Articles | Volume 14, issue 1
https://doi.org/10.5194/amt-14-511-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-511-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jan 2021
Research article |
| 25 Jan 2021
| 25 Jan 2021
Linking rain into ice microphysics across the melting layer in stratiform rain: a closure study
National Centre for Earth Observation, University of Leicester, Leicester, UK
Alessandro Battaglia
Earth Observation Science, Department of Physics and Astronomy, University of Leicester, Leicester, UK
Department of Environmental, Land and Infrastructure Engineering (DIATI), Politecnico of Turin, Turin, Italy
Stefan Kneifel
Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Leonie von Terzi
Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Markus Karrer
Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Davide Ori
Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Related authors
Karina McCusker, Chris Westbrook, Alessandro Battaglia, Kamil Mroz, Benjamin M. Courtier, Peter G. Huggard, Hui Wang, Richard Reeves, Christopher J. Walden, Richard Cotton, Stuart Fox, and Anthony J. Baran
EGUsphere, https://doi.org/10.5194/egusphere-2025-3974, https://doi.org/10.5194/egusphere-2025-3974, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
This work presents the first known retrievals of ice cloud and snowfall properties using G-band radar, representing a major step forward in the use of high-frequency radar for atmospheric remote sensing. We present theory and simulations to show that ice water content (IWC) and snowfall rate (S) can be retrieved efficiently with a single frequency G-band radar if the mass of a wavelength-sized particle is known or can be assumed, while details of the particle size distribution are not required.
Velibor Pejcic, Kamil Mroz, Kai Mühlbauer, and Silke Trömel
EGUsphere, https://doi.org/10.5194/egusphere-2025-1414, https://doi.org/10.5194/egusphere-2025-1414, 2025
Short summary
Short summary
Estimating the proportions of individual hydrometeor types (hydrometeor partitioning ratios, HPRs) in a mixture of a resolved radar volume and their evaluation is challenging. This study has three objectives, (1) to evaluate HPR retrievals, (2) to exploit the combination of dual-frequency (DF) space-borne radar (SR) and dual-polarisation (DP) ground-based radar (GR) observations for estimating HPRs based on SR DF observations and (3) to further improve HPR estimates based on DP GR observations.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Kamil Mroz, Alessandro Battaglia, Cuong Nguyen, Andrew Heymsfield, Alain Protat, and Mengistu Wolde
Atmos. Meas. Tech., 14, 7243–7254, https://doi.org/10.5194/amt-14-7243-2021, https://doi.org/10.5194/amt-14-7243-2021, 2021
Short summary
Short summary
A method for estimating microphysical properties of ice clouds based on radar measurements is presented. The algorithm exploits the information provided by differences in the radar response at different frequency bands in relation to changes in the snow morphology. The inversion scheme is based on a statistical relation between the radar simulations and the properties of snow calculated from in-cloud sampling.
Karina McCusker, Chris Westbrook, Alessandro Battaglia, Kamil Mroz, Benjamin M. Courtier, Peter G. Huggard, Hui Wang, Richard Reeves, Christopher J. Walden, Richard Cotton, Stuart Fox, and Anthony J. Baran
EGUsphere, https://doi.org/10.5194/egusphere-2025-3974, https://doi.org/10.5194/egusphere-2025-3974, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
This work presents the first known retrievals of ice cloud and snowfall properties using G-band radar, representing a major step forward in the use of high-frequency radar for atmospheric remote sensing. We present theory and simulations to show that ice water content (IWC) and snowfall rate (S) can be retrieved efficiently with a single frequency G-band radar if the mass of a wavelength-sized particle is known or can be assumed, while details of the particle size distribution are not required.
Henning Dorff, Florian Ewald, Heike Konow, Mario Mech, Davide Ori, Vera Schemann, Andreas Walbröl, Manfred Wendisch, and Felix Ament
Atmos. Chem. Phys., 25, 8329–8354, https://doi.org/10.5194/acp-25-8329-2025, https://doi.org/10.5194/acp-25-8329-2025, 2025
Short summary
Short summary
Using observations of an Arctic atmospheric river (AR) from a long-range research aircraft, we analyse how moisture transported into the Arctic by the AR is transformed and how it interacts with the Arctic environment. The moisture transport divergence is the main driver of local moisture change over time. Surface precipitation and evaporation are rather weak when averaged over extended AR sectors, although considerable heterogeneity of precipitation within the AR is observed.
Manfred Wendisch, Benjamin Kirbus, Davide Ori, Matthew D. Shupe, Susanne Crewell, Harald Sodemann, and Vera Schemann
EGUsphere, https://doi.org/10.5194/egusphere-2025-2062, https://doi.org/10.5194/egusphere-2025-2062, 2025
Short summary
Short summary
Aircraft observations of air parcels moving into and out of the Arctic are reported. From the data, heating and cooling as well as drying and moistening of the air masses along their way into and out of the Arctic could be measured for the first time. These data enable to evaluate if numerical weather prediction models are able to accurately represent these air mass transformations. This work helps to model the future climate changes in the Arctic, which are important for mid-latitude weather.
