Articles | Volume 18, issue 22
https://doi.org/10.5194/amt-18-6747-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-18-6747-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
First insights into deep convection by the Doppler velocity measurements of the EarthCARE Cloud Profiling Radar
Aida Galfione
CORRESPONDING AUTHOR
Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, 10129 Turin, Italy
Alessandro Battaglia
Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, 10129 Turin, Italy
Earth Observation Science Group, Department of Physics and Astronomy, University of Leicester, Leicester LE1 7RH, UK
Bernat Puigdomènech Treserras
Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, H3A 0B9, QC, Canada
Pavlos Kollias
Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, H3A 0B9, QC, Canada
School of Marine and Atmospheric Science, Stony Brook University, NY 11790, NY, USA
Related authors
No articles found.
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.
Jiseob Kim, Pavlos Kollias, Bernat Puigdomènech Treserras, Alessandro Battaglia, and Ivy Tan
Atmos. Chem. Phys., 25, 15389–15402, https://doi.org/10.5194/acp-25-15389-2025, https://doi.org/10.5194/acp-25-15389-2025, 2025
Short summary
Short summary
The EarthCARE satellite’s Cloud Profiling Radar (CPR) can now measure how fast particles fall within clouds from space. In this study, we compared these new satellite measurements with ground-based radar data and found that, after proper corrections, the CPR gives reliable results, especially in ice clouds. This means scientists can confidently use EarthCARE data to better understand clouds and improve weather and climate predictions.
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.
Filippo Emilio Scarsi, Alessandro Battaglia, Maximilian Maahn, and Stef Lhermitte
The Cryosphere, 19, 4875–4892, https://doi.org/10.5194/tc-19-4875-2025, https://doi.org/10.5194/tc-19-4875-2025, 2025
Short summary
Short summary
Snowfall measurements at high latitudes are crucial for estimating ice sheet mass balance. Spaceborne radar and radiometer missions help estimate snowfall but face uncertainties. This work evaluates uncertainties in snowfall estimates from a fixed near-nadir radar (CloudSat-like) and a conically scanning radar (WIVERN-like), showing that a WIVERN-like radar will provide better estimates than a CloudSat-like radar at smaller spatial and temporal scales.
Bernat Puigdomènech Treserras, Pavlos Kollias, Alessandro Battaglia, Simone Tanelli, and Hirotaka Nakatsuka
Atmos. Meas. Tech., 18, 5607–5618, https://doi.org/10.5194/amt-18-5607-2025, https://doi.org/10.5194/amt-18-5607-2025, 2025
Short summary
Short summary
We investigate how seasonal solar illumination affects the pointing accuracy of EarthCARE’s cloud profile radar (CPR) antenna and introduce a correction based on surface Doppler measurements. The correction improves measurement accuracy by reducing Doppler velocity biases to within 5 and 7 cm s−1. Our results demonstrate the importance of continuous pointing characterization to maintain the scientific accuracy of EarthCARE’s CPR Doppler observations.
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.
Ioanna Tsikoudi, Alessandro Battaglia, Christine Unal, and Eleni Marinou
Atmos. Meas. Tech., 18, 4857–4870, https://doi.org/10.5194/amt-18-4857-2025, https://doi.org/10.5194/amt-18-4857-2025, 2025
Short summary
Short summary
In the study, we simulate spectral polarimetric variables for raindrops as observed by cloud radar. Raindrops are modeled as oblate spheroids, and backscattering properties are computed via the T-matrix method, including noise, turbulence, and spectral averaging effects. When comparing simulations with measurements, differences in the amplitudes of polarimetric variables are noted. This shows the challenge of using simplified shapes to model raindrop polarimetric variables when moving to millimeter wavelengths.
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
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.
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.
Nina Maherndl, Alessandro Battaglia, Anton Kötsche, and Maximilian Maahn
Atmos. Meas. Tech., 18, 3287–3304, https://doi.org/10.5194/amt-18-3287-2025, https://doi.org/10.5194/amt-18-3287-2025, 2025
Short summary
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.
