Articles | Volume 18, issue 14
https://doi.org/10.5194/amt-18-3287-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-3287-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Jul 2025
Research article |
| 21 Jul 2025
| 21 Jul 2025
Riming-dependent snowfall rate and ice water content retrievals for W-band cloud radar
Leipzig Institute of Meteorology (LIM), Leipzig University, Leipzig, Germany
now at: Geoscience and Remote Sensing Department, Delft University of Technology, Delft, the Netherlands
Alessandro Battaglia
Department of Environment, Land and Infrastructure Engineering (DIATI), Politecnico of Torino, Turin, Italy
Department of Physics and Astronomy, University of Leicester, Leicester, UK
Anton Kötsche
Leipzig Institute of Meteorology (LIM), Leipzig University, Leipzig, Germany
Maximilian Maahn
Leipzig Institute of Meteorology (LIM), Leipzig University, Leipzig, Germany
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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.
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This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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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
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Atmos. Meas. Tech., 18, 2295–2310, https://doi.org/10.5194/amt-18-2295-2025, https://doi.org/10.5194/amt-18-2295-2025, 2025
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Aida Galfione, Alessandro Battaglia, Bernat Puigdomènech Treserras, and Pavlos Kollias
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Bernat Puigdomènech Treserras, Pavlos Kollias, Alessandro Battaglia, Simone Tanelli, and Hirotaka Nakatsuka
EGUsphere, https://doi.org/10.5194/egusphere-2025-1680, https://doi.org/10.5194/egusphere-2025-1680, 2025
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In this study, we examined how seasonal sunlight variations affect the pointing of EarthCARE’s radar antenna and introduced a correction based on surface Doppler signals. This correction reduces Doppler velocity biases and improves the accuracy of the measurements. The results confirm the importance of continuous pointing characterization to ensure the quality of EarthCARE’s observations of atmospheric dynamics.
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.
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.
Marco Coppola, Alessandro Battaglia, Frederic Tridon, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-416, https://doi.org/10.5194/egusphere-2025-416, 2025
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The WIVERN conically scanning Doppler W-band radar, has the potential, for the first time, to map the mesoscale and synoptic variability of cloud dynamics, and precipitation microphysics. This study shows that the oblique angle of incidence will be advantageous compared to standard nadir-looking radars due to substantial clutter suppression over ocean surface. This feature will enable the detection and quantification of light and moderate precipitation, with improved proximity to the surface.
Marcus Klingebiel, André Ehrlich, Micha Gryschka, Nils Risse, Nina Maherndl, Imke Schirmacher, Sophie Rosenburg, Sabine Hörnig, Manuel Moser, Evelyn Jäkel, Michael Schäfer, Hartwig Deneke, Mario Mech, Christiane Voigt, and Manfred Wendisch
EGUsphere, https://doi.org/10.5194/egusphere-2025-201, https://doi.org/10.5194/egusphere-2025-201, 2025
Short summary
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Our study is using aircraft measurements from the HALO-(𝒜𝒞)³ campaign to investigate the transition from organized Arctic cloud street structures to more scattered cloud shapes. We show that lower wind speeds cause this transition. In addition we look at the changes of the cloud coverage, the height of the clouds, the cloud particles and the radiative properties.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025, https://doi.org/10.5194/amt-18-487-2025, 2025
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Ioanna Tsikoudi, Alessandro Battaglia, Christine Unal, and Eleni Marinou
EGUsphere, https://doi.org/10.5194/egusphere-2024-3164, https://doi.org/10.5194/egusphere-2024-3164, 2025
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The study simulates spectral polarimetric variables for raindrops as observed by a cloud radar. Raindrops are modelled as oblate spheroids and backscattering properties are computed via the T-matrix method including noise, turbulence and spectral averaging effects. When comparing simulations to measurements, differences on the amplitudes of polarimetric variables are noted. This shows the challenge of using simplified shapes to model raindrop polarimetric variables when moving to mm wavelengths.
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
Short summary
Short summary
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.
Benjamin M. Courtier, Alessandro Battaglia, and Kamil Mroz
Atmos. Meas. Tech., 17, 6875–6888, https://doi.org/10.5194/amt-17-6875-2024, https://doi.org/10.5194/amt-17-6875-2024, 2024
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A new millimetre-wavelength radar is used to improve methods of retrieving information about the smallest droplets that exist within clouds. The radar is shown to be able to retrieve the vertical wind speed more accurately and more frequently and to retrieve the cloud properties for clouds with lower rainfall rates and smaller droplets than would be possible using longer-wavelength radars.
