Articles | Volume 16, issue 11
https://doi.org/10.5194/amt-16-2795-2023
© Author(s) 2023. 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-16-2795-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Jun 2023
Research article |
| 06 Jun 2023
| 06 Jun 2023
The classification of atmospheric hydrometeors and aerosols from the EarthCARE radar and lidar: the A-TC, C-TC and AC-TC products
Abdanour Irbah
CORRESPONDING AUTHOR
Laboratoire Atmosphére, Milieux et Observations Spatiales, Guyancourt, France
Julien Delanoë
Laboratoire Atmosphére, Milieux et Observations Spatiales, Guyancourt, France
Gerd-Jan van Zadelhoff
Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
David P. Donovan
Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
Pavlos Kollias
Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Canada
Bernat Puigdomènech Treserras
Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Canada
Shannon Mason
European Centre for Medium-range Weather Forecasts, Reading, UK
National Centre for Earth Observation, University of Reading, Reading, UK
Robin J. Hogan
European Centre for Medium-range Weather Forecasts, Reading, UK
Aleksandra Tatarevic
Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Canada
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Jiseob Kim, Pavlos Kollias, Bernat Puigdomènech Treserras, Alessandro Battaglia, Zhipeng Qu, and Cuong Nguyen
EGUsphere, https://doi.org/10.5194/egusphere-2026-6312, https://doi.org/10.5194/egusphere-2026-6312, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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EarthCARE's Cloud Profiling Radar measures how fast ice particles fall. We used it to detect riming, a process in which liquid droplets freeze onto ice particles and make them fall faster. Our method uses sharp increases in fall speed toward cloud base. Detections were more frequent in clouds with more liquid water and supported by aircraft observations. We produced the first near-global map of riming in stratiform clouds, providing new observational constraints for weather and climate models.
Kalliopi Artemis Voudouri, Andreas Karipis, Alexandra Tsekeri, Otto Hasekamp, Kirk Knobelspiesse, Meng Gao, Santiago Gasso, Gerd-jan van Zadelhoff, Athanasios Tsikerdekis, Guangliang Fu, Anton Lopatin, Oleg Dubovik, Edward Malina, Eleni Marinou, and Vassilis Amiridis
EGUsphere, https://doi.org/10.5194/egusphere-2026-5990, https://doi.org/10.5194/egusphere-2026-5990, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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This study evaluates two satellite-based methods for retrieving aerosol layer height using observations from the PACE and EarthCARE missions. We assess the consistency of their retrievals under different atmospheric conditions and across diverse regions, identifying when they agree best and highlighting the strengths and limitations of each approach. The results support the validation of these missions, which are expected to provide long-term observations for reliable climate data records.
Victor J. H. Trees, Ping Wang, Job I. Wiltink, Piet Stammes, Daphne M. Stam, David P. Donovan, and A. Pier Siebesma
Geosci. Model Dev., 19, 9677–9728, https://doi.org/10.5194/gmd-19-9677-2026, https://doi.org/10.5194/gmd-19-9677-2026, 2026
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We present MONKI (Monte Carlo KNMI), an accurate and efficient Fortran radiative transfer code for simulating total and polarised sunlight in 3D planetary atmospheres. Its novelty is the derivation of Monte Carlo equations for the transfer of polarised light, fully accounting for polarisation-dependent scattering directions. This formulation gives reliable convergence even for optically thick, strongly polarising atmospheres, supporting observations of Earth, Venus, or any other planet.
Johanna Mayer, Daniele Gasbarra, Robin J. Hogan, Edward Malina, Shannon L. Mason, and Blanka Piskala Gvozdikova
Atmos. Chem. Phys., 26, 14085–14110, https://doi.org/10.5194/acp-26-14085-2026, https://doi.org/10.5194/acp-26-14085-2026, 2026
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We investigate transitions from closed to open stratocumulus cells using synergistic observations from the EarthCARE and GOES satellites. Microphysical changes and increasing rain up to 25 hours before transitions support a precipitation-driven pathway. These findings provide new observational constraints on the processes driving stratocumulus breakup and its radiative impact.