Stefano Federico, Rosa Claudia Torcasio, Claudio Transerici, Mario Montopoli, Cinzia Cambiotti, Francesco Manconi, Alessandro Battaglia, and Maryam Pourshamsi
EGUsphere, https://doi.org/10.5194/egusphere-2025-2095, https://doi.org/10.5194/egusphere-2025-2095, 2025
Short summary
Short summary
The Wind Velocity Radar Nephoscope (WIVERN) mission will be the first space-based mission to provide global in-cloud wind, cloud and precipitation measurements. The mission is proposed as a candidate for the ESA Earth Explorer 11. Its data could be beneficial to several sectors, including numerical weather prediction performance enhancement. This paper aims to contribute to the last point by analyzing the impact that WIVERN would have in the case of a Tropical-like cyclone event.
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.
Jialin Yan, Mariko Oue, Pavlos Kollias, Edward Luke, and Fan Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2149, https://doi.org/10.5194/egusphere-2025-2149, 2025
Short summary
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.
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.
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.
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.
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.
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.
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
Short summary
Short summary
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.
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.
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.
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.
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.
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.
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
Short summary
Short summary
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.
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
Amayenc, P., Testud, J., and Marzoug, M.: Proposal for a Spaceborne Dual-Beam Rain Radar with Doppler Capability, J. Atmos. Ocean Technol., 10, 262–276, https://doi.org/10.1175/1520-0426(1993)010<0262:PFASDB>2.0.CO;2, 1993. a
Arakawa, A.: The Cumulus Parameterization Problem: Past, Present, and Future, J. Climate, 17, 2493–2525, https://doi.org/10.1175/1520-0442(2004)017<2493:RATCPP>2.0.CO;2, 2004. a
Battaglia, A. and Kollias, P.: Using ice clouds for mitigating the EarthCARE Doppler radar mispointing, IEEE Trans. Geosci. Remote Sens., 53, 2079–2085, https://doi.org/10.1109/TGRS.2014.2353219, 2014. a
Battaglia, A. and Tanelli, S.: DOMUS: Doppler MUltiple-Scattering Simulator, IEEE Trans. Geosci. Remote Sens., 49, 442–450, https://doi.org/10.1109/TGRS.2010.2052818, 2011. a, b
Battaglia, A., Haynes, J. M., L'Ecuyer, T., and Simmer, C.: Identifying multiple-scattering-affected profiles in CloudSat observations over the oceans, J. Geophys. Res. Atmos., 113, https://doi.org/10.1029/2008JD009960, 2008. a
Battaglia, A., Tanelli, S., Kobayashi, S., Zrnic, D., Hogan, R. J., and Simmer, C.: Multiple-scattering in radar systems: A review, J. Quant. Spectrosc. Radiat. Transfer, 111, 917–947, https://doi.org/10.1016/j.jqsrt.2009.11.024, 2010. a, b
Battaglia, A., Augustynek, T., Tanelli, S., and Kollias, P.: Multiple scattering identification in spaceborne W-band radar measurements of deep convective cores, J. Geophys. Res. Atmos., 116, https://doi.org/10.1029/2011JD016142, 2011. a
