Articles | Volume 25, issue 22
https://doi.org/10.5194/acp-25-16479-2025
© 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/acp-25-16479-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Nov 2025
Research article |
| 24 Nov 2025
| 24 Nov 2025
A radar view of ice microphysics and turbulence in Arctic cloud systems
School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
Mariko Oue
School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
Pavlos Kollias
School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
Environmental Science and Technologies Department, Brookhaven National Laboratory, Upton, NY, USA
Edward Luke
School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA
Environmental Science and Technologies Department, Brookhaven National Laboratory, Upton, NY, USA
Environmental Science and Technologies Department, Brookhaven National Laboratory, Upton, NY, USA
Related authors
No articles found.
Aida Galfione, Alessandro Battaglia, Bernat Puigdomènech Treserras, and Pavlos Kollias
Atmos. Meas. Tech., 18, 6747–6763, https://doi.org/10.5194/amt-18-6747-2025, https://doi.org/10.5194/amt-18-6747-2025, 2025
Short summary
Short summary
Convection drives atmospheric circulation but is difficult to observe and model. EarthCARE's radar provides the first space-based vertical wind data, capturing updrafts and downdrafts. Combined with satellite imagery from other sensors, it offers a broader view of convective storms. While resolution limits detail, cloud-top cooling helps track storm development. This combined approach improves understanding and modeling of convection.
Zhuocan Xu, Pavlos Kollias, Susmitha Sasikumar, Alessandro Battaglia, and Bernat Puigdomènech Treserras
EGUsphere, https://doi.org/10.5194/egusphere-2025-5421, https://doi.org/10.5194/egusphere-2025-5421, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
It has been a challenge to observe marine low-level clouds from space. A comparison with CloudSat climatology in this study shows a greatly improved detection of marine stratocumulus clouds and drizzle occurrence by the EarthCARE radar due to the enhanced sensitivity and full suppression of ground clutter above 0.5 km. A more accurate observational constraint on marine low clouds is now available on global scale.
Sarah Wugofski, Matthew R. Kumjian, Mariko Oue, and Pavlos Kollias
Atmos. Meas. Tech., 18, 6233–6249, https://doi.org/10.5194/amt-18-6233-2025, https://doi.org/10.5194/amt-18-6233-2025, 2025
Short summary
Short summary
Doppler spectral data inform on how particles of varying vertical velocities contribute to total backscattered power observed. Through examining three case studies, consistent features in radar moment data were found to be characteristic of multi-modal spectra. We quantified how spectrum width and mean Doppler velocity can be used to determine whether or not a layer is multi-modal. The identification criteria and methods are described in Part 1 and assessed in Part 2.
Nitika Yadlapalli Yurk, Matt D. Lebsock, Juan M. Socuellamos, Raquel Rodriguez Monje, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 18, 5141–5155, https://doi.org/10.5194/amt-18-5141-2025, https://doi.org/10.5194/amt-18-5141-2025, 2025
Short summary
Short summary
Current knowledge of the link between clouds and climate is limited by a lack of observations of the drop size distribution (DSD) within clouds, especially for the smallest drops. We demonstrate a method of retrieving DSDs down to small drop sizes using observations of drizzling marine layer clouds captured by the CloudCube millimeter-wave Doppler radar. We compare the shape of the observed spectra to theoretical expectations of radar echoes to solve for DSDs at each time step and elevation.
Marco Coppola, Alessandro Battaglia, Frederic Tridon, and Pavlos Kollias
Atmos. Meas. Tech., 18, 5071–5085, https://doi.org/10.5194/amt-18-5071-2025, https://doi.org/10.5194/amt-18-5071-2025, 2025
Short summary
Short summary
The WIVERN (WInd Velocity Radar Nephoscope) 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 the ocean surface. This feature will enable the detection and quantification of light and moderate precipitation, with improved proximity to the surface.
Laura M. Tomkins, Sandra E. Yuter, Matthew A. Miller, Mariko Oue, and Charles N. Helms
Atmos. Chem. Phys., 25, 9999–10026, https://doi.org/10.5194/acp-25-9999-2025, https://doi.org/10.5194/acp-25-9999-2025, 2025
Short summary
Short summary
This study investigates how radar-detected snow bands relate to snowfall rates during winter storms in the northeastern United States. Using over a decade of data, we found that snow bands are not consistently linked to heavy snowfall at the surface, as snow particles are often dispersed by wind before reaching the ground. These findings highlight limitations of using radar reflectivity for predicting snow rates and suggest focusing on radar echo duration to better understand snowfall patterns.