Nathan Feuillard, Felipe Toledo Bittner, Lukas Pfitzenmaier, Jean-François Ribaud, Julien Delanoë, Martial Haeffelin, and Jean-Charles Dupont
Atmos. Meas. Tech., 19, 6075–6097, https://doi.org/10.5194/amt-19-6075-2026, https://doi.org/10.5194/amt-19-6075-2026, 2026
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The Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) satellite was recently launched and its data needs to be validated. This work focus on the comparison of the data observed from the radar of EarthCARE and from the ground radars of the European Aerosol, Clouds and Trace Gases Research Infrastructure network. We find a very small difference between the data observed by the satellite and the data from the ground, confirming the high quality of the satellite’s radar calibration.
Konstantin Krüger, Martin Wirth, Athena Augusta Floutsi, David Patrick Donovan, Gerd-Jan van Zadelhoff, and Silke Martha Groß
Atmos. Meas. Tech., 19, 5923–5950, https://doi.org/10.5194/amt-19-5923-2026, https://doi.org/10.5194/amt-19-5923-2026, 2026
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Cloud properties from three ATLID Level 2 products (A-TC, A-FM, A-CTH) of the EarthCARE satellite mission are validated using independent airborne lidar observations collected during satellite underflights in the tropics and extratropics. The products reliably represent cloud pixel distribution and cloud top height; though ice cloud pixels and cloud top height are slightly overestimated. Comparison with a more recent baseline shows improvements, supporting their use for cloud property studies.
Woosub Roh, Masaki Satoh, Romain Fiévet, Mark D. Fielding, Robin J. Hogan, Bjorn Stevens, Takuji Kubota, and Hajime Okamoto
EGUsphere, https://doi.org/10.5194/egusphere-2026-3606, https://doi.org/10.5194/egusphere-2026-3606, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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Clouds and rainfall are still difficult to represent accurately in weather and climate models. We introduce a new project called the EarthCARE–ORCESTRA Model Intercomparison Project (ECOMIP), which uses observations from the new Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite and a tropical field campaign to compare and improve atmospheric models. Initial results show that different models can reproduce the same storm system but differ in its size and structure.
Zhipeng Qu, Cuong Nguyen, Paloma Borque, Alexei Korolev, Howard W. Barker, Keyvan Ranjbar, Lei Liu, Zen Mariani, Meriem Kacimi, Melissa Cholette, Ivan Heckman, Kenny Bala, Natalia Bliankinshtein, Leonid Nichman, Benjamin Riot-Bretêcher, Yi Huang, Jason N. S. Cole, Raphaël Peroni, Stephen Holden, Robert Reed, Robert Crawford, Daniel Michelson, Eve Bigras, Michael Wheeler, John Gyakum, Jason A. Milbrandt, Julie M. Thériault, Kaley A. Walker, Mengistu Wolde, Jean-Pierre Blanchet, Yann Blanchard, Jonas von Bismark, Rob Koopman, Montserrat Piñol Solé, Bernat Puigdomènech Treserras, Robin J. Hogan, and Shannon L. Mason
EGUsphere, https://doi.org/10.5194/egusphere-2026-5347, https://doi.org/10.5194/egusphere-2026-5347, 2026
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The ECALOT campaign brought together aircraft and ground-based observations in the Ottawa–Montreal region to help evaluate the European Space Agency’s EarthCARE satellite, which studies clouds, aerosols, radiation, and their interactions. By comparing satellite measurements with multi-platform remote-sensing and in situ observations collected during the campaign, ECALOT provides an integrated approach to evaluating EarthCARE products at different stages of the data processing chain.
Robin J. Hogan, Bernat Puigdomenech Treserras, Shannon L. Mason, Alexei Korolev, Zhipeng Qu, Kamil Mroz, David P. Donovan, Pavlos Kollias, and Mark D. Fielding
EGUsphere, https://doi.org/10.5194/egusphere-2026-5054, https://doi.org/10.5194/egusphere-2026-5054, 2026
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The EarthCARE satellite carries advanced instruments that allow it to accurately infer vertical profiles of the properties of ice clouds, important for understanding their role in the climate system. Here we use detailed observations from meteorological research aircraft flying underneath the satellite to evaluate EarthCARE’s estimates of the mass of ice in the clouds, the average particle size, and even how fast the air is rising and falling within the cloud due to atmospheric gravity waves.