Battaglia, A., Tanelli, S., and Kollias, P.: Polarization Diversity for Millimeter Spaceborne Doppler Radars: An Answer for Observing Deep Convection?, J. Atmos. Ocean Technol., 30, 2768–2787, https://doi.org/10.1175/JTECH-D-13-00085.1, 2013. 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.: Spaceborne Cloud and Precipitation Radars: Status, Challenges, and Ways Forward, Reviews of Geophysics, 58, e2019RG000686, https://doi.org/10.1029/2019RG000686, 2020. a
Battaglia, A., Rizik, A., Sikaneta, I., and Tridon, F.: I and Qs Simulation and Processing Envisaged for Spaceborne Polarization Diversity Doppler Radars, IEEE Trans. Geosci. Remote Sens., 63, 1–14, https://doi.org/10.1109/TGRS.2025.3529672, 2025. a
Bechtold, P., Bazile, E., Guichard, F., Mascart, P., and Richard, E.: A mass-flux convection scheme for regional and global models, Quarterly Journal of the Royal Meteorological Society, 127, 869–886, https://doi.org/10.1002/qj.49712757309, 2001. a
Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., Watanabe, M., and Webb, M.: Clouds, circulation and climate sensitivity, Nature Geoscience, 8, 261–268, https://doi.org/10.1038/ngeo2398, 2015. a
Eilts, M. D. and Smith, S. D.: Efficient Dealiasing of Doppler Velocities Using Local Environment Constraints, J. Atmos. Ocean Technol., 7, 118–128, https://doi.org/10.1175/1520-0426(1990)007<0118:EDODVU>2.0.CO;2, 1990. a
Eisinger, M., Marnas, F., Wallace, K., Kubota, T., Tomiyama, N., Ohno, Y., Tanaka, T., Tomita, E., Wehr, T., and Bernaerts, D.: The EarthCARE mission: science data processing chain overview, Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, 2024. 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
Feldmann, M., Curtis, N. J., Boscacci, M., Leuenberger, D., Gabella, M., Germann, U., Wolfensberger, D., and Berne, A.: R2D2: A Region-Based Recursive Doppler Dealiasing Algorithm for Operational Weather Radar, J. Atmos. Oceanic Technol., 37, 2341–2356, https://doi.org/10.1175/JTECH-D-20-0054.1, 2020. a
Fiolleau, T. and Roca, R.: A database of deep convective systems derived from the intercalibrated meteorological geostationary satellite fleet and the TOOCAN algorithm (2012–2020), Earth Syst. Sci. Data, 16, 4021–4050, https://doi.org/10.5194/essd-16-4021-2024, 2024. a
Fridlind, A. M., Li, X., Wu, D., van Lier-Walqui, M., Ackerman, A. S., Tao, W.-K., McFarquhar, G. M., Wu, W., Dong, X., Wang, J., Ryzhkov, A., Zhang, P., Poellot, M. R., Neumann, A., and Tomlinson, J. M.: Derivation of aerosol profiles for MC3E convection studies and use in simulations of the 20 May squall line case, Atmos. Chem. Phys., 17, 5947–5972, https://doi.org/10.5194/acp-17-5947-2017, 2017. a
Gasparini, B., Rasch, P. J., Hartmann, D. L., Wall, C. J., and Dütsch, M.: A Lagrangian Perspective on Tropical Anvil Cloud Lifecycle in Present and Future Climate, J. Geophys. Res. Atmos., 126, e2020JD033487, https://doi.org/10.1029/2020JD033487, 2021. a
Giangrande, S. C., Collis, S., Straka, J., Protat, A., Williams, C., and Krueger, S.: A Summary of Convective-Core Vertical Velocity Properties Using ARM UHF Wind Profilers in Oklahoma, Journal of Applied Meteorology and Climatology, 52, 2278–2295, https://doi.org/10.1175/JAMC-D-12-0185.1, 2013. a
Hagihara, Y., Ohno, Y., Horie, H., Roh, W., Satoh, M., Kubota, T., and Oki, R.: Assessments of Doppler Velocity Errors of EarthCARE Cloud Profiling Radar Using Global Cloud System Resolving Simulations: Effects of Doppler Broadening and Folding, IEEE Trans. Geosci. Remote Sens., 60, 1–9, https://doi.org/10.1109/TGRS.2021.3060828, 2022. a
Hamada, A., Takayabu, Y. N., Liu, C., and Zipser, E. J.: Weak linkage between the heaviest rainfall and tallest storms, Nature Communications, 6, 6213, https://doi.org/10.1038/ncomms7213, 2015. a
Hartmann, D. L., Hendon, H. H., and Houze, R. A.: Some Implications of the Mesoscale Circulations in Tropical Cloud Clusters for Large-Scale Dynamics and Climate, J. Atmos. Sci., 41, 113–121, https://doi.org/10.1175/1520-0469(1984)041<0113:SIOTMC>2.0.CO;2, 1984. a
Hartmann, D. L., Gasparini, B., Berry, S. E., and Blossey, P. N.: The Life Cycle and Net Radiative Effect of Tropical Anvil Clouds, Journal of Advances in Modeling Earth Systems, 10, 3012–3029, https://doi.org/10.1029/2018MS001484, 2018. a
Heikenfeld, M., Marinescu, P. J., Christensen, M., Watson-Parris, D., Senf, F., van den Heever, S. C., and Stier, P.: tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets, Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, 2019. a
Heymsfield, G. M., Tian, L., Heymsfield, A. J., Li, L., and Guimond, S.: Characteristics of Deep Tropical and Subtropical Convection from Nadir-Viewing High-Altitude Airborne Doppler Radar, J. Atmos. Sci., 67, 285–308, https://doi.org/10.1175/2009JAS3132.1, 2010. a
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
Jeyaratnam, J., Luo, Z. J., Giangrande, S. E., Wang, D., and Masunaga, H.: A Satellite-Based Estimate of Convective Vertical Velocity and Convective Mass Flux: Global Survey and Comparison With Radar Wind Profiler Observations, Geophys. Res. Lett., 48, e2020GL090675, https://doi.org/10.1029/2020GL090675, 2021. a
Khlopenkov, K., Bedka, K., Cooney, J., and Itterly, K.: Recent Advances in Detection of Overshooting Cloud Tops From Longwave Infrared Satellite Imagery, J. Geophys. Res. Atmos., 126, https://doi.org/10.1029/2020JD034359, 2021. a
Kobayashi, S., Kumagai, H., and Kuroiwa, H.: A proposal of pulse-pair Doppler operation on a spaceborn cloud-profiling radar in the W band, J. Atmos. Ocean Technol., 19, https://doi.org/10.1175/1520-0426(2002)019<1294:APOPPD>2.0.CO;2, 2002. a
Kollias, P., Szyrmer, W., Zawadzki, I., and Joe, P.: Considerations for spaceborne 94 GHz radar observations of precipitation, Geophys. Res. Lett., 34, https://doi.org/10.1029/2007GL031536, 2007. a
Kollias, P., Battaglia, A., A., T., Lamer, K., Tridon, F., and Pfitzenmaier, L.: The EarthCARE cloud profiling radar (CPR) Doppler measurements in deep convection: challenges, post-processing, and science applications, in: Remote Sensing of the Atmosphere, Clouds, and Precipitation VII, edited by: Im, E. and Yang, S., vol. 10776, International Society for Optics and Photonics, SPIE, 57–68, https://doi.org/10.1117/12.2324321, 2018. a, b
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, b, c, d, e, f, g, h, i
Kummerow, C., Barnes, W., Kozu, T., Shiue, J., and Simpson, J.: The Tropical Rainfall Measuring Mission (TRMM) Sensor Package, J. Atmos. Ocean Technol., 15, 809–817, https://doi.org/10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2, 1998. a
Kummerow, C., Simpson, J., Thiele, O., Barnes, W., Chang, A. T. C., Stocker, E., Adler, R. F., Hou, A., Kakar, R., Wentz, F., Ashcroft, P., Kozu, T., Hong, Y., Okamoto, K., Iguchi, T., Kuroiwa, H., Im, E., Haddad, Z., Huffman, G., Ferrier, B., Olson, W. S., Zipser, E., Smith, E. A., Wilheit, T. T., North, G., Krishnamurti, T., and Nakamura, K.: The Status of the Tropical Rainfall Measuring Mission (TRMM) after Two Years in Orbit, J. Appl. Meteorol., 39, 1965–1982, https://doi.org/10.1175/1520-0450(2001)040<1965:TSOTTR>2.0.CO;2, 2000. a