Susmitha Sasikumar, Alessandro Battaglia, Bernat Puigdomènech Treserras, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-3573, https://doi.org/10.5194/egusphere-2025-3573, 2025
Short summary
Short summary
The study present a method to estimate how much the radar signal is weakened as it passes through rain or clouds, designed to implement in the new EarthCARE satellite cloud profiling radar data. The approach builds on the method used in the CloudSat mission, with key improvements that make it robust under non-ideal instrument conditions in the early mission phase. This leads to more reliable retrieval of clouds and rainfall during initial satellite operations.
Zeen Zhu, Fan Yang, Steven Krueger, and Yangang Liu
EGUsphere, https://doi.org/10.5194/egusphere-2025-3489, https://doi.org/10.5194/egusphere-2025-3489, 2025
Short summary
Short summary
To better understand cloud behavior, we used computer simulations to study how the air mix in clouds. Our results show that the pattern of mixing seen from aircraft measurements may not reflect the true mixing process happening inside clouds. This result suggests that care is needed when using aircraft data to study the cloud mixing process and that new ways of observing clouds could offer clearer insights.
Zackary Mages, Pavlos Kollias, Bernat Puigdomènech Treserras, Paloma Borque, and Mariko Oue
Atmos. Chem. Phys., 25, 6025–6045, https://doi.org/10.5194/acp-25-6025-2025, https://doi.org/10.5194/acp-25-6025-2025, 2025
Short summary
Short summary
Convective clouds are a key component of the climate system. Using remote sensing observations during two field experiments in Houston, Texas, we identify four diurnal patterns of shallow convective clouds. We find areas more frequently experiencing shallow convective clouds, and we find areas where the vertical extent of shallow convective clouds is higher and where they are more likely to precipitate. This provides insight into the complicated environment that forms these clouds in Houston.
Francesco Manconi, Alessandro Battaglia, and Pavlos Kollias
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
Short summary
Short summary
The paper aims to study the ground reflection, or clutter, of the signal from a spaceborne radar in the context of ESA's WIVERN (WInd VElocity Radar Nephoscop) mission, which will observe in-cloud winds. Using topography and land type data, with a model of the satellite orbit and rotating antenna, simulations of scans have been run over the Piedmont region of Italy. These measurements cover the full range of the ground clutter over land for WIVERN and have allowed for analyses of the precision and accuracy of velocity observations.
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025, https://doi.org/10.5194/amt-18-1641-2025, 2025
Short summary
Short summary
A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.
Fan Yang, Hamed Fahandezh Sadi, Raymond A. Shaw, Fabian Hoffmann, Pei Hou, Aaron Wang, and Mikhail Ovchinnikov
Atmos. Chem. Phys., 25, 3785–3806, https://doi.org/10.5194/acp-25-3785-2025, https://doi.org/10.5194/acp-25-3785-2025, 2025
Short summary
Short summary
Large-eddy simulations of a convection cloud chamber show two new microphysics regimes, cloud oscillation and cloud collapse, due to haze–cloud interactions. Our results suggest that haze particles and their interactions with cloud droplets should be considered especially in polluted conditions. To properly simulate haze–cloud interactions, we need to resolve droplet activation and deactivation processes, instead of using Twomey-type activation parameterization.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
Short summary
Short summary
The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
Short summary
Short summary
This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Bernat Puigdomènech Treserras and Pavlos Kollias
Atmos. Meas. Tech., 17, 6301–6314, https://doi.org/10.5194/amt-17-6301-2024, https://doi.org/10.5194/amt-17-6301-2024, 2024
Short summary
Short summary
The paper presents a comprehensive approach to improve the geolocation accuracy of spaceborne radar and lidar systems, crucial for the successful interpretation of data from the upcoming EarthCARE mission. The paper details the technical background of the presented methods and various examples of geolocation analyses, including a short period of CloudSat observations when the star tracker was not operating properly and lifetime statistics from the CloudSat and CALIPSO missions.