Velibor Pejcic, Kamil Mroz, Kai Mühlbauer, and Silke Trömel
EGUsphere, https://doi.org/10.5194/egusphere-2025-1414, https://doi.org/10.5194/egusphere-2025-1414, 2025
Short summary
Short summary
Estimating the proportions of individual hydrometeor types (hydrometeor partitioning ratios, HPRs) in a mixture of a resolved radar volume and their evaluation is challenging. This study has three objectives, (1) to evaluate HPR retrievals, (2) to exploit the combination of dual-frequency (DF) space-borne radar (SR) and dual-polarisation (DP) ground-based radar (GR) observations for estimating HPRs based on SR DF observations and (3) to further improve HPR estimates based on DP GR observations.
Anton Kötsche, Alexander Myagkov, Leonie von Terzi, Maximilian Maahn, Veronika Ettrichrätz, Teresa Vogl, Alexander Ryzhkov, Petar Bukovcic, Davide Ori, and Heike Kalesse-Los
EGUsphere, https://doi.org/10.5194/egusphere-2025-734, https://doi.org/10.5194/egusphere-2025-734, 2025
Short summary
Short summary
Our study combines radar observations of snowf with snowfall camera observations on the ground to enhance our understanding of radar variables and snowfall properties. We found that values of an important radar variable (KDP) can be related to many different snow particle properties and number concentrations. We were able to constrain which particle sizes contribute to KDP by using computer models of snowflakes and showed which microphysical processes during snow formation can influence KDP.
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
Short summary
Short summary
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.
Theresa Kiszler, Davide Ori, and Vera Schemann
Atmos. Chem. Phys., 24, 10039–10053, https://doi.org/10.5194/acp-24-10039-2024, https://doi.org/10.5194/acp-24-10039-2024, 2024
Short summary
Short summary
Microphysical processes impact the phase-partitioning of clouds. In this study we evaluate these processes while focusing on low-level Arctic clouds. To achieve this we used an extensive simulation set in combination with a new diagnostic tool. This study presents our findings on the relevance of these processes and their behaviour under different thermodynamic regimes.
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona M. Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schaefer, Vera Schemann, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
Atmos. Chem. Phys., 24, 8865–8892, https://doi.org/10.5194/acp-24-8865-2024, https://doi.org/10.5194/acp-24-8865-2024, 2024
Short summary
Short summary
The Arctic is warming faster than the rest of the globe. Warm-air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold-air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to mid-latitude weather. In our article, we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented with state-of-the-art remote-sensing and in situ measurement devices.
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
Short summary
Short summary
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.
Giovanni Chellini, Rosa Gierens, Kerstin Ebell, Theresa Kiszler, Pavel Krobot, Alexander Myagkov, Vera Schemann, and Stefan Kneifel
Earth Syst. Sci. Data, 15, 5427–5448, https://doi.org/10.5194/essd-15-5427-2023, https://doi.org/10.5194/essd-15-5427-2023, 2023
Short summary
Short summary
We present a comprehensive quality-controlled dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region, and substantially warm the surface. However, our understanding of microphysical processes in these clouds is incomplete. This dataset includes a comprehensive set of variables which allow for extensive investigation of such processes in LLMPCs at the site.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Frederic Tridon, Israel Silber, Alessandro Battaglia, Stefan Kneifel, Ann Fridlind, Petros Kalogeras, and Ranvir Dhillon
Atmos. Chem. Phys., 22, 12467–12491, https://doi.org/10.5194/acp-22-12467-2022, https://doi.org/10.5194/acp-22-12467-2022, 2022
Short summary
Short summary
The role of ice precipitation in the Earth water budget is not well known because ice particles are complex, and their formation involves intricate processes. Riming of ice crystals by supercooled water droplets is an efficient process, but little is known about its importance at high latitudes. In this work, by exploiting the deployment of an unprecedented number of remote sensing systems in Antarctica, we find that riming occurs at much lower temperatures compared with the mid-latitudes.
Leonie von Terzi, José Dias Neto, Davide Ori, Alexander Myagkov, and Stefan Kneifel
Atmos. Chem. Phys., 22, 11795–11821, https://doi.org/10.5194/acp-22-11795-2022, https://doi.org/10.5194/acp-22-11795-2022, 2022
Short summary
Short summary
We present a statistical analysis of ice microphysical processes (IMP) in mid-latitude clouds. Combining various radar approaches, we find that the IMP active at −20 to −10 °C seems to be the main driver of ice particle size, shape and concentration. The strength of aggregation at −20 to −10 °C correlates with the increase in concentration and aspect ratio of locally formed ice particles. Despite ongoing aggregation, the concentration of ice particles stays enhanced until −4 °C.
Alexander Myagkov and Davide Ori
Atmos. Meas. Tech., 15, 1333–1354, https://doi.org/10.5194/amt-15-1333-2022, https://doi.org/10.5194/amt-15-1333-2022, 2022
Short summary
Short summary
This study provides equations to characterize random errors of spectral polarimetric observations from cloud radars. The results can be used for a broad spectrum of applications. For instance, accurate error characterization is essential for advanced retrievals of microphysical properties of clouds and precipitation. Moreover, error characterization allows for the use of measurements from polarimetric cloud radars to potentially improve weather forecasts.