Ladino, L. A., Korolev, A., Heckman, I., Wolde, M., Fridlind, A. M., and Ackerman, A. S.: On the role of ice-nucleating aerosol in the formation of ice particles in tropical mesoscale convective systems, Geophys. Res. Lett., 44, 1574–1582, https://doi.org/10.1002/2016GL072455, 2017. a
Lee, Y., Kummerow, C. D., and Zupanski, M.: A simplified method for the detection of convection using high-resolution imagery from GOES-16, Atmos. Meas. Tech., 14, 3755–3771, https://doi.org/10.5194/amt-14-3755-2021, 2021. a
Liu, N. and Liu, C.: Global distribution of deep convection reaching tropopause in 1 year GPM observations, J. Geophys. Res. Atmos., 121, 3824–3842, https://doi.org/10.1002/2015JD024430, 2016. a
Liu, N., Liu, C., and Hayden, L.: Climatology and Detection of Overshooting Convection From 4Years of GPM Precipitation Radar and Passive Microwave Observations, J. Geophys. Res. Atmos., 125, e2019JD032003, https://doi.org/10.1029/2019JD032003, 2020. a
Louf, V., Protat, A., Jackson, R. C., Collis, S. M., and Helmus, J.: UNRAVEL: A Robust Modular Velocity Dealiasing Technique for Doppler Radar, Journal of Atmospheric and Oceanic Technology, 37, 741–758, https://doi.org/10.1175/JTECH-D-19-0020.1, 2020. a
Luo, Z. J., Liu, G. Y., and Stephens, G. L.: CloudSat adding new insight into tropical penetrating convection, Geophys. Res. Lett., 35, https://doi.org/10.1029/2008GL035330, 2008. a, b
Luo, Z. J., Liu, G. Y., and Stephens, G. L.: Use of A-Train data to estimate convective buoyancy and entrainment rate, Geophys. Res. Lett., 37, https://doi.org/10.1029/2010GL042904, 2010. a
Luo, Z. J., Jeyaratnam, J., Iwasaki, S., Takahashi, H., and Anderson, R.: Convective vertical velocity and cloud internal vertical structure: An A-Train perspective, Geophys. Res. Lett., 41, 723–729, https://doi.org/10.1002/2013GL058922, 2014. a, b, c
Maahn, M. and Kollias, P.: Improved Micro Rain Radar snow measurements using Doppler spectra post-processing, Atmos. Meas. Tech., 5, 2661–2673, https://doi.org/10.5194/amt-5-2661-2012, 2012. a
Manabe, S. and Strickler, R. F.: Thermal Equilibrium of the Atmosphere with a Convective Adjustment, J. Atmos. Sci., 21, 361–385, https://doi.org/10.1175/1520-0469(1964)021<0361:TEOTAW>2.0.CO;2, 1964. a
Meneghini, R., Bidwell, S., Liao, L., Rincon, R., and Heymsfield, G.: Differential-frequency Doppler weather radar: Theory and experiment, Radio Science, 38, https://doi.org/10.1029/2002RS002656, 2003. a
Mroz, K., Treserras, B. P., Battaglia, A., Kollias, P., Tatarevic, A., and Tridon, F.: Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product, Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, 2023. a
Ni, X., Liu, C., and Zipser, E.: Ice Microphysical Properties near the Tops of Deep Convective Cores Implied by the GPM Dual-Frequency Radar Observations, J. Atmos. Sci., 76, 2899–2917, https://doi.org/10.1175/JAS-D-18-0243.1, 2019. a
North, K. W., Oue, M., Kollias, P., Giangrande, S. E., Collis, S. M., and Potvin, C. K.: Vertical air motion retrievals in deep convective clouds using the ARM scanning radar network in Oklahoma during MC3E, Atmos. Meas. Tech., 10, 2785–2806, https://doi.org/10.5194/amt-10-2785-2017, 2017. a
Oue, M., Kollias, P., Shapiro, A., Tatarevic, A., and Matsui, T.: Investigation of observational error sources in multi-Doppler-radar three-dimensional variational vertical air motion retrievals, Atmos. Meas. Tech., 12, 1999–2018, https://doi.org/10.5194/amt-12-1999-2019, 2019. a