Esmail Ghaemi, Andreas Kvas, Ulrich Foelsche, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2026-4256, https://doi.org/10.5194/egusphere-2026-4256, 2026
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Knowing how moisture and cloud water change before rain helps to understand and predict precipitation. We used four years of ground-based measurements in southeast Austria to follow water vapor, cloud liquid water, and cloud base height during the hours before rain. We found that the column moistens first and the liquid water builds up mainly in the last hour, with clear differences between shower-type and widespread rain. These signals can help to test weather models and their prediction.
Paolo Andreozzi, Mark D. Fielding, Robin J. Hogan, Richard M. Forbes, Adrian A. Hill, Samuel Rémy, Birger Bohn, and Ulrich Löhnert
Atmos. Chem. Phys., 26, 11309–11338, https://doi.org/10.5194/acp-26-11309-2026, https://doi.org/10.5194/acp-26-11309-2026, 2026
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Aerosols significantly contribute to the Earth’s climate, but models still struggle to represent them. Here we use satellite observations of clouds to constrain aerosol processes in a weather and air-quality model. We show that African wildfires lead to overly bright simulated clouds and that the model removes too much aerosol in mixed-phase clouds. This demonstrates how cloud observations can target weakly constrained aerosol processes, with potential benefits for weather and climate modelling.
Nicolas Chiabrando, Gwendal Rivière, Étienne Vignon, and Julien Delanoë
EGUsphere, https://doi.org/10.5194/egusphere-2026-3987, https://doi.org/10.5194/egusphere-2026-3987, 2026
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Arctic cyclones are large-scale depressions evolving within the Arctic circle. We study a summertime Arctic cyclone that moved near Svalbard in 2022 and was well observed during the THINICE field campaign to understand what controls the intensity of a near-surface wind jet associated with the cyclone. We find that clouds and near-surface frictional effects globally strengthened the wind while the cyclone was growing, but became much less important after it evolved toward a cold-core structure.
Heather Corden, Julien Delanoë, Massimo Del Guasta, and Alexis Berne
EGUsphere, https://doi.org/10.5194/egusphere-2026-3964, https://doi.org/10.5194/egusphere-2026-3964, 2026
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In Antarctica, intrusions of warm, moist air from the coast to the high plateau are responsible for a significant proportion of the snow accumulating on the ice sheet each year. We present a case study of an intrusion in East Antarctica in February 2025. Meteorological radars along a 1100-km transect were used to investigate the processes leading to precipitation in the intrusion. The processes vary according to the changing temperature, humidity and wind dynamics between the coast and plateau.
Anthony J. Baran, Stuart Fox, Richard Cotton, Julien Delanoë, Christopher J. Walden, Karina McCusker, Christopher D. Westbrook, and Peter G. Huggard
Atmos. Meas. Tech., 19, 4367–4392, https://doi.org/10.5194/amt-19-4367-2026, https://doi.org/10.5194/amt-19-4367-2026, 2026
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We demonstrate how multi-frequency ground-based radars at 3, 35 and 94 GHz can be used to determine vertical profiles of ice-particle size spectra by combining reflectivity with machine-learning prior information on UK wintertime ice clouds. The method is validated using independent profiles from a 200 GHz radar and aircraft-based in-situ observations. It gives a consistent representation to compare with aircraft-based radiometric measurements in future radiative-transfer closure studies.