Robin J. Hogan, Anthony J. Illingworth, Pavlos Kollias, Hajime Okamoto, and Ulla Wandinger
Atmos. Meas. Tech., 17, 3081–3083, https://doi.org/10.5194/amt-17-3081-2024, https://doi.org/10.5194/amt-17-3081-2024, 2024
Kristofer S. Tuftedal, Bernat Puigdomènech Treserras, Mariko Oue, and Pavlos Kollias
Atmos. Chem. Phys., 24, 5637–5657, https://doi.org/10.5194/acp-24-5637-2024, https://doi.org/10.5194/acp-24-5637-2024, 2024
Short summary
Short summary
This study analyzed coastal convective cells from June through September 2018–2021. The cells were classified and their lifecycles were analyzed to better understand their characteristics. Features such as convective-core growth, for example, are shown. The study found differences in the initiation location of shallow convection and in the aerosol loading in deep convective environments. This work provides a foundation for future analyses of convection or other tracked events elsewhere.
Zeen Zhu, Fan Yang, Pavlos Kollias, Raymond A. Shaw, Alex B. Kostinski, Steve Krueger, Katia Lamer, Nithin Allwayin, and Mariko Oue
Atmos. Meas. Tech., 17, 1133–1143, https://doi.org/10.5194/amt-17-1133-2024, https://doi.org/10.5194/amt-17-1133-2024, 2024
Short summary
Short summary
In this article, we demonstrate the feasibility of applying advanced radar technology to detect liquid droplets generated in the cloud chamber. Specifically, we show that using radar with centimeter-scale resolution, single drizzle drops with a diameter larger than 40 µm can be detected. This study demonstrates the applicability of remote sensing instruments in laboratory experiments and suggests new applications of ultrahigh-resolution radar for atmospheric sensing.
Shannon L. Mason, Howard W. Barker, Jason N. S. Cole, Nicole Docter, David P. Donovan, Robin J. Hogan, Anja Hünerbein, Pavlos Kollias, Bernat Puigdomènech Treserras, Zhipeng Qu, Ulla Wandinger, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 17, 875–898, https://doi.org/10.5194/amt-17-875-2024, https://doi.org/10.5194/amt-17-875-2024, 2024
Short summary
Short summary
When the EarthCARE mission enters its operational phase, many retrieval data products will be available, which will overlap both in terms of the measurements they use and the geophysical quantities they report. In this pre-launch study, we use simulated EarthCARE scenes to compare the coverage and performance of many data products from the European Space Agency production model, with the intention of better understanding the relation between products and providing a compact guide to users.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
Short summary
Short summary
Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
David P. Donovan, Pavlos Kollias, Almudena Velázquez Blázquez, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5327–5356, https://doi.org/10.5194/amt-16-5327-2023, https://doi.org/10.5194/amt-16-5327-2023, 2023
Short summary
Short summary
The Earth Cloud, Aerosol and Radiation Explorer mission (EarthCARE) is a multi-instrument cloud–aerosol–radiation-oriented satellite for climate and weather applications. For this satellite mission to be successful, the development and implementation of new techniques for turning the measured raw signals into useful data is required. This paper describes how atmospheric model data were used as the basis for creating realistic high-resolution simulated data sets to facilitate this process.
Imke Schirmacher, Pavlos Kollias, Katia Lamer, Mario Mech, Lukas Pfitzenmaier, Manfred Wendisch, and Susanne Crewell
Atmos. Meas. Tech., 16, 4081–4100, https://doi.org/10.5194/amt-16-4081-2023, https://doi.org/10.5194/amt-16-4081-2023, 2023
Short summary
Short summary
CloudSat’s relatively coarse spatial resolution, low sensitivity, and blind zone limit its assessment of Arctic low-level clouds, which affect the surface energy balance. We compare cloud fractions from CloudSat and finely resolved airborne radar observations to determine CloudSat’s limitations. Cloudsat overestimates cloud fractions above its blind zone, especially during cold-air outbreaks over open water, and misses a cloud fraction of 32 % and half of the precipitation inside its blind zone.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech., 16, 3727–3737, https://doi.org/10.5194/amt-16-3727-2023, https://doi.org/10.5194/amt-16-3727-2023, 2023
Short summary
Short summary
We show that large rain droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration for this inertial effect leads to an artificial broadening of the Doppler spectrum from the conventional simulator. Based on the physics-based simulation, we propose a new approach to generate the radar Doppler spectra. This simulator provides a valuable tool to decode cloud microphysical and dynamical properties from radar observation.