Peral, E., Statham, S., Im, E., Tanelli, S., Imken, T., Price, D., Sauder, J., Chahat, N., and Williams, A.: The Radar-in-a-Cubesat (RAINCUBE) and Measurement Results, in: IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, 6297–6300, https://doi.org/10.1109/IGARSS.2018.8519194, 2018. a
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K., Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S., Schmidli, J., van Lipzig, N. P. M., and Leung, R.: A review on regional convection-permitting climate modeling: Demonstrations, prospects, and challenges, Reviews of Geophysics, 53, 323–361, https://doi.org/10.1002/2014RG000475, 2015. a
Puigdomènech Treserras, B., Kollias, P., Battaglia, A., Tanelli, S., and Nakatsuka, H.: EarthCARE's cloud profiling radar antenna pointing correction using surface Doppler measurements, Atmos. Meas. Tech., 18, 5607–5618, https://doi.org/10.5194/amt-18-5607-2025, 2025. a, b
Schutgens, N. A. J.: Simulated Doppler Radar Observations of Inhomogeneous Clouds: Application to the EarthCARE Space Mission, J. Atmos. Ocean Technol., 25, 1514–1528, https://doi.org/10.1175/2007JTECHA1026.1, 2008a. a
Schutgens, N. A. J.: Simulating Range Oversampled Doppler Radar Profiles of Inhomogeneous Targets, J. Atmos. Ocean Technol., 25, 26–42, https://doi.org/10.1175/2007JTECHA956.1, 2008b. a
Skofronick-Jackson, G., Petersen, W. A., Berg, W., Kidd, C., Stocker, E. F., Kirschbaum, D. B., Kakar, R., Braun, S. A., Huffman, G. J., Iguchi, T., Kirstetter, P. E., Kummerow, C., Meneghini, R., Oki, R., Olson, W. S., Takayabu, Y. N., Furukawa, K., and Wilheit, T.: The Global Precipitation Measurement (GPM) Mission for Science and Society, B. Am. Meteorol. Soc., 98, 1679–1695, https://doi.org/10.1175/BAMS-D-15-00306.1, 2017. a
Skofronick-Jackson, G., Kirschbaum, D., Petersen, W., Huffman, G., Kidd, C., Stocker, E., and Kakar, R.: The Global Precipitation Measurement (GPM) mission's scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations, Q. J. Roy. Meteor. Soc., 144, 27–48, https://doi.org/10.1002/qj.3313, 2018. a
Sokolowsky, G. A., Freeman, S. W., Jones, W. K., Kukulies, J., Senf, F., Marinescu, P. J., Heikenfeld, M., Brunner, K. N., Bruning, E. C., Collis, S. M., Jackson, R. C., Leung, G. R., Pfeifer, N., Raut, B. A., Saleeby, S. M., Stier, P., and van den Heever, S. C.: tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena, Geosci. Model Dev., 17, 5309–5330, https://doi.org/10.5194/gmd-17-5309-2024, 2024. a
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and Team, C. S.: The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation, B. Am. Meteorol. Soc., 83, 1771–1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002. a
Stephens, G. L., Shiro, K. A., Hakuba, M. Z., Takahashi, H., Pilewskie, J. A., Andrews, T., Stubenrauch, C. J., and Wu, L.: Tropical Deep Convection, Cloud Feedbacks and Climate Sensitivity, Surveys in Geophysics, 45, 1903–1931, https://doi.org/10.1007/s10712-024-09831-1, 2024. a
Sy, O. O., Tanelli, S., Takahashi, N., Ohno, Y., Horie, H., and Kollias, P.: Simulation of EarthCARE spaceborne Doppler radar products using ground-based and airborne data: Effects of aliasing and nonuniform beam-filling, IEEE Trans. Geosci. Remote Sens., 52, https://doi.org/10.1109/TGRS.2013.2251639, 2014. a, b
Takahashi, H. and Luo, Z.: Where is the level of neutral buoyancy for deep convection?, Geophys. Res. Lett., 39, https://doi.org/10.1029/2012GL052638, 2012. a