Imke Schirmacher, Sabrina Schnitt, Marcus Klingebiel, Nina Maherndl, Benjamin Kirbus, André Ehrlich, Mario Mech, and Susanne Crewell
Atmos. Chem. Phys., 24, 12823–12842, https://doi.org/10.5194/acp-24-12823-2024, https://doi.org/10.5194/acp-24-12823-2024, 2024
Short summary
Short summary
During Arctic marine cold-air outbreaks, cold air flows from sea ice over open water. Roll circulations evolve, forming cloud streets. We investigate the initial circulation and cloud development using high-resolution airborne measurements. We compute the distance an air mass traveled over water (fetch) from back trajectories. Cloud streets form at 15 km fetch, cloud cover strongly increases at around 20 km, and precipitation forms at around 30 km.
Filippo Emilio Scarsi, Alessandro Battaglia, Maximilian Maahn, and Stef Lhermitte
EGUsphere, https://doi.org/10.5194/egusphere-2024-1917, https://doi.org/10.5194/egusphere-2024-1917, 2024
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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), determining that WIVERN will provide much better estimates than CloudSat, and at much smaller spatial and temporal scales.
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.
Andreas Walbröl, Janosch Michaelis, Sebastian Becker, Henning Dorff, Kerstin Ebell, Irina Gorodetskaya, Bernd Heinold, Benjamin Kirbus, Melanie Lauer, Nina Maherndl, Marion Maturilli, Johanna Mayer, Hanno Müller, Roel A. J. Neggers, Fiona M. Paulus, Johannes Röttenbacher, Janna E. Rückert, Imke Schirmacher, Nils Slättberg, André Ehrlich, Manfred Wendisch, and Susanne Crewell
Atmos. Chem. Phys., 24, 8007–8029, https://doi.org/10.5194/acp-24-8007-2024, https://doi.org/10.5194/acp-24-8007-2024, 2024
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To support the interpretation of the data collected during the HALO-(AC)3 campaign, which took place in the North Atlantic sector of the Arctic from 7 March to 12 April 2022, we analyze how unusual the weather and sea ice conditions were with respect to the long-term climatology. From observations and ERA5 reanalysis, we found record-breaking warm air intrusions and a large variety of marine cold air outbreaks. Sea ice concentration was mostly within the climatological interquartile range.
Junghwa Lee, Patric Seifert, Tempei Hashino, Maximilian Maahn, Fabian Senf, and Oswald Knoth
Atmos. Chem. Phys., 24, 5737–5756, https://doi.org/10.5194/acp-24-5737-2024, https://doi.org/10.5194/acp-24-5737-2024, 2024
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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.
Kamil Mroz, Alessandro Battaglia, and Ann M. Fridlind
Atmos. Meas. Tech., 17, 1577–1597, https://doi.org/10.5194/amt-17-1577-2024, https://doi.org/10.5194/amt-17-1577-2024, 2024
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In this study, we examine the extent to which radar measurements from space can inform us about the properties of clouds and precipitation. Surprisingly, our analysis showed that the amount of ice turning into rain was lower than expected in the current product. To improve on this, we came up with a new way to extract information about the size and concentration of particles from radar data. As long as we use this method in the right conditions, we can even estimate how dense the ice is.
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.
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.
Filippo Emilio Scarsi, Alessandro Battaglia, Frederic Tridon, Paolo Martire, Ranvir Dhillon, and Anthony Illingworth
Atmos. Meas. Tech., 17, 499–514, https://doi.org/10.5194/amt-17-499-2024, https://doi.org/10.5194/amt-17-499-2024, 2024
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The WIVERN mission, one of the two candidates to be the ESA's Earth Explorer 11 mission, aims at providing measurements of horizontal winds in cloud and precipitation systems through a conically scanning W-band Doppler radar. This work discusses four methods that can be used to characterize and correct the Doppler velocity error induced by the antenna mispointing. The proposed methodologies can be extended to other Doppler concepts featuring conically scanning or slant viewing Doppler systems.
Alessandro Battaglia, Filippo Emilio Scarsi, Kamil Mroz, and Anthony Illingworth
Atmos. Meas. Tech., 16, 3283–3297, https://doi.org/10.5194/amt-16-3283-2023, https://doi.org/10.5194/amt-16-3283-2023, 2023
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Some of the new generation of cloud and precipitation spaceborne radars will adopt conical scanning. This will make some of the standard calibration techniques impractical. This work presents a methodology to cross-calibrate radars in orbits by matching the reflectivity probability density function of ice clouds observed by the to-be-calibrated and by the reference radar in quasi-coincident locations. Results show that cross-calibration within 1 dB (26 %) is feasible.
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
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We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as the C-CLD product.
Pavlos Kollias, Bernat Puidgomènech Treserras, Alessandro Battaglia, Paloma C. Borque, and Aleksandra Tatarevic
Atmos. Meas. Tech., 16, 1901–1914, https://doi.org/10.5194/amt-16-1901-2023, https://doi.org/10.5194/amt-16-1901-2023, 2023
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The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission developed by the European Space Agency (ESA) and Japan Aerospace Exploration Agency (JAXA) features the first spaceborne 94 GHz Doppler cloud-profiling radar (CPR) with Doppler capability. Here, we describe the post-processing algorithms that apply quality control and corrections to CPR measurements and derive key geophysical variables such as hydrometeor locations and best estimates of particle sedimentation fall velocities.
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.
Heike Kalesse-Los, Anton Kötsche, Andreas Foth, Johannes Röttenbacher, Teresa Vogl, and Jonas Witthuhn
Atmos. Meas. Tech., 16, 1683–1704, https://doi.org/10.5194/amt-16-1683-2023, https://doi.org/10.5194/amt-16-1683-2023, 2023
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The Virga-Sniffer, a new modular open-source Python package tool to characterize full precipitation evaporation (so-called virga) from ceilometer cloud base height and vertically pointing cloud radar reflectivity time–height fields, is described. Results of its first application to RV Meteor observations during the EUREC4A field experiment in January–February 2020 are shown. About half of all detected clouds with bases below the trade inversion height were found to produce virga.
Frederic Tridon, Israel Silber, Alessandro Battaglia, Stefan Kneifel, Ann Fridlind, Petros Kalogeras, and Ranvir Dhillon
Atmos. Chem. Phys., 22, 12467–12491, https://doi.org/10.5194/acp-22-12467-2022, https://doi.org/10.5194/acp-22-12467-2022, 2022
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The role of ice precipitation in the Earth water budget is not well known because ice particles are complex, and their formation involves intricate processes. Riming of ice crystals by supercooled water droplets is an efficient process, but little is known about its importance at high latitudes. In this work, by exploiting the deployment of an unprecedented number of remote sensing systems in Antarctica, we find that riming occurs at much lower temperatures compared with the mid-latitudes.
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.
Alessandro Battaglia, Paolo Martire, Eric Caubet, Laurent Phalippou, Fabrizio Stesina, Pavlos Kollias, and Anthony Illingworth
Atmos. Meas. Tech., 15, 3011–3030, https://doi.org/10.5194/amt-15-3011-2022, https://doi.org/10.5194/amt-15-3011-2022, 2022
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We present an instrument simulator for a new sensor, WIVERN (WInd VElocity Radar Nephoscope), a conically scanning radar payload with Doppler capabilities, recently down-selected as one of the four candidates for the European Space Agency Earth Explorer 11 program. The mission aims at measuring horizontal winds in cloudy areas. The simulator is instrumental in the definition and consolidation of the mission requirements and the evaluation of mission performances.
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.
Alessandro Battaglia
Atmos. Meas. Tech., 14, 7809–7820, https://doi.org/10.5194/amt-14-7809-2021, https://doi.org/10.5194/amt-14-7809-2021, 2021
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Space-borne radar returns can be contaminated by artefacts caused by radiation that undergoes multiple scattering events and appears to originate from ranges well below the surface range. While such artefacts have been rarely observed from the currently deployed systems, they may become a concern in future cloud radar systems, potentially enhancing cloud cover high up in the troposphere via ghost returns.
Alessandro Battaglia, Pavlos Kollias, Ranvir Dhillon, Katia Lamer, Marat Khairoutdinov, and Daniel Watters
Atmos. Meas. Tech., 13, 4865–4883, https://doi.org/10.5194/amt-13-4865-2020, https://doi.org/10.5194/amt-13-4865-2020, 2020
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Warm rain accounts for slightly more than 30 % of the total rain amount and 70 % of the total rain area in the tropical belt and usually appears in kilometer-size cells. Spaceborne radars adopting millimeter wavelengths are excellent tools for detecting such precipitation types and for separating between the cloud and rain components. Our work highlights the benefits of operating multifrequency radars and discusses the impact of antenna footprints in quantitative estimates of liquid water paths.
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
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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
Battaglia, A. and Panegrossi, G.: What can we learn from the CloudSat radiometric mode observations of snowfall over the ice-free ocean?, Remote Sens.-Basel, 12, 3285, https://doi.org/10.3390/rs12203285, 2020. a
Battaglia, A., Martire, P., Caubet, E., Phalippou, L., Stesina, F., Kollias, P., and Illingworth, A.: Observation error analysis for the WInd VElocity Radar Nephoscope W-band Doppler conically scanning spaceborne radar via end-to-end simulations, Atmos. Meas. Tech., 15, 3011–3030, https://doi.org/10.5194/amt-15-3011-2022, 2022. a, b, c
Battaglia, A., Rizik, A., Tridon, F., and Isakeneta, I.: I and Qs simulation and processing envisaged for space-borne polarisation Diversity Doppler Radars, IEEE T. Geosci. Remote, 63, 1–14 pp., https://doi.org/10.1109/TGRS.2025.3529672, 2024. a
Billault-Roux, A.-C. and Berne, A.: Integrated water vapor and liquid water path retrieval using a single-channel radiometer, Atmos. Meas. Tech., 14, 2749–2769, https://doi.org/10.5194/amt-14-2749-2021, 2021. a, b
Bukovčić, P., Ryzhkov, A., and Zrnić, D.: Polarimetric relations for snow estimation—radar verification, J. Appl. Meteorol. Clim., 59, 991–1009, https://doi.org/10.1175/JAMC-D-19-0140.1, 2020. a
Coppola, M., Battaglia, A., Tridon, F., and Kollias, P.: Improved hydrometeor detection near the Earth's surface by a conically scanning spaceborne W-band radar, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-416, 2025. a
Delanoë, J. and Hogan, R. J.: Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds, J. Geophys. Res.-Atmos., 115, D00H29, https://doi.org/10.1029/2009JD012346, 2010. a
Ebell, K. and Ritter, C.: Custom collection of microwave radiometer, and radar data from Ny-Ålesund between 1 Jan 2023 and 31 Mar 2024, ACTRIS Cloud remote sensing data centre unit (CLU) [data set], https://doi.org/10.60656/c86dcce9d89b4532, 2024. a, b
Ebell, K., Nomokonova, T., Maturilli, M., and Ritter, C.: Radiative effect of clouds at Ny-Ålesund, Svalbard, as inferred from ground-based remote sensing observations, J. Appl. Meteorol. Clim., 59, 3–22, https://doi.org/10.1175/JAMC-D-19-0080.1, 2020. a
Feldman, D. R., Aiken, A. C., Boos, W. R., Carroll, R. W. H., Chandrasekar, V., Collis, S., Creamean, J. M., de Boer, G., Deems, J., DeMott, P. J., Fan, J., Flores, A. N., Gochis, D., Grover, M., Hill, T. C. J., Hodshire, A., Hulm, E., Hume, C. C., Jackson, R., Junyent, F., Kennedy, A., Kumjian, M., Levin, E. J. T., Lundquist, J. D., O'Brien, J., Raleigh, M. S., Reithel, J., Rhoades, A., Rittger, K., Rudisill, W., Sherman, Z., Siirila-Woodburn, E., Skiles, S. M., Smith, J. N., Sullivan, R. C., Theisen, A., Tuftedal, M., Varble, A. C., Wiedlea, A., Wielandt, S., Williams, K., and Xu, Z.: The surface atmosphere integrated field laboratory (SAIL) campaign, B. Am. Meteorol. Soc., 104, E2192–E2222, https://doi.org/10.1175/BAMS-D-22-0049.1, 2023. a, b
Førland, E. J., Benestad, R., Hanssen-Bauer, I., Haugen, J. E., and Skaugen, T. E.: Temperature and precipitation development at Svalbard 1900–2100, Adv. Meteorol., 2011, 893790, https://doi.org/10.1155/2011/893790, 2011. a
Fuller, S., Marlow, S. A., Haimov, S., Burkhart, M., Shaffer, K., Morgan, A., and Snider, J. R.: W-band S–Z relationships for rimed snow particles: observational evidence from combined airborne and ground-based observations, Atmos. Meas. Tech., 16, 6123–6142, https://doi.org/10.5194/amt-16-6123-2023, 2023. a, b, c, d