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.
Abdanour Irbah, Julien Delanoë, Gerd-Jan van Zadelhoff, David P. Donovan, Pavlos Kollias, Bernat Puigdomènech Treserras, Shannon Mason, Robin J. Hogan, and Aleksandra Tatarevic
Atmos. Meas. Tech., 16, 2795–2820, https://doi.org/10.5194/amt-16-2795-2023, https://doi.org/10.5194/amt-16-2795-2023, 2023
Short summary
Short summary
The Cloud Profiling Radar (CPR) and ATmospheric LIDar (ATLID) aboard the EarthCARE satellite are used to probe the Earth's atmosphere by measuring cloud and aerosol profiles. ATLID is sensitive to aerosols and small cloud particles and CPR to large ice particles, snowflakes and raindrops. It is the synergy of the measurements of these two instruments that allows a better classification of the atmospheric targets and the description of the associated products, which are the subject of this paper.
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
Short summary
Short summary
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.
Zackary Mages, Pavlos Kollias, Zeen Zhu, and Edward P. Luke
Atmos. Chem. Phys., 23, 3561–3574, https://doi.org/10.5194/acp-23-3561-2023, https://doi.org/10.5194/acp-23-3561-2023, 2023
Short summary
Short summary
Cold-air outbreaks (when cold air is advected over warm water and creates low-level convection) are a dominant cloud regime in the Arctic, and we capitalized on ground-based observations, which did not previously exist, from the COMBLE field campaign to study them. We characterized the extent and strength of the convection and turbulence and found evidence of secondary ice production. This information is useful for model intercomparison studies that will represent cold-air outbreak processes.
Mariko Oue, Stephen M. Saleeby, Peter J. Marinescu, Pavlos Kollias, and Susan C. van den Heever
Atmos. Meas. Tech., 15, 4931–4950, https://doi.org/10.5194/amt-15-4931-2022, https://doi.org/10.5194/amt-15-4931-2022, 2022
Short summary
Short summary
This study provides an optimization of radar observation strategies to better capture convective cell evolution in clean and polluted environments as well as a technique for the optimization. The suggested optimized radar observation strategy is to better capture updrafts at middle and upper altitudes and precipitation particle evolution of isolated deep convective clouds. This study sheds light on the challenge of designing remote sensing observation strategies in pre-field campaign periods.
Zeen Zhu, Pavlos Kollias, Edward Luke, and Fan Yang
Atmos. Chem. Phys., 22, 7405–7416, https://doi.org/10.5194/acp-22-7405-2022, https://doi.org/10.5194/acp-22-7405-2022, 2022
Short summary
Short summary
Drizzle (small rain droplets) is an important component of warm clouds; however, its existence is poorly understood. In this study, we capitalized on a machine-learning algorithm to develop a drizzle detection method. We applied this algorithm to investigate drizzle occurrence and found out that drizzle is far more ubiquitous than previously thought. This study demonstrates the ubiquitous nature of drizzle in clouds and will improve understanding of the associated microphysical process.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Sonja Drueke, Daniel J. Kirshbaum, and Pavlos Kollias
Atmos. Chem. Phys., 21, 14039–14058, https://doi.org/10.5194/acp-21-14039-2021, https://doi.org/10.5194/acp-21-14039-2021, 2021
Short summary
Short summary
This numerical study provides insights into the sensitivity of shallow-cumulus dilution to geostrophic vertical wind profile. The cumulus dilution is strongly sensitive to vertical wind shear in the cloud layer, with shallow cumuli being more diluted in sheared environments. On the other hand, wind shear in the subcloud layer leads to less diluted cumuli. The sensitivities are explained by jointly considering the impacts of vertical velocity and the properties of the entrained air.
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.