Aymeric Dziduch, Antoine Baudoux, Guillaume Mioche, Antoine Canzi, Benjamin Doiteau, Manuel Moser, Emma Järvinen, Étienne Vignon, Jean-Christophe Raut, Régis Dupuy, Christophe Gourbeyre, Jean-Luc Baray, Marie Monier, Frédéric Szczap, Guillaume Penide, Julien Delanoë, Odran Sourdeval, Alfons Schwarzenboeck, Stephan Mertes, Franz Martin Schnaiter, Christiane Voigt, Mario Mech, Susanne Crewell, Andreas Herber, Christof Lüpkes, André Ehrlich, Manfred Wendisch, and Olivier Jourdan
EGUsphere, https://doi.org/10.5194/egusphere-2026-3265, https://doi.org/10.5194/egusphere-2026-3265, 2026
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This study examines the microphysical properties and ice crystal habits of low-level Arctic mixed-phase clouds around Svalbard. Using airborne in situ observations from four campaigns, we show that cloud microphysical structure and ice crystal habits are influenced by weather conditions and surface type. These results provide quantitative observations to better understand low-level Arctic clouds, their ice formation processes, and their influence on the Arctic atmosphere and climate system.
Holger Baars, Moritz Haarig, Leonard König, Athena A. Floutsi, Elizaveta Basharova, Julian Hofer, Henriette Gebauer, Ronny Engelmann, Dietrich Althausen, Annett Skupin, Benedikt Gast, Felix Fritzsch, Kevin Ohneiser, Cristofer Jimenez, Tom Gaudek, Martin Radenz, Håvard Buholdt, Birgit Heese, Andi Klamt, Patric Seifert, David P. Donovan, Gerd-Jan van Zadelhoff, Sabur F. Abdullaev, Shohina K. Khalifaeva, Dilovar F. Nozirov, and Ulla Wandinger
Atmos. Meas. Tech., 19, 3831–3864, https://doi.org/10.5194/amt-19-3831-2026, https://doi.org/10.5194/amt-19-3831-2026, 2026
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The satellite mission EarthCARE (Earth Cloud, Aerosol, and Radiation Explorer) was launched in May 2024 and has 4 unique instruments on board. We used ground-based PollyNET/ACTRIS (Aerosol, Clouds and Trace gases Research Infrastructure) profiling data to discuss the quality of EarthCARE’s lidar products based on case studies from different geographic locations. Our investigations revealed that the space-borne lidar instrument has remarkable profiling capabilities but currently also still some limitations, however, not preventing first scientific studies when quality checking the data intensively.
Benedict Pery, Helen Brindley, Jonathan Murray, Sophie Mosselmans, Sanjeevani Panditharatne, Laura Warwick, Robin Hogan, Yi Huang, Benjamin Riot-Bretêcher, Hilke Oetjen, Dirk Schuettemeyer, and Tristan L'Ecuyer
EGUsphere, https://doi.org/10.5194/egusphere-2026-2443, https://doi.org/10.5194/egusphere-2026-2443, 2026
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The Polar Radiant Energy in the Far-InfraRed Experiment (PREFIRE) satellite mission measures the Earth in a rarely measured part of the electromagnetic spectrum. To build confidence in these new measurements, we test them with an experiment observing the same atmosphere as PREFIRE but from the ground. We find that the PREFIRE measurements agree with what we expect from our ground-based observations, with some unexplained differences needing further study.
Kalliopi Artemis Voudouri, Eleni Marinou, Ilianna Koutsoupi, Vassilis Spyrakos, Dimitra Karkani, Ioanna Tsikoudi, Alessandro Battaglia, Pavlos Kollias, Eleni Giannakaki, and Vassilis Amiridis
EGUsphere, https://doi.org/10.5194/egusphere-2026-2384, https://doi.org/10.5194/egusphere-2026-2384, 2026
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Cloud observations from the CloudSat mission (2007–2017) reveal how clouds vary per altitude and season across the Mediterranean. Comparisons with ERA5 reanalysis show good overall agreement, though ice clouds are often underestimated. Early EarthCARE mission data are also presented, towards improving long-term climate records.
Susmitha Sasikumar, Alessandro Battaglia, Bernat Puigdomènech Treserras, and Pavlos Kollias
Atmos. Meas. Tech., 19, 2313–2328, https://doi.org/10.5194/amt-19-2313-2026, https://doi.org/10.5194/amt-19-2313-2026, 2026
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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.
Zhuocan Xu, Pavlos Kollias, Susmitha Sasikumar, Alessandro Battaglia, Bernat Puigdomènech Treserras, and Matthew L. Walker McLinden
Atmos. Chem. Phys., 26, 4619–4632, https://doi.org/10.5194/acp-26-4619-2026, https://doi.org/10.5194/acp-26-4619-2026, 2026
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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.
Aymeric Dziduch, Guillaume Mioche, Quentin Coopman, Clément Bazantay, Julien Delanoë, and Olivier Jourdan
Atmos. Chem. Phys., 26, 4019–4047, https://doi.org/10.5194/acp-26-4019-2026, https://doi.org/10.5194/acp-26-4019-2026, 2026
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Low-level clouds play a central role in the Arctic climate by regulating energy exchanges between the surface and the atmosphere. Using eight years of CALIPSO–CloudSat observations, this study investigates the regional and seasonal variability of Arctic low-level cloud types. Mixed-phase and supercooled liquid clouds are frequent over oceanic regions, especially during transition seasons. Their occurrence is strongly influenced by surface coupling, temperature, stability, and cold-air outbreaks.
Michael Kahnert, Melanie Ades, Mickaël Bacles, Johannes Flemming, Vincent Guidard, Alexander Haefele, Robin J. Hogan, Samuel Rémy, and Eric Sauvageat
EGUsphere, https://doi.org/10.5194/egusphere-2025-6077, https://doi.org/10.5194/egusphere-2025-6077, 2026
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The Copernicus Atmosphere Monitoring Service (CAMS) provides quality-controlled information related to air quality and health. We explore the possibility to constrain the CAMS global forecasting model by use of ground-based observations of laser light backscattered by particulate matter. We find that the vertical distribution of particulate matter can be predicted more faithfully with this approach, which can have implications for air quality forecasts provided by CAMS to end users.
Thirza N. Feenstra, Willem Jan van de Berg, Gerd-Jan van Zadelhoff, David P. Donovan, Christiaan T. van Dalum, and Michiel R. van den Broeke
Atmos. Meas. Tech., 19, 1323–1344, https://doi.org/10.5194/amt-19-1323-2026, https://doi.org/10.5194/amt-19-1323-2026, 2026
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Cloud representation brings large uncertainties in polar climate modeling. We show the first evaluation of Greenland clouds in the Regional Atmospheric Climate Model (RACMO2.4) with new EarthCARE satellite data. Comparing lidar and radar observations and retrieved cloud profiles with co-located RACMO output, we find RACMO captures low ice clouds but underestimates high clouds, mid-altitude liquid clouds, and snowfall. These results highlight EarthCARE's potential to improve polar climate models.
Romain Joseph, Jérôme Vidot, Julien Delanoë, and Emmanuel Fontaine
EGUsphere, https://doi.org/10.5194/egusphere-2026-48, https://doi.org/10.5194/egusphere-2026-48, 2026
Preprint archived
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We used aircraft radar measurements inside tropical storm clouds to estimate their ice content, then simulated satellite observations using a radiative transfer model. By testing different assumptions about the shape and number of ice crystals, we examined how these choices influence the simulated radiances. We found that satellite information for these clouds is mainly controlled by conditions near the cloud top, while deeper layers remain largely hidden.
Konstantinos Rizos, Emmanouil Proestakis, Thanasis Georgiou, Antonis Gkikas, Eleni Marinou, Peristera Paschou, Kalliopi Artemis Voudouri, Athanasios Tsikerdekis, David P. Donovan, Gerd-Jan van Zadelhoff, Angela Benedetti, Holger Baars, Athena Augusta Floutsi, Nikos Benas, Martin Stengel, Christian Retscher, Edward Malina, and Vassilis Amiridis
Atmos. Meas. Tech., 19, 699–728, https://doi.org/10.5194/amt-19-699-2026, https://doi.org/10.5194/amt-19-699-2026, 2026
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The Aeolus satellite's lidar system had limitations in detecting certain atmospheric layers and distinguishing between aerosol and cloud types. To improve accuracy, a new dust detection product was developed. By combining data from various sources and validating it with ground-based measurements, this enhanced product performs better than the original. It helps improve dust transport models and weather predictions, making it a valuable tool for atmospheric monitoring and forecasting.
Sandrine Bony, Basile Poujol, Brett McKim, Nicolas Rochetin, Marie Lothon, Julia Windmiller, Nicolas Maury, Clarisse Dufaux, Louis Jaffeux, Patrick Chazette, and Julien Delanoë
Atmos. Chem. Phys., 25, 17331–17362, https://doi.org/10.5194/acp-25-17331-2025, https://doi.org/10.5194/acp-25-17331-2025, 2025
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Space photographs of the Earth show that clouds form diverse, common but poorly understood cloud patterns. The analysis of observations gathered from research aircraft over the tropical ocean shows that the merging of thermals and clouds in the first kilometer of the atmosphere plays a key role in controlling the size, depth and spacing of clouds. This reveals a fundamental process through which clouds interact with each other and with their environment.
Jialin Yan, Mariko Oue, Pavlos Kollias, Edward Luke, and Fan Yang
Atmos. Chem. Phys., 25, 16479–16490, https://doi.org/10.5194/acp-25-16479-2025, https://doi.org/10.5194/acp-25-16479-2025, 2025
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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.
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
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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.
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
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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
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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
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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
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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.
Kaah P. Menang, Stefan A. Buehler, Lukas Kluft, Robin J. Hogan, and Florian E. Roemer
Atmos. Chem. Phys., 25, 11689–11701, https://doi.org/10.5194/acp-25-11689-2025, https://doi.org/10.5194/acp-25-11689-2025, 2025
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We investigated the impact of the shortwave water vapour continuum absorption on clear-sky shortwave radiative feedback. For current temperatures, the impact is modest (<2%). In a warmer world, continuum-induced uncertainty in estimated feedback would be up to ~5%. Representing continuum absorption with the widely used semi-empirical model in radiative transfer calculations leads to an underestimation of this feedback. Constraining the shortwave continuum will help reduce these discrepancies.
Peristera Paschou, Nikolaos Siomos, Eleni Marinou, Antonis Gkikas, Samira M. Idrissa, Daniel T. Quaye, Désiré D. Fiogbe Attannon, Kalliopi Artemis Voudouri, Charikleia Meleti, David P. Donovan, George Georgoussis, Tommaso Parrinello, Thorsten Fehr, Jonas von Bismarck, and Vassilis Amiridis
Atmos. Meas. Tech., 18, 4731–4754, https://doi.org/10.5194/amt-18-4731-2025, https://doi.org/10.5194/amt-18-4731-2025, 2025
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This study presents the results from a validation study on the Level 2A products (aerosol optical properties) of the ESA's (European Space Agency) Aeolus mission. Measurements from the eVe lidar, a combined linear/circular polarization and Raman lidar and ESA's ground reference system, that have been collected during the Joint Aeolus Tropical Atlantic Campaign are compared with collocated Aeolus Level 2A profiles obtained from the latest version (Baseline 16) of the Aeolus algorithms.
Guillaume Feger, Jean-Pierre Chaboureau, Thibaut Dauhut, Julien Delanoë, and Pierre Coutris
Atmos. Chem. Phys., 25, 7447–7465, https://doi.org/10.5194/acp-25-7447-2025, https://doi.org/10.5194/acp-25-7447-2025, 2025
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Saharan air at the trade wind layer, cold pools, and dry upper troposphere has these three main factors inhibiting the cyclogenesis of the Pierre Henri mesoscale convective system. The findings were obtained through observations made during two flights of the Clouds-Atmospheric Dynamics-Dust Interactions in West Africa (CADDIWA) campaign and a convection-permitting simulation run with the Meso-NH model. They provide new insights into the complex dynamics of cyclogenesis in the Cabo Verde region and challenge the existing model of the Saharan Air Layer (SAL).
Howard W. Barker, Jason N. S. Cole, Najda Villefranque, Zhipeng Qu, Almudena Velázquez Blázquez, Carlos Domenech, Shannon L. Mason, and Robin J. Hogan
Atmos. Meas. Tech., 18, 3095–3107, https://doi.org/10.5194/amt-18-3095-2025, https://doi.org/10.5194/amt-18-3095-2025, 2025
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Measurements made by three instruments aboard EarthCARE are used to retrieve estimates of cloud and aerosol properties. A radiative closure assessment of these retrievals is performed by the ACMB-DF processor. Radiative transfer models acting on retrieved information produce broadband radiances commensurate with measurements made by EarthCARE’s broadband radiometer. Measured and modelled radiances for small domains are compared, and the likelihood of them differing by 10 W m2 defines the closure.
Aymeric Dziduch, Guillaume Mioche, Quentin Coopman, Clément Bazantay, Julien Delanoë, and Olivier Jourdan
EGUsphere, https://doi.org/10.5194/egusphere-2025-2698, https://doi.org/10.5194/egusphere-2025-2698, 2025
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Low-level clouds play a central role in the Arctic climate, modulating the surface energy budget with competing warming and cooling effects. In this study, radar-lidar satellite products are used to investigate the geographical and seasonal variations of different cloud types observed over eight years. Oceanic regions are characterized by high occurrences of low-level mixed-phase clouds, which are influenced by atmospheric stability conditions, surface temperatures and cold air outbreaks.
Théophane Costabloz, Frédéric Burnet, Christine Lac, Pauline Martinet, Julien Delanoë, Susana Jorquera, and Maroua Fathalli
Atmos. Chem. Phys., 25, 6539–6573, https://doi.org/10.5194/acp-25-6539-2025, https://doi.org/10.5194/acp-25-6539-2025, 2025
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This study documents vertical profiles of liquid water content (LWC) in fogs from in situ measurements collected during the SOFOG3D field campaign in 2019–2020. The analysis of 140 vertical profiles reveals a reverse trend in LWC, maximum values at ground decreasing with height, during stable conditions in optically thin fogs, evolving towards quasi-adiabatic characteristics when fogs become thick. These results offer new perspectives for better constraining fog numerical simulations.
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
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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.
Martin de Graaf, Maarten Sneep, Mark ter Linden, L. Gijsbert Tilstra, David P. Donovan, Gerd-Jan van Zadelhoff, and J. Pepijn Veefkind
Atmos. Meas. Tech., 18, 2553–2571, https://doi.org/10.5194/amt-18-2553-2025, https://doi.org/10.5194/amt-18-2553-2025, 2025
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The aerosol layer height (ALH) from the TROPOspheric Monitoring Instrument (TROPOMI) has been constantly improved since its release in 2019. Over bright surfaces, fitting the albedo improved the retrieval, as shown for a set of situations, ranging from multiple layers of smoke to thick desert dust plumes and low-altitude industrial pollution. The latest results of the operational ALH are compared to profiles from the ATmospheric LIDar (ATLID) on board the recently launched EarthCARE mission.
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
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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.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
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The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
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
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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.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
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Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Lev D. Labzovskii, Gerd-Jan van Zadelhoff, David P. Donovan, Jos de Kloe, L. Gijsbert Tilstra, Ad Stoffelen, Damien Josset, and Piet Stammes
Atmos. Meas. Tech., 17, 7183–7208, https://doi.org/10.5194/amt-17-7183-2024, https://doi.org/10.5194/amt-17-7183-2024, 2024
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The Atmospheric Laser Doppler Instrument (ALADIN) on the Aeolus satellite was the first of its kind to measure high-resolution vertical profiles of aerosols and cloud properties from space. We present an algorithm that produces Aeolus lidar surface returns (LSRs), containing useful information for measuring UV reflectivity. Aeolus LSRs matched well with existing UV reflectivity data from other satellites, like GOME-2 and TROPOMI, and demonstrated excellent sensitivity to modeled snow cover.
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
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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
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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.
Ping Wang, David Patrick Donovan, Gerd-Jan van Zadelhoff, Jos de Kloe, Dorit Huber, and Katja Reissig
Atmos. Meas. