2019 dry period.
Cited articles
Andrejczuk, M., Grabowski, W. W., Reisner, J., and Gadian, A.: Cloud-aerosol interactions for boundary layer stratocumulus in the Lagrangian cloud model, J. Geophys. Res., 115, D22214, https://doi.org/10.1029/2010JD014248, 2010. a
Ansmann, A., Mamouri, R.-E., Bühl, J., Seifert, P., Engelmann, R., Hofer, J., Nisantzi, A., Atkinson, J. D., Kanji, Z. A., Sierau, B., Vrekoussis, M., and Sciare, J.: Ice-nucleating particle versus ice crystal number concentrationin altocumulus and cirrus layers embedded in Saharan dust:a closure study, Atmos. Chem. Phys., 19, 15087–15115, https://doi.org/10.5194/acp-19-15087-2019, 2019. a, b
Arabas, S., Jaruga, A., Pawlowska, H., and Grabowski, W. W.: libcloudph++ 1.0: a single-moment bulk, double-moment bulk, and particle-based warm-rain microphysics library in C++, Geosci. Model Dev., 8, 1677–1707, https://doi.org/10.5194/gmd-8-1677-2015, 2015. a
Atkinson, J. D., Murray, B. J., Woodhouse, M. T., Whale, T. F., Baustian, K. J., Carslaw, K. S., Dobbie, S., O'Sullivan, D., and Malkin, T. L.: The importance of feldspar for ice nucleation by mineral dust in mixed-phase clouds, Nature, 498, 355–358, https://doi.org/10.1038/nature12278, 2013. a
Auer, A. H. and Veal, D. L.: The Dimension of Ice Crystals in Natural Clouds, J. Atmos. Sci., 27, 919–926, https://doi.org/10.1175/1520-0469(1970)027<0919:TDOICI>2.0.CO;2, 1970. a
Bailey, M. P. and Hallett, J.: A Comprehensive Habit Diagram for Atmospheric Ice Crystals: Confirmation from the Laboratory, AIRS II, and Other Field Studies, J. Atmos. Sci., 66, 2888–2899, https://doi.org/10.1175/2009JAS2883.1, 2009. a, b, c
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational convective-scale numerical weather prediction with the COSMO model: Description and sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Barrett, A. I. and Hoose, C.: Microphysical Pathways Active Within Thunderstorms and Their Sensitivity to CCN Concentration and Wind Shear, J. Geophys. Res.-Atmos., 128, e2022JD036965, https://doi.org/10.1029/2022JD036965, 2023. a
Barthlott, C. and Hoose, C.: Aerosol Effects on Clouds and Precipitation over Central Europe in Different Weather Regimes, J. Atmos. Sci., 75, 4247–4264, https://doi.org/10.1175/JAS-D-18-0110.1, 2018. a
Bergeron, T.: On the physics of clouds and precipitation, in: Proces Verbaux de l'Association de Meteorologie, Int. Union of Geodesy and Geophys., Paris, 156–178, 1935. a
Bony, S., Stevens, B., W Frierson, D. M., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Pier Siebesma, A., Sobel, A. H., Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity, Nat. Geosci., 8, 261–268, https://doi.org/10.1038/NGEO2398, 2015. a
Borys, R. D., Lowenthal, D. H., Cohn, S. A., and Brown, W. O.: Mountaintop and radar measurements of anthropogenic aerosol effects on snow growth and snowfall rate, Geophys. Res. Lett., 30, 1538, https://doi.org/10.1029/2002gl016855, 2003. a
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K., Sherwood, S., Stevens, B., and Zhang, X. Y.: Clouds and aerosols, Cambridge University Press, Cambridge, UK, 571–657, https://doi.org/10.1017/CBO9781107415324.016, 2013. a
Chen, J. and Liu, Y.: New understanding and quantification of the regime dependence of aerosol-cloud interaction for studying aerosol indirect effects, Geophys. Res. Lett., 43, 1780–1787, 2016. a
Chen, J.-P. and Lamb, D.: The Theoretical Basis for the Parameterization of Ice Crystal Habits: Growth by Vapor Deposition, J. Atmos. Sci., 51, 1206–1222, https://doi.org/10.1175/1520-0469(1994)051<1206:TTBFTP>2.0.CO;2, 1994. a
Choudhury, G. and Tesche, M.: A first global height-resolved cloud condensation nuclei data set derived from spaceborne lidar measurements, Earth Syst. Sci. Data, 15, 3747–3760, https://doi.org/10.5194/essd-15-3747-2023, 2023. a
de Boer, G., Hashino, T., and Tripoli, G. J.: Ice nucleation through immersion freezing in mixed-phase stratiform clouds: Theory and numerical simulations, Atmos. Res., 96, 315–324, https://doi.org/10.1016/j.atmosres.2009.09.012, 2010. a
de Boer, G., Hashino, T., Tripoli, G. J., and Eloranta, E. W.: A numerical study of aerosol influence on mixed-phase stratiform clouds through modulation of the liquid phase, Atmos. Chem. Phys., 13, 1733–1749, https://doi.org/10.5194/acp-13-1733-2013, 2013. a
DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D., Twohy, C. H., Richardson, M. S., Eidhammer, T., and Rogers, D. C.: Predicting global atmospheric ice nuclei distributions and their impacts on climate, P. Natl. Acad. Sci. USA, 107, 11217–11222, https://doi.org/10.1073/pnas.0910818107, 2010. a
Desai, N., Chandrakar, K. K., Kinney, G., Cantrell, W., and Shaw, R. A.: Aerosol-Mediated Glaciation of Mixed-Phase Clouds: Steady-State Laboratory Measurements, Geophys. Res. Lett., 46, 9154–9162, https://doi.org/10.1029/2019GL083503, 2019. a
Diehl, K. and Wurzler, S.: Heterogeneous drop freezing in the immersion mode: Model calculations considering soluble and insoluble particles in the drops, J. Atmos. Sci., 61, 2063–2072, https://doi.org/10.1175/1520-0469(2004)061<2063:HDFITI>2.0.CO;2, 2004. a, b, c
Dziekan, P., Waruszewski, M., and Pawlowska, H.: University of Warsaw Lagrangian Cloud Model (UWLCM) 1.0: a modern large-eddy simulation tool for warm cloud modeling with Lagrangian microphysics, Geosci. Model Dev., 12, 2587–2606, https://doi.org/10.5194/gmd-12-2587-2019, 2019. a
Engelmann, R., Ansmann, A., Ohneiser, K., Griesche, H., Radenz, M., Hofer, J., Althausen, D., Dahlke, S., Maturilli, M., Veselovskii, I., Jimenez, C., Wiesen, R., Baars, H., Bühl, J., Gebauer, H., Haarig, M., Seifert, P., Wandinger, U., and Macke, A.: Wildfire smoke, Arctic haze, and aerosol effects on mixed-phase and cirrus clouds over the North Pole region during MOSAiC: an introduction, Atmos. Chem. Phys., 21, 13397–13423, https://doi.org/10.5194/acp-21-13397-2021, 2021. a
Fan, J., Leung, L. R., DeMott, P. J., Comstock, J. M., Singh, B., Rosenfeld, D., Tomlinson, J. M., White, A., Prather, K. A., Minnis, P., Ayers, J. K., and Min, Q.: Aerosol impacts on California winter clouds and precipitation during CalWater 2011: local pollution versus long-range transported dust, Atmos. Chem. Phys., 14, 81–101, https://doi.org/10.5194/acp-14-81-2014, 2014. a
Fan, J., Leung, L. R., Rosenfeld, D., and DeMott, P. J.: Effects of cloud condensation nuclei and ice nucleating particles on precipitation processes and supercooled liquid in mixed-phase orographic clouds, Atmos. Chem. Phys., 17, 1017–1035, https://doi.org/10.5194/acp-17-1017-2017, 2017. a, b
Findeisen, W.: Kolloid-meteorologische Vorgänge bei Neiderschlagsbildung, Meteorol. Z., 55, 121–133, 1938. a
Fridlind, A. M., Van Diedenhoven, B., Ackerman, A. S., Avramov, A., Mrowiec, A., Morrison, H., Zuidema, P., and Shupe, M. D.: A fire-ACE/SHEBA case study of mixed-phase arctic boundary layer clouds: Entrainment rate limitations on rapid primary ice nucleation processes, J. Atmos. Sci., 69, 365–389, https://doi.org/10.1175/JAS-D-11-052.1, 2012. a, b
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, 2011b. a, b, c
Heymsfield, A. and Westbrook, C.: A review of cloud particle types in storms and their contributions to rainfall and snowfall, Atmos. Res., 97, 273–282, 2010. a
Heyn, I., Block, K., Mülmenstädt, J., Gryspeerdt, E., Kühne, P., Salzmann, M., and Quaas, J.: Assessment of simulated aerosol effective radiative forcings in the terrestrial spectrum, Geophys. Res. Lett., 44, 1001–1007, https://doi.org/10.1002/2016GL071975, 2017. a
Hill, A.: The Kinematic Driver (KiD) model code, GitHub [code], https://github.com/Adehill/KiD-A.git (last access: 5 January 2023), 2023. a
Hill, A. A., Shipway, B. J., and Boutle, I. A.: How sensitive are aerosol-precipitation interactions to the warm rain representation?, J. Adv. Model. Earth Sy., 7, 987–1004, https://doi.org/10.1002/2014MS000422, 2015. a
Hill, A. A., Lebo, Z. J., Andrejczuk, M., Arabas, S., Dziekan, P., Field, P., Gettelman, A., Hoffmann, F., Pawlowska, H., Onishi, R., and Vié, B.: Toward a Numerical Benchmark for Warm Rain Processes, J. Atmos. Sci., 80, 1329–1359, https://doi.org/10.1175/JAS-D-21-0275.1, 2023. a
Hoffmann, F., Raasch, S., and Noh, Y.: Entrainment of aerosols and their activation in a shallow cumulus cloud studied with a coupled LCM–LES approach, Atmos. Res., 156, 43–57, https://doi.org/10.1016/j.atmosres.2014.12.008, 2015. a
Hogan, R. J., Illingworth, A. J., Sauvageot, H., and Gaussiat, N.: Stratocumulus Liquid Water Content from Dual-Wavelength Radar, J. Atmos. Ocean. Techn., 17, 379–386, 2000. a
Hogan, R. J., Westbrook, C. D., Illingworth, A. J., Wood, C. R., and Donovan, D. P.: A Variational Scheme for Retrieving Ice Cloud Properties from Combined Radar, Lidar, and Infrared Radiometer, J. Atmos. Sci., 69, 634–654, 2012. 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
Hoose, C. and Möhler, O.: Heterogeneous ice nucleation on atmospheric aerosols: a review of results from laboratory experiments, Atmos. Chem. Phys., 12, 9817–9854, https://doi.org/10.5194/acp-12-9817-2012, 2012. a, b
Kahn, R. A., Andrews, E., Brock, C. A., Chin, M., Feingold, G., Gettelman, A., Levy, R. C., Murphy, D. M., Nenes, A., Pierce, J. R., Popp, T., Redemann, J., Sayer, A. M., da Silva, A. M., Sogacheva, L., and Stier, P.: Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within-The-Atmosphere Observations: A Three-Way Street, Rev. Geophys., 61, e2022RG000796, https://doi.org/10.1029/2022RG000796, 2023. a
Kanji, Z. A., Ladino, L. A., Wex, H., Boose, Y., Burkert-Kohn, M., Cziczo, D. J., and Krämer, M.: Overview of Ice Nucleating Particles, Meteor. Mon., 58, 1.1–1.33, 2017. a
Klein, S. A., McCoy, R. B., Morrison, H., Ackerman, A. S., Avramov, A., de Boer, G., Chen, M., Cole, J. N. S., Del Genio, A. D., Falk, M., Foster, M. J., Fridlind, A., Golaz, J.-C., Hashino, T., Harrington, J. Y., Hoose, C., Khairoutdinov, M. F., Larson, V. E., Liu, X., Luo, Y., McFarquhar, G. M., Menon, S., Neggers, R. A. J., Park, S., Poellot, M. R., Schmidt, J. M., Sednev, I., Shipway, B. J., Shupe, M. D., Spangenberg, D. A., Sud, Y. C., Turner, D. D., Veron, D. E., von Salzen, K., Walker, G. K., Wang, Z., Wolf, A. B., Xie, S., Xu, K.-M., Yang, F., and Zhang, G.: Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. I: single-layer cloud, Q. J. Roy. Meteor. Soc., 135, 979–1002, https://doi.org/10.1002/qj.416, 2009. a, b
Kneifel, S., Redl, S., Orlandi, E., Löhnert, U., Cadeddu, M. P., Turner, D. D., and Chen, M.: Snow Scattering Signals in Ground-Based Passive Microwave Radiometer Measurements, J. Appl. Meteorol. Clim., 54, 1712–1729, 2015. a
Lee, J., Seifert, P., Hashino, T., Maahn, M., Fabian, S., and Knoth, O.: Numerical evidence that the impact of CCN and INP concentrations on mixed-phase clouds is observable with cloud radars, Zenodo [data set], https://doi.org/10.5281/zenodo.8257078, 2023. a
Lebo, Z. J. and Seinfeld, J. H.: A continuous spectral aerosol-droplet microphysics model, Atmos. Chem. Phys., 11, 12297–12316, https://doi.org/10.5194/acp-11-12297-2011, 2011. a
Liou, K.-N. and Ou, S. S. C.: Light scattering by ice crystals: fundamentals and applications, Prog. Energ. Combust., 30, 267–306, 2004. a
Maahn, M., Hoffmann, F., Shupe, M. D., de Boer, G., Matrosov, S. Y., and Luke, E. P.: Can liquid cloud microphysical processes be used for vertically pointing cloud radar calibration?, Atmos. Meas. Tech., 12, 3151–3171, https://doi.org/10.5194/amt-12-3151-2019, 2019. a
Magono, C. and Lee, C. W.: Meteorological classification of natural snow crystals, Journal of the Faculty of Science, Hokkaido University, Series 7 (Geophysics), 2, 321–335, 1966. a
Maherndl, N., Maahn, M., Tridon, F., Leinonen, J., Ori, D., and Kneifel, S.: A riming-dependent parameterization of scattering by snowflakes using the self-similar Rayleigh–Gans Approximation, Q. J. Roy. Meteor. Soc., 149, 3562–3581, https://doi.org/10.1002/qj.4573, 2023. a
Matsui, T., Iguchi, T., Li, X., Han, M.-I., Tao, W.-K., Peters-Lidard, C., Kummerow, C., Lang, S., Meneghini, R., and Liao, L.: Evaluation of cloud microphysics schemes in simulations of a winter storm using radar and radiometer measurements, J. Geophys. Res.-Atmos., 124, 639–663, 2019. a
McFarquhar, G. M. and Heymsfield, A. J.: Parameterization of Tropical Cirrus Ice Crystal Size Distributions and Implications for Radiative Transfer: Method and Preliminary Results, J. Atmos. Sci., 54, 2187–2200, 1997. 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
Miltenberger, A. K., Field, P. R., Hill, A. A., Rosenberg, P., Shipway, B. J., Wilkinson, J. M., Scovell, R., and Blyth, A. M.: Aerosol–cloud interactions in mixed-phase convective clouds – Part 1: Aerosol perturbations, Atmos. Chem. Phys., 18, 3119–3145, https://doi.org/10.5194/acp-18-3119-2018, 2018. a
Mishchenko, M. I., Travis, L. D., and Lacis, A. A.: Scattering, Absorption, and Emission of Light by Small Particles, Cambridge University Press, ISBN 9780521782524, 1996. a
Mitchell, D. L.: Use of Mass- and Area-Dimensional Power Laws for Determining Precipitation Particle Terminal Velocities, J. Atmos. Sci., 53, 1710–1723, 1996. a
Morrison, H., Curry, J. A., and Khvorostyanov, V. I.: A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part I: Description, J. Atmos. Sci., 62, 1665–1677, 2005. a
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes, Mon. Weather Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009. a