Henneberger, J., Ramelli, F., Spirig, R., Omanovic, N., Miller, A. J., Fuchs, C., Zhang, H., Bühl, J., Hervo, M., Kanji, Z. A., Ohneiser, K., Radenz, M., Rösch, M., Seifert, P., and Lohmann, U.: Seeding of supercooled low stratus clouds with a UAV to study microphysical ice processes: an introduction to the CLOUDLAB project, B. Am. Meteorol. Soc., 104, E1962–E1979, https://doi.org/10.1175/BAMS-D-22-0178.1, 2023. a
Heymsfield, A. J., Matrosov, S. Y., and Wood, N. B.: Toward improving ice water content and snow-rate retrievals from radars. Part I: X and W bands, emphasizing CloudSat, J. Appl. Meteorol. Clim., 55, 2063–2090, https://doi.org/10.1175/JAMC-D-15-0290.1, 2016. a
Hiley, M. J., Kulie, M. S., and Bennartz, R.: Uncertainty analysis for CloudSat snowfall retrievals, J. Appl. Meteorol. Clim., 50, 399–418, https://doi.org/10.1175/2010JAMC2505.1, 2011. 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, b
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, b
Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H., Clerbaux, N., Cole, J., Delanoë, J., Domenech, C., Donovan, D. P., Fukuda, S., Hirakata, M., Hogan, R. J., Huenerbein, A., Kollias, P., Kubota, T., Nakajima, T., Nakajima, T. Y., Nishizawa, T., Ohno, Y., Okamoto, H., Oki, R., Sato, K., Satoh, M., Shephard, M. W., Velázquez-Blázquez, A., Wandinger, U., Wehr, T., and van Zadelhoff, G.-J.: The EarthCARE satellite: the next step forward in global measurements of clouds, aerosols, precipitation, and radiation, B. Am. Meteorol. Soc., 96, 1311–1332, https://doi.org/10.1175/BAMS-D-12-00227.1, 2015. a, b
Illingworth, A. J., Battaglia, A., Bradford, J., Forsythe, M., Joe, P., Kollias, P., Lean, K., Lori, M., Mahfouf, J.-F., Melo, S., Midthassel, R., Munro, Y., Nicol, J., Potthast, R., Rennie, M., Stein, T. H. M., Tanelli, S., Tridon, F., Walden, C. J., and Wolde, M.: WIVERN: a new satellite concept to provide global in-cloud winds, precipitation, and cloud properties, B. Am. Meteorol. Soc., 99, 1669–1687, https://doi.org/10.1175/BAMS-D-16-0047.1, 2018. a, b, c
Kalesse-Los, H., Maahn, M., Ettrichrätz, V., and Kötsche, A.: Characterization of Orography-Influenced Riming and Secondary Ice Production and Their Effects on Precipitation Rates Using Radar Polarimetry and Doppler Spectra (CORSIPP-SAIL), Tech. Rep. DOE/SC-ARM-23-046, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US), Atmospheric Radiation Measurement (ARM) Data Center; Pacific Northwest National Laboratory (PNNL), Richland, WA (US), https://doi.org/10.2172/2242406, 2023. a
Kalesse-Los, H., Maahn, M., Kötsche, A., Ettrichrätz, V., and Steinke, I.: Leipzig university W-band cloud radar, gothic (colorado), SAIL campaign second winter (15.11.2022–05.06.2023), ARM [data set], https://doi.org/10.5439/2229846, 2025. a
Kneifel, S. and Moisseev, D.: 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
Kochendorfer, J., Meyers, T. P., Hall, M. E., Landolt, S. D., Lentz, J., and Diamond, H. J.: A new reference-quality precipitation gauge wind shield, Atmos. Meas. Tech., 16, 5647–5657, https://doi.org/10.5194/amt-16-5647-2023, 2023. a
Kollias, P., Puidgomènech Treserras, B., Battaglia, A., Borque, P. C., and Tatarevic, A.: Processing reflectivity and Doppler velocity from EarthCARE's cloud-profiling radar: the C-FMR, C-CD and C-APC products, Atmos. Meas. Tech., 16, 1901–1914, https://doi.org/10.5194/amt-16-1901-2023, 2023. a
Küchler, N., Kneifel, S., Löhnert, U., Kollias, P., Czekala, H., and Rose, T.: A W-band radar–radiometer system for accurate and continuous monitoring of clouds and precipitation, J. Atmos. Ocean. Tech., 34, 2375–2392, https://doi.org/10.1175/JTECH-D-17-0019.1, 2017. a
Kulie, M. S. and Bennartz, R.: Utilizing spaceborne radars to retrieve dry snowfall, J. Appl. Meteorol. Clim., 48, 2564–2580, https://doi.org/10.1175/2009JAMC2193.1, 2009. a, b, c