Cuong M. Nguyen, Mengistu Wolde, Alessandro Battaglia, Leonid Nichman, Natalia Bliankinshtein, Samuel Haimov, Kenny Bala, and Dirk Schuettemeyer
Atmos. Meas. Tech., 15, 775–795, https://doi.org/10.5194/amt-15-775-2022, https://doi.org/10.5194/amt-15-775-2022, 2022
Short summary
Short summary
An analysis of airborne triple-frequency radar and almost perfectly co-located coincident in situ data from an Arctic storm confirms the main findings of modeling work with radar dual-frequency ratios (DFRs) at different zones of the DFR plane associated with different ice habits. High-resolution CPI images provide accurate identification of rimed particles within the DFR plane. The relationships between the triple-frequency signals and cloud microphysical properties are also presented.
Teresa Vogl, Maximilian Maahn, Stefan Kneifel, Willi Schimmel, Dmitri Moisseev, and Heike Kalesse-Los
Atmos. Meas. Tech., 15, 365–381, https://doi.org/10.5194/amt-15-365-2022, https://doi.org/10.5194/amt-15-365-2022, 2022
Short summary
Short summary
We are using machine learning techniques, a type of artificial intelligence, to detect graupel formation in clouds. The measurements used as input to the machine learning framework were performed by cloud radars. Cloud radars are instruments located at the ground, emitting radiation with wavelenghts of a few millimeters vertically into the cloud and measuring the back-scattered signal. Our novel technique can be applied to different radar systems and different weather conditions.
Silke Trömel, Clemens Simmer, Ulrich Blahak, Armin Blanke, Sabine Doktorowski, Florian Ewald, Michael Frech, Mathias Gergely, Martin Hagen, Tijana Janjic, Heike Kalesse-Los, Stefan Kneifel, Christoph Knote, Jana Mendrok, Manuel Moser, Gregor Köcher, Kai Mühlbauer, Alexander Myagkov, Velibor Pejcic, Patric Seifert, Prabhakar Shrestha, Audrey Teisseire, Leonie von Terzi, Eleni Tetoni, Teresa Vogl, Christiane Voigt, Yuefei Zeng, Tobias Zinner, and Johannes Quaas
Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, https://doi.org/10.5194/acp-21-17291-2021, 2021
Short summary
Short summary
The article introduces the ACP readership to ongoing research in Germany on cloud- and precipitation-related process information inherent in polarimetric radar measurements, outlines pathways to inform atmospheric models with radar-based information, and points to remaining challenges towards an improved fusion of radar polarimetry and atmospheric modelling.
Markus Karrer, Axel Seifert, Davide Ori, and Stefan Kneifel
Atmos. Chem. Phys., 21, 17133–17166, https://doi.org/10.5194/acp-21-17133-2021, https://doi.org/10.5194/acp-21-17133-2021, 2021
Short summary
Short summary
Modeling precipitation is of great relevance, e.g., for mitigating damage caused by extreme weather. A key component in accurate precipitation modeling is aggregation, i.e., sticking together of snowflakes. Simulating aggregation is difficult due to multiple parameters that are not well-known. Knowing how these parameters affect aggregation can help its simulation. We put new parameters in the model and select a combination of parameters with which the model can simulate observations better.
Kamil Mroz, Alessandro Battaglia, Cuong Nguyen, Andrew Heymsfield, Alain Protat, and Mengistu Wolde
Atmos. Meas. Tech., 14, 7243–7254, https://doi.org/10.5194/amt-14-7243-2021, https://doi.org/10.5194/amt-14-7243-2021, 2021
Short summary
Short summary
A method for estimating microphysical properties of ice clouds based on radar measurements is presented. The algorithm exploits the information provided by differences in the radar response at different frequency bands in relation to changes in the snow morphology. The inversion scheme is based on a statistical relation between the radar simulations and the properties of snow calculated from in-cloud sampling.
Mariko Oue, Pavlos Kollias, Sergey Y. Matrosov, Alessandro Battaglia, and Alexander V. Ryzhkov
Atmos. Meas. Tech., 14, 4893–4913, https://doi.org/10.5194/amt-14-4893-2021, https://doi.org/10.5194/amt-14-4893-2021, 2021
Short summary
Short summary
Multi-wavelength radar measurements provide capabilities to identify ice particle types and growth processes in clouds beyond the capabilities of single-frequency radar measurements. This study introduces Doppler velocity and polarimetric radar observables into the multi-wavelength radar reflectivity measurement to improve identification analysis. The analysis clearly discerns snowflake aggregation and riming processes and even early stages of riming.
Katia Lamer, Mariko Oue, Alessandro Battaglia, Richard J. Roy, Ken B. Cooper, Ranvir Dhillon, and Pavlos Kollias
Atmos. Meas. Tech., 14, 3615–3629, https://doi.org/10.5194/amt-14-3615-2021, https://doi.org/10.5194/amt-14-3615-2021, 2021
Short summary
Short summary
Observations collected during the 25 February 2020 deployment of the VIPR at the Stony Brook Radar Observatory clearly demonstrate the potential of G-band radars for cloud and precipitation research. The field experiment, which coordinated an X-, Ka-, W- and G-band radar, revealed that the differential reflectivity from Ka–G band pair provides larger signals than the traditional Ka–W pairing underpinning an increased sensitivity to smaller amounts of liquid and ice water mass and sizes.