Marek Jacob, Pavlos Kollias, Felix Ament, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 5757–5777, https://doi.org/10.5194/gmd-13-5757-2020, https://doi.org/10.5194/gmd-13-5757-2020, 2020
Short summary
Short summary
We compare clouds in different cloud-resolving atmosphere simulations with airborne remote sensing observations. The focus is on warm shallow clouds in the Atlantic trade wind region. Those clouds are climatologically important but challenging for climate models. We use forward operators to apply instrument-specific thresholds for cloud detection to model outputs. In this comparison, the higher-resolution model better reproduces the layered cloud structure.
Cited articles
Andrić, J., Kumjian, M. R., Zrnić, D. S., Straka, J. M., and Melnikov, V. M.: Polarimetric signatures above the melting layer in winter storms: An observational and modeling study, Journal of Applied Meteorology and Climatology, 52, 682–700, 2013. 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, Journal of the Atmospheric Sciences, 66, 2888–2899, https://doi.org/10.1175/2009JAS2883.1, 2009. a
Baker, B. A. and Lawson, R. P.: In Situ Observations of the Microphysical Properties of Wave, Cirrus, and Anvil Clouds. Part I: Wave Clouds, Journal of the Atmospheric Sciences, 63, 3160–3185, https://doi.org/10.1175/jas3802.1, 2006. a
Bechini, R., Baldini, L., and Chandrasekar, V.: Polarimetric radar observations in the ice region of precipitating clouds at C-band and X-band radar frequencies, Journal of applied meteorology and climatology, 52, 1147–1169, 2013. a
Bharadwaj, N., Lindenmaier, I., Johnson, K., Nelson, D., Isom, B., Hardin, J., Matthews, A., Wendler, T., Melo de Castro, V., Deng, M., Rocque, M., and Feng, Y.: Ka ARM Zenith Radar (KAZRSPECCMASKMDCOPOL), 2011-11-11 to 2019-10-21, North Slope Alaska (NSA) Central Facility, Barrow AK (C1), ARM Data Center [data set], https://doi.org/10.5439/1976090, 2025. a, b
Borque, P., Luke, E., and Kollias, P.: On the unified estimation of turbulence eddy dissipation rate using Doppler cloud radars and lidars, Journal of Geophysical Research: Atmospheres, 121, 5972–5989, https://doi.org/10.1002/2015JD024543, 2016. a, b
Chellini, G. and Kneifel, S.: Turbulence as a Key Driver of Ice Aggregation and Riming in Arctic Low-Level Mixed-Phase Clouds, Revealed by Long-Term Cloud Radar Observations, Geophysical Research Letters, 51, e2023GL106599, https://doi.org/10.1029/2023GL106599, 2024. a, b
Chu, X., Xue, L., Geerts, B., and Kosović, B.: The impact of boundary layer turbulence on snow growth and precipitation: Idealized Large Eddy Simulations, Atmospheric Research, 204, 54–66, https://doi.org/10.1016/j.atmosres.2018.01.015, 2018. a
Fukuta, N. and Takahashi, T.: The Growth of Atmospheric Ice Crystals: A Summary of Findings in Vertical Supercooled Cloud Tunnel Studies, Journal of the Atmospheric Sciences, 56, 1963–1979, https://doi.org/10.1175/1520-0469(1999)056<1963:TGOAIC>2.0.CO;2, 1999. a, b
Garrett, T. J. and Yuter, S. E.: Observed influence of riming, temperature, and turbulence on the fallspeed of solid precipitation, Geophysical Research Letters, 41, 6515–6522, https://doi.org/10.1002/2014gl061016, 2014. a
Griggs, D. J. and Choularton, T. W.: A laboratory study of secondary ice particle production by the fragmentation of rime and vapour-grown ice crystals, Quarterly Journal of the Royal Meteorological Society, 112, 149–163, 1986. a
Heymsfield, A. J., Schmitt, C., Chen, C.-C.-J., Bansemer, A., Gettelman, A., Field, P. R., and Liu, C.: Contributions of the liquid and ice phases to global surface precipitation: Observations and global climate modeling, Journal of the Atmospheric Sciences, 77, 2629–2648, 2020. a