Kötsche, A., Myagkov, A., von Terzi, L., Maahn, M., Ettrichrätz, V., Vogl, T., Ryzhkov, A., Bukovcic, P., Ori, D., and Kalesse-Los, H.: Investigating KDP signatures inside and below the dendritic growth layer with W-band Doppler Radar and in situ snowfall camera, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-734, 2025. a
Kyrouac, J., Shi, Y., and Tuftedal, M.: “Surface Meteorological Instrumentation (MET)”, Atmospheric Radiation Measurement (ARM) user facility, ARM [data set], https://doi.org/10.5439/1786358, 2025. a
List, R. and Schemenauer, R. S.: Free-fall behavior of planar snow crystals, conical graupel and small hail, J. Atmos. Sci., 28, 110–115, https://doi.org/10.1175/1520-0469(1971)028<0110:FFBOPS>2.0.CO;2, 1971. a
Liu, G.: Deriving snow cloud characteristics from CloudSat observations, J. Geophys. Res.-Atmos., 113, D00A09, https://doi.org/10.1029/2007JD009766, 2008. a, b
Maahn, M. and Maherndl, N.: Video In Situ Snowfall Sensor (VISSS) data for Ny-Ålesund (2021–2022) [dataset publication series], PANGAEA [data set], https://doi.org/10.1594/PANGAEA.958537, 2023a. a
Maahn, M. and Moisseev, D.: Video In Situ Snowfall Sensor (VISSS) data for Hyytiälä (2021–2022) [dataset publication series], PANGAEA [data set], https://doi.org/10.1594/PANGAEA.959046, 2023. a
Maahn, M. and Maherndl, N.: Video In Situ Snowfall Sensor (VISSS) data for Ny-Ålesund (July 2022–December 2023) [dataset publication series], PANGAEA [data set], https://doi.org/10.1594/PANGAEA.965766, 2024. a
Maahn, M., Burgard, C., Crewell, S., Gorodetskaya, I. V., Kneifel, S., Lhermitte, S., Van Tricht, K., and van Lipzig, N. P. M.: How does the spaceborne radar blind zone affect derived surface snowfall statistics in polar regions?, J. Geophys. Res.-Atmos., 119, 13604–13620, https://doi.org/10.1002/2014JD022079, 2014. a
Maahn, M., Turner, D. D., Löhnert, U., Posselt, D. J., Ebell, K., Mace, G. G., and Comstock, J. M.: Optimal estimation retrievals and their uncertainties: what every atmospheric scientist should know, B. Am. Meteorol. Soc., 101, E1512–E1523, https://doi.org/10.1175/BAMS-D-19-0027.1, 2020. a
Maahn, M., Moisseev, D., Steinke, I., Maherndl, N., and Shupe, M. D.: Introducing the Video In Situ Snowfall Sensor (VISSS), Atmos. Meas. Tech., 17, 899–919, https://doi.org/10.5194/amt-17-899-2024, 2024. a, b
Maahn, M., Ettrichraetz, V., and Steinke, I.: VISSS raw data from SAIL at gothic from November 2022 to june 2023, ARM [data set], https://doi.org/10.5439/2278627, 2025. 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, b, c, d, e, f, g, h, i, j
Maherndl, N., Moser, M., Lucke, J., Mech, M., Risse, N., Schirmacher, I., and Maahn, M.: Quantifying riming from airborne data during the HALO-(AC)3 campaign, Atmos. Meas. Tech., 17, 1475–1495, https://doi.org/10.5194/amt-17-1475-2024, 2024a. a, b, c, d
Maherndl, N., Moser, M., Schirmacher, I., Bansemer, A., Lucke, J., Voigt, C., and Maahn, M.: How does riming influence the observed spatial variability of ice water in mixed-phase clouds?, Atmos. Chem. Phys., 24, 13935–13960, https://doi.org/10.5194/acp-24-13935-2024, 2024b. a
Mason, S. L., Chiu, C. J., Hogan, R. J., Moisseev, D., and Kneifel, S.: Retrievals of riming and snow density from vertically pointing Doppler Radars, J. Geophys. Res.,Atm., 123, 13807–13834, https://doi.org/10.1029/2018JD028603, 2018a. a
Mason, S. L., Chiu, C. J., Hogan, R. J., Moisseev, D., 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, 2018b. a
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Short summary
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.
Accurate measurements of ice water content (IWC) and snowfall rate (SR) are challenging due to...