Morrison, H., Zuidema, P., Ackerman, A. S., Avramov, A., de Boer, G., Fan, J., Fridlind, A. M., Hashino, T., Harrington, J. Y., Luo, Y., Ovchinnikov, M., and Shipway, B.: Intercomparison of cloud model simulations of Arctic mixed-phase boundary layer clouds observed during SHEBA/FIRE-ACE, J. Adv. Model. Earth Sy., 3, M05001, https://doi.org/10.1029/2011MS000066, 2011. a, b, c
Morrison, H., de Boer, G., Feingold, G., Harrington, J., Shupe, M. D., and Sulia, K.: Resilience of persistent Arctic mixed-phase clouds, Nat. Geosci., 5, 11–17, https://doi.org/10.1038/ngeo1332, 2012. a
Mülmenstädt, J., Stevens, B., and Bony, S.: Cloud-top radiative cooling: a factor to be considered in convective self-aggregation, J. Adv. Model. Earth Sy., 7, 700–714, 2015. a
Ong, C. R., Koike, M., Hashino, T., and Miura, H.: Modeling Performance of SCALE‐AMPS: Simulations of Arctic Mixed‐Phase Clouds Observed During SHEBA, J. Adv. Model. Earth Sy., 14, 1–22, https://doi.org/10.1029/2021ms002887, 2022. a, b, c, d
Onishi, R. and Takahashi, K.: A warm-bin–cold-bulk hybrid cloud microphysical model, J. Atmos. Sci., 69, 1474–1497, https://doi.org/10.1175/JAS-D-11-0166.1, 2012. a
Ori, D., Schemann, V., Karrer, M., and et al.: 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
Possner, A., Ekman, A. M., and Lohmann, U.: Cloud response and feedback processes in stratiform mixed-phase clouds perturbed by ship exhaust, Geophys. Res. Lett., 44, 1964–1972, https://doi.org/10.1002/2016GL071358, 2017. a
Radenz, M., Bühl, J., Seifert, P., Baars, H., Engelmann, R., Barja González, B., Mamouri, R.-E., Zamorano, F., and Ansmann, A.: Hemispheric contrasts in ice formation in stratiform mixed-phase clouds: disentangling the role of aerosol and dynamics with ground-based remote sensing, Atmos. Chem. Phys., 21, 17969–17994, https://doi.org/10.5194/acp-21-17969-2021, 2021. a, b, c, d
Schnitt, S., Löhnert, U., and Preusker, R.: Potential of Dual-Frequency Radar and Microwave Radiometer Synergy for Water Vapor Profiling in the Cloudy Trade Wind Environment, J. Atmos. Ocean. Tech., 37, 1973–1986, https://doi.org/10.1175/JTECH-D-19-0110.1, 2020. a
Seifert, A. and Beheng, K. D.: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description, Meteorol. Atmos. Physics, 92, 45–66, https://doi.org/10.1007/s00703-005-0112-4, 2006. a
Seifert, A., Köhler, C., and Beheng, K. D.: Aerosol-cloud-precipitation effects over Germany as simulated by a convective-scale numerical weather prediction model, Atmos. Chem. Phys., 12, 709–725, https://doi.org/10.5194/acp-12-709-2012, 2012. a, b
Seifert, P., Ansmann, A., Mattis, I., Wandinger, U., Tesche, M., Engelmann, R., Müller, D., Pérez, C., and Haustein, K.: Saharan dust and heterogeneous ice formation: Eleven years of cloud observations at a central European EARLINET site, J. Geophys. Res.-Atmos., 115, D20201, https://doi.org/10.1029/2009JD013222, 2010. a
Shipway, B. J. and Hill, A. A.: Diagnosis of systematic differences between multiple parametrizations of warm rain microphysics using a kinematic framework, Q. J. Roy. Meteor. Soc., 138, 2196–2211, https://doi.org/10.1002/qj.1913, 2012. a, b, c, d
Solomon, A., de Boer, G., Creamean, J. M., McComiskey, A., Shupe, M. D., Maahn, M., and Cox, C.: The relative impact of cloud condensation nuclei and ice nucleating particle concentrations on phase partitioning in Arctic mixed-phase stratocumulus clouds, Atmos. Chem. Phys., 18, 17047–17059, https://doi.org/10.5194/acp-18-17047-2018, 2018. a, b
Tao, W. K., Chen, J. P., Li, Z., Wang, C., and Zhang, C.: Impact of aerosols on convective clouds and precipitation, Rev. Geophys., 50, RG2001, https://doi.org/10.1029/2011RG000369, 2012. a
Thompson, G., Rasmussen, R. M., and Manning, K.: Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part I: Description and Sensitivity Analysis, Mon. Weather Rev., 132, 519–542, https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2, 2004. a
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008. a
Twomey, S.: Pollution and the planetary albedo, Atmos. Environ., 8, 1251–1256, https://doi.org/10.1016/0004-6981(74)90004-3, 1974. a
Tzivion, S., Feingold, G., and Levin, Z.: An Efficient Numerical Solution to the Stochastic Collection Equation, J. Atmos. Sci., 44, 3139–3149, https://doi.org/10.1175/1520-0469(1987)044<3139:AENSTT>2.0.CO;2, 1987. a
Tzivion, S., Feingold, G., and Levin, Z.: The evolution of raindrop spectra. Part II: Collisional collection/breakup and evaporation in a rainshaft, J. Atmos. Sci., 46, 3312–3328, https://doi.org/10.1175/1520-0469(1989)046<3312:TEORSP>2.0.CO;2, 1989. a
Ulaby, F. T., Moore, R. K., and Fung, A. K.: Microwave Remote Sensing: Active and Passive, in: Artech House microwave library, Artech House, 456 pp., ISBN 9780890061909, 1981. a
Um, J. and McFarquhar, G. M.: An update on the ice particle scattering database for radiative transfer studies, J. Quant. Spectrosc. Ra., 112, 650–652, 2011. a
Vázquez-Martín, S., Kuhn, T., and Eliasson, S.: Shape dependence of snow crystal fall speed, Atmos. Chem. Phys., 21, 7545–7565, https://doi.org/10.5194/acp-21-7545-2021, 2021. a
Vié, B., Pinty, J.-P., Berthet, S., and Leriche, M.: LIMA (v1.0): A quasi two-moment microphysical scheme driven by a multimodal population of cloud condensation and ice freezing nuclei, Geosci. Model Dev., 9, 567–586, https://doi.org/10.5194/gmd-9-567-2016, 2016. a
von Lerber, A., Mech, M., Rinke, A., Zhang, D., Lauer, M., Radovan, A., Gorodetskaya, I., and Crewell, S.: Evaluating seasonal and regional distribution of snowfall in regional climate model simulations in the Arctic, Atmos. Chem. Phys., 22, 7287–7317, https://doi.org/10.5194/acp-22-7287-2022, 2022. a
Westbrook, C. D. and Illingworth, A. J.: The fall speeds of sub-100 μm ice crystals, Q. J. Roy. Meteor. Soc., 137, 1266–1281, 2011. 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
Zhang, D., Wang, Z., Kollias, P., Vogelmann, A. M., Yang, K., and Luo, T.: Ice particle production in mid-level stratiform mixed-phase clouds observed with collocated A-Train measurements, Atmos. Chem. Phys., 18, 4317–4327, https://doi.org/10.5194/acp-18-4317-2018, 2018. a, b, c
Short summary
Spectral bin model simulations of an idealized supercooled stratiform cloud were performed with the AMPS model for variable CCN and INP concentrations. We performed radar forward simulations with PAMTRA to transfer the simulations into radar observational space. The derived radar reflectivity factors were compared to observational studies of stratiform mixed-phase clouds. These studies report a similar response of the radar reflectivity factor to aerosol perturbations as we found in our study.
Spectral bin model simulations of an idealized supercooled stratiform cloud were performed with...
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