Takahashi, H. and Luo, Z. J.: Characterizing tropical overshooting deep convection from joint analysis of CloudSat and geostationary satellite observations, J. Geophys. Res. Atmos., 119, 112–121, https://doi.org/10.1002/2013JD020972, 2014. a, b
Takahashi, H., Luo, Z. J., and Stephens, G. L.: Level of neutral buoyancy, deep convective outflow, and convective core: New perspectives based on 5 years of CloudSat data, J. Geophys. Res. Atmos., 122, 2958–2969, https://doi.org/10.1002/2016JD025969, 2017. a, b
Tanelli, S., Im, E., Durden, S. L., Facheris, L., and Giuli, D.: The Effects of Nonuniform Beam Filling on Vertical Rainfall Velocity Measurements with a Spaceborne Doppler Radar, J. Atmos. Ocean Technol., 19, 1019–1034, https://doi.org/10.1175/1520-0426(2002)019<1019:TEONBF>2.0.CO;2, 2002. a, b
Tanelli, S., Im, E., Mascelloni, S. R., and Facheris, L.: Spaceborne Doppler radar measurements of rainfall: correction of errors induced by pointing uncertainties, J. Atmos. Ocean Technol., 22, 1676–1690, https://doi.org/10.1175/JTECH1797.1, 2005. a, b
Tiedtke, M.: A Comprehensive Mass Flux Scheme for Cumulus Parameterization in Large-Scale Models, Monthly Weather Review, 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989. a
Varble, A., Zipser, E. J., Fridlind, A. M., Zhu, P., Ackerman, A. S., Chaboureau, J.-P., Collis, S., Fan, J., Hill, A., and Shipway, B.: Evaluation of cloud-resolving and limited area model intercomparison simulations using TWP-ICE observations: 1. Deep convective updraft properties, J. Geophys. Res. Atmos., 119, 13891–13918, https://doi.org/10.1002/2013JD021371, 2014. a
Wang, D., Giangrande, S. E., Feng, Z., Hardin, J. C., and Prein, A. F.: Updraft and Downdraft Core Size and Intensity as Revealed by Radar Wind Profilers: MCS Observations and Idealized Model Comparisons, J. Geophys. Res. Atmos., 125, e2019JD031774, https://doi.org/10.1029/2019JD031774, 2020. a
Wehr, T., Kubota, T., Tzeremes, G., Wallace, K., Nakatsuka, H., Ohno, Y., Koopman, R., Rusli, S., Kikuchi, M., Eisinger, M., Tanaka, T., Taga, M., Deghaye, P., Tomita, E., and Bernaerts, D.: The EarthCARE mission – science and system overview, Atmos. Meas. Tech., 16, 3581–3608, https://doi.org/10.5194/amt-16-3581-2023, 2023. a
Xu, W. and Zipser, E.: Properties of deep convection in tropical continental, monsoon, and oceanic rainfall regimes, Geophys. Res. Lett., 39, https://doi.org/10.1029/2012GL051242, 2012. a
Yang, J., Wang, Z., Heymsfield, A. J., and French, J. R.: Characteristics of vertical air motion in isolated convective clouds, Atmos. Chem. Phys., 16, 10159–10173, https://doi.org/10.5194/acp-16-10159-2016, 2016. a
Yang, K., Wang., Deng, M., and Dettmann, B.: Combining CloudSat/CALIPSO and MODIS measurements to reconstruct tropical convective cloud structure, Remote Sensing of Environment, 287, 113478, https://doi.org/10.1016/j.rse.2023.113478, 2023. a
Yokoyama, C., Zipser, E. J., and Liu, C.: TRMM-Observed Shallow versus Deep Convection in the Eastern Pacific Related to Large-Scale Circulations in Reanalysis Datasets, J. Climate, 27, 5575–5592, https://doi.org/10.1175/JCLI-D-13-00315.1, 2014. a
Zhang, J. and Wang, S.: An Automated 2D Multipass Doppler Radar Velocity Dealiasing Scheme, J. Atmos. Ocean Technol., 23, 1239–1248, https://doi.org/10.1175/JTECH1910.1, 2006. a
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.
Convection drives atmospheric circulation but is difficult to observe and model. EarthCARE's...