Hobbs, P. V. and Rangno, A. L.: Microstructures of Low and Middle‐level Clouds over the Beaufort Sea, Quarterly Journal of the Royal Meteorological Society, 124, 2035–2071, https://doi.org/10.1002/qj.49712455012, 1998. a
Houze, R. A. and Medina, S.: Turbulence as a Mechanism for Orographic Precipitation Enhancement, Journal of the Atmospheric Sciences, 62, 3599–3623, https://doi.org/10.1175/JAS3555.1, 2005. a
Kalesse, H., Kollias, P., and Szyrmer, W.: On using the relationship between Doppler velocity and radar reflectivity to identify microphysical processes in midlatitudinal ice clouds, Journal of Geophysical Research: Atmospheres, 118, 12168–12179, https://doi.org/10.1002/2013JD020386, 2013. 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, Meteorological Monographs, 58, 1.1–1.33, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0006.1, 2017. a
Keeler, E., Burk, K., and Kyrouac, J.: Balloon-Borne Sounding System (SONDEWNPN), 2002-04-28 to 2025-05-06, North Slope Alaska (NSA), Central Facility, Barrow AK (C1), ARM Data Center, [data set], https://doi.org/10.5439/1595321, 2025. a, b
Khain, A. P., Beheng, K. D., Heymsfield, A., Korolev, A., Krichak, S. O., Levin, Z., Pinsky, M., Phillips, V., Prabhakaran, T., Teller, A., Van Den Heever, S. C., and Yano, J. I.: Representation of microphysical processes in cloud‐resolving models: Spectral (bin) microphysics versus bulk parameterization, Reviews of Geophysics, 53, 247–322, https://doi.org/10.1002/2014rg000468, 2015. a, b
Kikuchi, K., Kameda, T., Higuchi, K., and Yamashita, A.: A global classification of snow crystals, ice crystals, and solid precipitation based on observations from middle latitudes to polar regions, Atmos. Res., 132–133, 460–472, 2013. a
Kollias, P., Bharadwaj, N., Clothiaux, E. E., Lamer, K., Oue, M., Hardin, J., Isom, B., Lindenmaier, I., Matthews, A., Luke, E. P., Giangrande, S. E., Johnson, K., Collis, S., Comstock, J., and Mather, J. H.: The ARM Radar Network: At the Leading Edge of Cloud and Precipitation Observations, Bulletin of the American Meteorological Society, 101, E588–E607, https://doi.org/10.1175/BAMS-D-18-0288.1, 2020. a, b
Korolev, A.: Limitations of the Wegener–Bergeron–Findeisen mechanism in the evolution of mixed-phase clouds, Journal of the Atmospheric Sciences, 64, 3372–3375, 2007. a
Korolev, A. and Field, P. R.: The effect of dynamics on mixed-phase clouds: Theoretical considerations, Journal of the Atmospheric Sciences, 65, 66–86, 2008. a
Korolev, A. and Leisner, T.: Review of experimental studies of secondary ice production, Atmos. Chem. Phys., 20, 11767–11797, https://doi.org/10.5194/acp-20-11767-2020, 2020. a, b
Korolev, A., Heckman, I., Wolde, M., Ackerman, A. S., Fridlind, A. M., Ladino, L. A., Lawson, R. P., Milbrandt, J., and Williams, E.: A new look at the environmental conditions favorable to secondary ice production, Atmos. Chem. Phys., 20, 1391–1429, https://doi.org/10.5194/acp-20-1391-2020, 2020. a
Korolev, A. V., Isaac, G. A., and Hallett, J.: Ice particle habits in Arctic clouds, Geophysical Research Letters, 26, 1299–1302, https://doi.org/10.1029/1999GL900232, 1999. a
Kumjian, M. R., Tobin, D. M., Oue, M., and Kollias, P.: Microphysical Insights into Ice Pellet Formation Revealed by Fully Polarimetric Ka-Band Doppler Radar, Journal of Applied Meteorology and Climatology, 59, 1557–1580, https://doi.org/10.1175/JAMC-D-20-0054.1, 2020. a