Davide Ori, Leonie von Terzi, Markus Karrer, and Stefan Kneifel
Geosci. Model Dev., 14, 1511–1531, https://doi.org/10.5194/gmd-14-1511-2021, https://doi.org/10.5194/gmd-14-1511-2021, 2021
Short summary
Short summary
Snowflakes have very complex shapes, and modeling their properties requires vast computing power. We produced a large number of realistic snowflakes and modeled their average properties by leveraging their fractal structure. Our approach allows modeling the properties of big ensembles of snowflakes, taking into account their natural variability, at a much lower cost. This enables the usage of remote sensing instruments, such as radars, to monitor the evolution of clouds and precipitation.
Jie Gong, Xiping Zeng, Dong L. Wu, S. Joseph Munchak, Xiaowen Li, Stefan Kneifel, Davide Ori, Liang Liao, and Donifan Barahona
Atmos. Chem. Phys., 20, 12633–12653, https://doi.org/10.5194/acp-20-12633-2020, https://doi.org/10.5194/acp-20-12633-2020, 2020
Short summary
Short summary
This work provides a novel way of using polarized passive microwave measurements to study the interlinked cloud–convection–precipitation processes. The magnitude of differences between polarized radiances is found linked to ice microphysics (shape, size, orientation and density), mesoscale dynamic and thermodynamic structures, and surface precipitation. We conclude that passive sensors with multiple polarized channel pairs may serve as cheaper and useful substitutes for spaceborne radar sensors.
Alexander Myagkov, Stefan Kneifel, and Thomas Rose
Atmos. Meas. Tech., 13, 5799–5825, https://doi.org/10.5194/amt-13-5799-2020, https://doi.org/10.5194/amt-13-5799-2020, 2020
Short summary
Short summary
This study shows two methods for evaluating the reflectivity calibration of W-band cloud radars. Both methods use natural rain as a reference target. The first method is based on spectral polarimetric observations and requires a polarimetric cloud radar with a scanner. The second method utilizes disdrometer observations and can be applied to scanning and vertically pointed radars. Both methods show consistent results and can be applied for operational monitoring of measurement quality.
Frédéric Tridon, Alessandro Battaglia, and Stefan Kneifel
Atmos. Meas. Tech., 13, 5065–5085, https://doi.org/10.5194/amt-13-5065-2020, https://doi.org/10.5194/amt-13-5065-2020, 2020
Short summary
Short summary
The droplets and ice crystals composing clouds and precipitation interact with microwaves and can therefore be observed by radars, but they can also attenuate the signal they emit. By combining the observations made by two ground-based radars, this study describes an original approach for estimating such attenuation. As a result, the latter can be not only corrected in the radar observations but also exploited for providing an accurate characterization of droplet and ice crystal properties.
Mario Mech, Maximilian Maahn, Stefan Kneifel, Davide Ori, Emiliano Orlandi, Pavlos Kollias, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, https://doi.org/10.5194/gmd-13-4229-2020, 2020
Short summary
Short summary
The Passive and Active Microwave TRAnsfer tool (PAMTRA) is a public domain software package written in Python and Fortran for the simulation of microwave remote sensing observations. PAMTRA models the interaction of radiation with gases, clouds, precipitation, and the surface using either in situ observations or model output as input parameters. The wide range of applications is demonstrated for passive (radiometer) and active (radar) instruments on ground, airborne, and satellite platforms.
Cited articles
Atlas, D., Srivastava, R. C., and Sekhon, R. S.: Doppler radar characteristics
of precipitation at vertical incidence, Rev. Geophys., 11, 1–35,
https://doi.org/10.1029/RG011i001p00001, 1973. a, b
Battaglia, A. and Kollias, P.: Evaluation of differential absorption radars in the 183 GHz band for profiling water vapour in ice clouds, Atmos. Meas. Tech., 12, 3335–3349, https://doi.org/10.5194/amt-12-3335-2019, 2019. a
Battaglia, A., Kummerow, C., Shin, D.-B., and Williams, C.: Toward
characterizing the effect of radar bright bands on microwave brightness
temperatures, J. Atmos. Ocean, Technol., 20, 856–871,
https://doi.org/10.1175/1520-0426(2003)020<0856:CMBTBR>2.0.CO;2,
2003. a
Battaglia, A., Kollias, P., Dhillon, R., , Roy, R., Tanelli, S., Lamer, K.,
Grecu, M., Lebsock, M., Watters, D., Mroz, K., Heymsfield, G., Li, L., and
Furukawa, K.: Space-borne cloud and precipitation radars: status, challenges
and ways forward, Rev. Geophys., 58, e2019RG000686,
https://doi.org/10.1029/2019RG000686, 2020a. a, b
Battaglia, A., Tanelli, S., Tridon, F., Kneifel, S., Leinonen, J., and Kollias,
P.: Satellite precipitation measurement, vol. 67 of Adv.Global Change
Res., chap. Triple-frequency radar retrievals, Springer, Cham, Switzerland, ISBN 978-3-030-24567-2, 2020b. a
Böhm, J. P.: A general hydrodynamic theory for mixed-phase microphysics.
Part III: Riming and aggregation, Atmos. Res., 28, 103–123,
https://doi.org/10.1016/0169-8095(92)90023-4,
1992. a
Borque, P., Luke, E., and Kollias, P.: On the Unified Estimation of Turbulence
Eddy Dissipation Rate Using Doppler Cloud Radars and Lidars, J. Geophys.
Res., 120, 5972–5989, https://doi.org/10.1002/2015JD024543, 2016. a
Brandes, E. A., Zhang, G., and Vivekanandan, J.: Corrigendum, J.
Appl. Meteorol., 44, 186–186,
https://doi.org/10.1175/1520-0450(2005)44<186:C>2.0.CO;2,
2005. 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
Devisetty, H., Jha, A. K., Das, S. K., Deshpande, S. M., MuraliKrishna, U.,
Kalekar, P. M., and Pandithurai, G.: A case study on bright band transition
from very light to heavy rain using simultaneous observations of collocated
X- and Ka-band radars, J. Earth Syst. Sci., 128, 1–10,
https://doi.org/10.1007/s12040-019-1171-0, 2019. a
Dias Neto, J., Kneifel, S., Ori, D., Trömel, S., Handwerker, J., Bohn, B., Hermes, N., Mühlbauer, K., Lenefer, M., and Simmer, C.: The TRIple-frequency and Polarimetric radar Experiment for improving process observations of winter precipitation, Earth Syst. Sci. Data, 11, 845–863, https://doi.org/10.5194/essd-11-845-2019, 2019. a, b, c
Doviak, R. J. and Zrnic, D. S.: Doppler radar and weather observations, 2nd edn.,
Academic Press, San Diego, USA, London, UK, 1993. a
Fabry, F. and Zawadzki, I.: Long-Term Radar Observations of the Melting Layer
of Precipitation and Their Interpretation, J. Atmos. Sci., 52, 838–851,
https://doi.org/10.1175/1520-0469(1995)052<0838:LTROOT>2.0.CO;2,
1995. a
Garrett, T. J., Fallgatter, C., Shkurko, K., and Howlett, D.: Fall speed measurement and high-resolution multi-angle photography of hydrometeors in free fall, Atmos. Meas. Tech., 5, 2625–2633, https://doi.org/10.5194/amt-5-2625-2012, 2012. a
Gatlin, P. N., Petersen, W. A., Knupp, K. R., and Carey, L. D.: Observed
Response of the Raindrop Size Distribution to Changes in the Melting Layer,
Atmosphere, 9, 319, https://doi.org/10.3390/atmos9080319, 2018. a, b
Giangrande, S. E., Luke, E. P., and Kollias, P.: Characterization of Vertical
Velocity and Drop Size Distribution Parameters in Widespread Precipitation at
ARM Facilities, J. Appl. Meteorol. Clim., 51, 380–391, https://doi.org/10.1175/JAMC-D-10-05000.1, 2012. a
Giangrande, S. E., Toto, T., Bansemer, A., Kumjian, M. R., Mishra, S., and
Ryzhkov, A. V.: Insights into riming and aggregation processes as revealed by
aircraft, radar, and disdrometer observations for a 27 April 2011 widespread
precipitation event, J. Geophys. Res.-Atmos., 121, 5846–5863,
https://doi.org/10.1002/2015JD024537,
2016. a
Gunn, R. and Kinzer, G. D.: The terminal velocity of fall for water droplets in
stagnant air, J. Meteorol., 6, 243–248,
https://doi.org/10.1175/1520-0469(1949)006<0243:TTVOFF>2.0.CO;2, 1949. a, b
Haynes, J. M., L'Ecuyer, T. S., Stephens, G. L., Miller, S. D., Mitrescu, C.,
Wood, N. B., and Tanelli, S.: Rainfall retrieval over the ocean with
spaceborne W-band radar, J. Geophys. Res.-Atmos., 114,
https://doi.org/10.1029/2008JD009973,
2009. a, b
Heymsfield, A., Bansemer, A., Wood, N. B., Liu, G., Tanelli, S., Sy, O. O.,
Poellot, M., and Liu, C.: Toward Improving Ice Water Content and Snow-Rate
Retrievals from Radars. Part II: Results from Three Wavelength
Radar–Collocated In Situ Measurements and CloudSat–GPM–TRMM Radar
Data, J. Appl. Meteorol. Clim., 57, 365–389,
https://doi.org/10.1175/JAMC-D-17-0164.1, 2018. a, b, c
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, 2014. 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.
Meteorol. Soc., 143, 834–844, 2017. a
Houze, R. A.: Stratiform Precipitation in Regions of Convection: A
Meteorological Paradox?, B. Am. Meteorol. Soc., 78, 2179–2196,
https://doi.org/10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2,
1997. a
Huang, G.-J., Bringi, V. N., and Thurai, M.: Orientation Angle Distributions of
Drops after an 80-m Fall Using a 2D Video Disdrometer, J. Atmos. Ocean. Tech., 25, 1717–1723, https://doi.org/10.1175/2008JTECHA1075.1, 2008. a
Kalesse, H., Szyrmer, W., Kneifel, S., Kollias, P., and Luke, E.: Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling, Atmos. Chem. Phys., 16, 2997–3012, https://doi.org/10.5194/acp-16-2997-2016, 2016. a, b
Kneifel, S. and Moisseev, D.: Long-term Statistics of Riming in non-convective 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, b
Kneifel, S., Kulie, M. S., and Bennartz, R.: A triple-frequency approach to
retrieve microphysical snowfall parameters, J. Geophys. Res.-Atmos., 116, D11203, https://doi.org/10.1029/2010JD015430, 2011. a
Kneifel, S., von Lerber, A., Tiira, J., Moisseev, D., Kollias, P., and
Leinonen, J.: Observed relations between snowfall microphysics and
triple-frequency radar measurements, J. Geophys. Res.-Atmos., 120, 6034–6055,
https://doi.org/10.1002/2015JD023156,
2015. a, b, c
Kneifel, S., Kollias, P., Battaglia, A., Leinonen, J., Maahn, M., Kalesse, H., and Tridon, F.: First observations of triple-frequency radar Doppler
spectra in snowfall: Interpretation and applications, Geophys. Res. Lett.,
43, 2225–2233, https://doi.org/10.1002/2015GL067618, 2016. a
Kollias, P., Albrecht, B. A., and Marks, F.: Why Mie?, Bulletin of the American
Meteorological Society, 83, 1471–1484, https://doi.org/10.1175/BAMS-83-10-1471, 2002. a
Leinonen, J.: High-level interface to T-matrix scattering calculations:
architecture, capabilities and limitations, Opt. Express, 22, 1655–1660,
https://doi.org/10.1364/OE.22.001655,
2014. a
Leinonen, J. and Moisseev, D.: What do triple-frequency radar signatures
reveal about aggregate snowflakes?, J. Geophys. Res.-Atmos., 120, 229–239,
https://doi.org/10.1002/2014JD022072,
2015. a, b
Leinonen, J. and Szyrmer, W.: Radar signatures of snowflake riming: A modeling
study, Earth and Space Science, 2, 346–358, https://doi.org/10.1002/2015EA000102,
2015. a
Leinonen, J. and von Lerber, A.: Snowflake Melting Simulation Using Smoothed
Particle Hydrodynamics, J. Geophys. Res.-Atmos., 123, 1811–1825,
https://doi.org/10.1002/2017JD027909, 2018. a, b
Leinonen, J., Kneifel, S., and Hogan, R. J.: Evaluation of the Rayleigh-Gans
approximation for microwave scattering by rimed snowflakes, Q. J. Roy.
Meteor. Soc., 144, 77–88, https://doi.org/10.1002/qj.3093, 2017. a
Leinonen, J., Kneifel, S., and Hogan, R. J.: Evaluation of the Rayleigh–Gans
approximation for microwave scattering by rimed snowflakes, Q. J. Roy.
Meteor. Soc., 144, 77–88, https://doi.org/10.1002/qj.3093,
2018a. a
Leinonen, J., Lebsock, M. D., Tanelli, S., Sy, O. O., Dolan, B., Chase, R. J., Finlon, J. A., von Lerber, A., and Moisseev, D.: Retrieval of snowflake microphysical properties from multifrequency radar observations, Atmos. Meas. Tech., 11, 5471–5488, https://doi.org/10.5194/amt-11-5471-2018, 2018b. a
Li, H. and Moisseev, D.: Melting Layer Attenuation at Ka- and W-Bands as
Derived From Multifrequency Radar Doppler Spectra Observations, J.
Geophys. Res.-Atmos., 124, 9520–9533,
https://doi.org/10.1029/2019JD030316, 2019. a
Li, H., Tiira, J., von Lerber, A., and Moisseev, D.: Towards the connection between snow microphysics and melting layer: insights from multifrequency and dual-polarization radar observations during BAECC, Atmos. Chem. Phys., 20, 9547–9562, https://doi.org/10.5194/acp-20-9547-2020, 2020. a
Liao, L., Meneghini, R., Tokay, A., and Bliven, L. F.: Retrieval of Snow
Properties for Ku- and Ka-Band Dual-Frequency Radar, J. Appl.
Meteorol. Clim., 55, 1845–1858, https://doi.org/10.1175/JAMC-D-15-0355.1, 2016. a
Löffler-Mang, M. and Joss, J.: An optical disdrometer for measuring size and velocity of hydrometeors, J. Atmos. Ocean. Tech.,
17, 130–139, https://doi.org/10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2, 2000. a
Löhnert, U., Schween, J. H., Acquistapace, C., Ebell, K., Maahn, M.,
Barrera-Verdejo, M., Hirsikko, A., Bohn, B., Knaps, A., O’Connor, E. J.,
Simmer, C., Wahner, A., and Crewell, S.: JOYCE: Jülich Observatory for
Cloud Evolution, B. Am. Meteorol. Soc., 96,
1157–1174, https://doi.org/10.1175/BAMS-D-14-00105.1, 2015. a
Mason, S. L., Chiu, J. C., Hogan, R. J., and Tian, L.: Improved rain rate and drop size retrievals from airborne Doppler radar, Atmos. Chem. Phys., 17, 11567–11589, https://doi.org/10.5194/acp-17-11567-2017, 2017. 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, 13,807–13,834,
https://doi.org/10.1029/2018JD028603,
2018. a
Matrosov, S. Y.: Assessment of Radar Signal Attenuation Caused by the Melting
Hydrometeor Layer, IEEE T. Geosci. Remote, 46, 1039–1047,
https://doi.org/10.1109/TGRS.2008.915757, 2008. a, b, c
Mossop, S. C.: Production of secondary ice particles during the growth of
graupel by riming, Q. J. Roy. Meteor. Soc.,
102, 45–57, https://doi.org/10.1002/qj.49710243104, 1976. a
Oraltay, R. G. and Hallett, J.: The Melting Layer: A Laboratory Investigation
of Ice Particle Melt and Evaporation near 0 ∘C, J. Appl.
Meteorol., 44, 206–220, https://doi.org/10.1175/JAM2194.1, 2005. a, b
Ori, D., von Terzi, L., Karrer, M., and Kneifel, S.: snowScatt 1.0: Consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh-Gans Approximation, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2020-359, in review, 2020a. a
Ori, D., von Terzi, L., Karrer, M., and Kneifel S.: snowScatt (Version v1.0), Zenodo, https://doi.org/10.5281/zenodo.4118245, 2020b. a
Oue, M., Kumjian, M. R., Lu, Y., Jiang, Z., Clothiaux, E. E., Verlinde, J., and
Aydin, K.: X-band polarimetric and Ka-band Doppler spectral radar
observations of a graupel producing Arctic mixed-phase cloud, J. Appl.
Meteorol. Clim., 54, 1335–1351, https://doi.org/10.1175/JAMC-D-14-0315.1, 2015a. a
Oue, M., Kumjian, M. R., Lu, Y., Verlinde, J., Aydin, K., and Clothiaux, E. E.:
Linear Depolarization Ratios of Columnar Ice Crystals in a Deep
Precipitating System over the Arctic Observed by Zenith-Pointing Ka-Band
Doppler Radar, J. Appl. Meteorol. Clim., 54, 1060–1068,
https://doi.org/10.1175/JAMC-D-15-0012.1, 2015b. a, b, c
Raupach, T. H. and Berne, A.: Correction of raindrop size distributions measured by Parsivel disdrometers, using a two-dimensional video disdrometer as a reference, Atmos. Meas. Tech., 8, 343–365, https://doi.org/10.5194/amt-8-343-2015, 2015. a
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding, WORLD SCIENTIFIC,
https://doi.org/10.1142/3171, 2000. a
Roy, R. J., Lebsock, M., Millán, L., and Cooper, K. B.: Validation of a
G-Band Differential Absorption Cloud Radar for Humidity Remote Sensing, J.
Atmos. Ocean. Tech., 37, 1085–1102, https://doi.org/10.1175/JTECH-D-19-0122.1, 2020. a
Schumacher, C. and Houze, R. A.: Stratiform Rain in the Tropics as Seen by the TRMM Precipitation Radar, J. Climate, 16, 1739–1756,
https://doi.org/10.1175/1520-0442(2003)016<1739:SRITTA>2.0.CO;2,
2003. a
Seifert, A., Blahak, U., and Buhr, R.: On the analytic approximation of bulk collision rates of non-spherical hydrometeors, Geosci. Model Dev., 7, 463–478, https://doi.org/10.5194/gmd-7-463-2014, 2014. a
Seto, S., Iguchi, T., and Oki, T.: The Basic Performance of a
Precipitation Retrieval Algorithm for the Global Precipitation Measurement
Mission's Single/Dual-Frequency Radar Measurements, IEEE T.
Geosci. Remote, 51, 5239–5251, 2013. a
Stein, T. H. M., Westbrook, C. D., and Nicol, J. C.: Fractal geometry of
aggregate snowflakes revealed by triple-wavelength radar measurements,
Geophys. Res. Lett., 42, 176–183, https://doi.org/10.1002/2014GL062170,
2015. a
Stephens, G., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C., L'Ecuyer,
T., and Lebsock, M.: CloudSat and CALIPSO within the A-Train: Ten Years of
Actively Observing the Earth System, B. Am. Meteorol. Soc., 99, 569–581,
https://doi.org/10.1175/BAMS-D-16-0324.1, 2018. a
Stephens, G. L.: Cloud Feedbacks in the Climate System: A Critical Review, J. Climate, 18, 237–273, https://doi.org/10.1175/JCLI-3243.1, 2005. a
Szyrmer, W. and Zawadzki, I.: Modeling of the Melting Layer. Part I: Dynamics
and Microphysics, J. Atmos. Sci., 56, 3573–3592,
https://doi.org/10.1175/1520-0469(1999)056<3573:MOTMLP>2.0.CO;2,
1999. a, b
Tao, W.-K., Lang, S., Zeng, X., Shige, S., and Takayabu, Y.: Relating
Convective and Stratiform Rain to Latent Heating, J. Climate, 23, 1874–1893,
https://doi.org/10.1175/2009JCLI3278.1, 2010. a
Thurai, M. and Bringi, V. N.: Application of the Generalized Gamma Model to
Represent the Full Rain Drop Size Distribution Spectra, J. Appl. Meteorol.
Clim., 57, 1197–1210, https://doi.org/10.1175/jamc-d-17-0235.1, 2018. a
Thurai, M., Bringi, V., Gatlin, P. N., Petersen, W. A., and Wingo, M. T.:
Measurements and Modeling of the Full Rain Drop Size Distribution,
Atmosphere, 10, 39, https://doi.org/10.3390/atmos10010039, 2019. a
Tridon, F. and Battaglia, A.: Dual-frequency radar Doppler spectral retrieval
of rain drop size distributions and entangled dynamics variables, J. Geophys.
Res., 120, 5585–5601, https://doi.org/10.1002/2014JD023023, 2015. a, b, c, d
Tridon, F., Battaglia, A., and Kollias, P.: Disentangling Mie and attenuation effects in rain using a Ka-W dual-wavelength Doppler spectral ratio technique, Geophys. Res. Lett., 40, 5548–5552, https://doi.org/10.1002/2013GL057454,
2013. a, b
Tridon, F., Battaglia, A., Luke, E., and Kollias, P.: Rain retrieval from
dual-frequency radar Doppler spectra: validation and potential for a
midlatitude precipitating case study, Q. J. Roy. Meteor. Soc., 143,
1363–1380, https://doi.org/10.1002/qj.3010, 2017a. a, b, c
Tridon, F., Battaglia, A., and Watters, D.: Evaporation in action sensed by
multi-wavelenth Doppler radars, J. Geophys. Res., 122, 9379–9390,
https://doi.org/10.1002/2016JD025998, 2017b. a
Tridon, F., Battaglia, A., Chase, R. J., Turk, F. J., Leinonen, J., Kneifel,
S., Mroz, K., Finlon, J., Bansemer, A., Tanelli, S., Heymsfield, A. J., and
Nesbitt, S. W.: The Microphysics of Stratiform Precipitation During OLYMPEX:
Compatibility Between Triple-Frequency Radar and Airborne In Situ
Observations, J. Geophys. Res.-Atmos., 124, 8764–8792,
https://doi.org/10.1029/2018JD029858,
2019. a, b, c
Turner, D. D., Kneifel, S., and Cadeddu, M. P.: An Improved Liquid Water
Absorption Model at Microwave Frequencies for Supercooled Liquid Water
Clouds, J. Atmos. Ocean. Tech., 33, 33–44,
https://doi.org/10.1175/JTECH-D-15-0074.1, 2016.
a
Tyynelä, J., Leinonen, J., Moisseev, D., and Nousiainen, T.: Radar
Backscattering from Snowflakes: Comparison of Fractal, Aggregate, and Soft
Spheroid Models, J. Atmos. Ocean. Tech., 28,
1365–1372, https://doi.org/10.1175/JTECH-D-11-00004.1, 2011. a
Van Tricht, K., Gorodetskaya, I. V., Lhermitte, S., Turner, D. D., Schween, J. H., and Van Lipzig, N. P. M.: An improved algorithm for polar cloud-base detection by ceilometer over the ice sheets, Atmos. Meas. Tech., 7, 1153–1167, https://doi.org/10.5194/amt-7-1153-2014, 2014. a
Vogel, J. M. and Fabry, F.: Contrasting Polarimetric Observations of
Stratiform Riming and Nonriming Events, J. Appl. Meteorol. Clim., 57,
457–476, https://doi.org/10.1175/JAMC-D-16-0370.1, 2018. a
Williams, C. R.: Reflectivity and Liquid Water Content Vertical Decomposition
Diagrams to Diagnose Vertical Evolution of Raindrop Size Distributions, J.
Atmos. Ocean. Tech., 33, 579–595, https://doi.org/10.1175/JTECH-D-15-0208.1, 2016. a
Williams, C. R., Beauchamp, R. M., and Chandrasekar, V.: Vertical Air
Motions and Raindrop Size Distributions Estimated Using Mean Doppler Velocity
Difference From 3- and 35-GHz Vertically Pointing Radars, IEEE T. Geosci. Remote, 54, 6048–6060,
https://doi.org/10.1109/TGRS.2016.2580526, 2016. a
Zawadzki, I., Fabry, F., and Szyrmer, W.: Observations of supercooled water
and secondary ice generation by a vertically pointing X-band Doppler radar,
Atmos. Res., 59-60, 343–359,
https://doi.org/10.1016/S0169-8095(01)00124-7, 2001. a, b
Zawadzki, I., Szyrmer, W., Bell, C., and Fabry, F.: Modeling of the Melting
Layer. Part III: The Density Effect, J. Atmos. Sci., 62, 3705–3723,
https://doi.org/10.1175/JAS3563.1,
2005. a, b, c
Short summary
The article examines the relationship between the characteristics of rain and the properties of the ice cloud from which the rain originated. Our results confirm the widely accepted assumption that the mass flux through the melting zone is well preserved with an exception of extreme aggregation and riming conditions. Moreover, it is shown that the mean (mass-weighted) size of particles above and below the melting zone is strongly linked, with the former being on average larger.
The article examines the relationship between the characteristics of rain and the properties of...