Kumjian, M. R., Prat, O. P., Reimel, K. J., Van Lier-Walqui, M., and Morrison, H. C.: Dual-Polarization Radar Fingerprints of Precipitation Physics: A Review, Remote Sensing, 14, 3706, https://doi.org/10.3390/rs14153706, 2022. a
Lamb, D. and Verlinde, J.: Physics and Chemistry of Clouds, Cambridge University Press, ISBN 978-0-511-97637-7, ISBN 978-1-139-19063-3, ISBN 978-1-62870-283-5, 2011. a
Leinonen, J., Moisseev, D., and Nousiainen, T.: Linking Snowflake Microstructure to Multi‐frequency Radar Observations, Journal of Geophysical Research: Atmospheres, 118, 3259–3270, https://doi.org/10.1002/jgrd.50163, 2013. a
Liou, K.-N.: Influence of cirrus clouds on weather and climate processes: A global perspective, Monthly Weather Review, 114, 1167–1199, 1986. a
Locatelli, J. D. and Hobbs, P. V.: Fall speeds and masses of solid precipitation particles, Journal of Geophysical Research, 79, 2185–2197, https://doi.org/10.1029/jc079i015p02185, 1974. a
Lonardi, M., Akansu, E. F., Ehrlich, A., Mazzola, M., Pilz, C., Shupe, M. D., Siebert, H., and Wendisch, M.: Tethered balloon-borne observations of thermal-infrared irradiance and cooling rate profiles in the Arctic atmospheric boundary layer, Atmos. Chem. Phys., 24, 1961–1978, https://doi.org/10.5194/acp-24-1961-2024, 2024. a
Luke, E. P., Yang, F., Kollias, P., Vogelmann, A. M., and Maahn, M.: New insights into ice multiplication using remote-sensing observations of slightly supercooled mixed-phase clouds in the Arctic, Proceedings of the National Academy of Sciences, 118, e2021387118, https://doi.org/10.1073/pnas.2021387118, 2021. a, b, c, d, e
Lynch, D. K., Sassen, K., Starr, D. O., and Stephens, G.: Cirrus, Oxford University Press, ISBN 978-0195351392, 2002. a
Morrison, H., De Boer, G., Feingold, G., Harrington, J., Shupe, M. D., and Sulia, K.: Resilience of persistent Arctic mixed-phase clouds, Nature Geoscience, 5, 11–17, https://doi.org/10.1038/ngeo1332, 2012. a, b
Mülmenstädt, J., Sourdeval, O., Delanoë, J., and Quaas, J.: Frequency of occurrence of rain from liquid-, mixed-, and ice-phase clouds derived from A-Train satellite retrievals, Geophysical Research Letters, 42, 6502–6509, 2015. a
Oue, M., Kollias, P., Ryzhkov, A., and Luke, E. P.: Toward Exploring the Synergy Between Cloud Radar Polarimetry and Doppler Spectral Analysis in Deep Cold Precipitating Systems in the Arctic, Journal of Geophysical Research: Atmospheres, 123, 2797–2815, https://doi.org/10.1002/2017jd027717, 2018. a, b
Oue, M., Kollias, P., Matrosov, S. Y., Battaglia, A., and Ryzhkov, A. V.: Analysis of the microphysical properties of snowfall using scanning polarimetric and vertically pointing multi-frequency Doppler radars, Atmos. Meas. Tech., 14, 4893–4913, https://doi.org/10.5194/amt-14-4893-2021, 2021. a
Ramelli, F., Henneberger, J., David, R. O., Lauber, A., Pasquier, J. T., Wieder, J., Bühl, J., Seifert, P., Engelmann, R., Hervo, M., and Lohmann, U.: Influence of low-level blocking and turbulence on the microphysics of a mixed-phase cloud in an inner-Alpine valley, Atmos. Chem. Phys., 21, 5151–5172, https://doi.org/10.5194/acp-21-5151-2021, 2021. a
Schrom, R. S. and Kumjian, M. R.: Connecting Microphysical Processes in Colorado Winter Storms with Vertical Profiles of Radar Observations, Journal of Applied Meteorology and Climatology, 55, 1771–1787, https://doi.org/10.1175/JAMC-D-15-0338.1, 2016. a, b, c, d
Schwarzenboeck, A., Shcherbakov, V., Lefevre, R., Gayet, J.-F., Pointin, Y., and Duroure, C.: Indications for Stellar-Crystal Fragmentation in Arctic Clouds, Atmospheric Research, 92, 220–228, https://doi.org/10.1016/j.atmosres.2008.10.002, 2009. a
Silber, I., McGlynn, P. S., Harrington, J. Y., and Verlinde, J.: Habit‐Dependent Vapor Growth Modulates Arctic Supercooled Water Occurrence, Geophysical Research Letters, 48, https://doi.org/10.1029/2021gl092767, 2021. a
Stephens, G. L., Tsay, S.-C., Stackhouse Jr, P. W., and Flatau, P. J.: The relevance of the microphysical and radiative properties of cirrus clouds to climate and climatic feedback, Journal of Atmospheric Sciences, 47, 1742–1754, 1990. a
Takahashi, T.: Influence of Liquid Water Content and Temperature on the Form and Growth of Branched Planar Snow Crystals in a Cloud, Journal of the Atmospheric Sciences, 71, 4127–4142, https://doi.org/10.1175/JAS-D-14-0043.1, 2014. a, b
Takahashi, T. and Fukuta, N.: Supercooled cloud tunnel studies on the growth of snow crystals between-4 and-20 C, Journal of the Meteorological Society of Japan. Ser. II, 66, 841–855, 1988. a
Takahashi, T., Endoh, T., Wakahama, G., and Fukuta, N.: Vapor Diffusional Growth of Free-Falling Snow Crystals between -3 and -23 degC, Journal of the Meteorological Society of Japan. Ser. II, 69, 15–30, https://doi.org/10.2151/jmsj1965.69.1_15, 1991. a, b, c
Takahashi, T., Nagao, Y., and Kushiyama, Y.: Possible High Ice Particle Production during Graupel–Graupel Collisions, Journal of the Atmospheric Sciences, 52, 4523–4527, https://doi.org/10.1175/1520-0469(1995)052<4523:PHIPPD>2.0.CO;2, 1995. a
Tyynelä, J. and Chandrasekar, V.: Characterizing Falling Snow Using Multifrequency Dual-Polarization Measurements: TYYNELÄ AND CHANDRASEKAR, Journal of Geophysical Research: Atmospheres, 119, 8268–8283, https://doi.org/10.1002/2013JD021369, 2014. a
Tyynelä, J., McCusker, K., Ori, D., Ekelund, R., and Fenni, I.: Scattering by Snow Particles, in: Advances in Weather Radar. Volume 2: Precipitation Science, Scattering and Processing Algorithms, edited by: Bringi, V., Mishra, K. V., and Thurai, M., Institution of Engineering and Technology, 99–142, ISBN 978-1-83953-624-3 978-1-83953-625-0, https://doi.org/10.1049/SBRA557G_ch3, 2023. a, b, c
Vardiman, L.: The generation of secondary ice particles in clouds by crystal–crystal collision, Journal of Atmospheric Sciences, 35, 2168–2180, 1978. a
Verlinde, J., Zak, B. D., Shupe, M. D., Ivey, M. D., and Stamnes, K.: The ARM North Slope of Alaska (NSA) Sites, Meteorological Monographs, 57, 8.1–8.13, https://doi.org/10.1175/amsmonographs-d-15-0023.1, 2016. a
von Terzi, L., Dias Neto, J., Ori, D., Myagkov, A., and Kneifel, S.: Ice microphysical processes in the dendritic growth layer: a statistical analysis combining multi-frequency and polarimetric Doppler cloud radar observations, Atmos. Chem. Phys., 22, 11795–11821, https://doi.org/10.5194/acp-22-11795-2022, 2022. a, b, c
Zhang, D., Ermold, B., and Morris, V.: Ceilometer (CEIL), 1997-12-11 to 2025-04-12, North Slope Alaska (NSA) [data set], Central Facility, Barrow AK (C1), https://doi.org/10.5439/1181954, 2025. a, b, c
Short summary
In this study, we analyzed over six years of ground-based radar and weather balloon data collected in northern Alaska. We found that ice particle changes depend strongly on temperature, humidity conditions, and turbulence. We also found that turbulence and the presence of supercooled liquid water often occur together, and when they do, ice particle growth is especially strong. These findings help scientists to improve weather models.
In this study, we analyzed over six years of ground-based radar and weather balloon data...
Altmetrics
Final-revised paper
Preprint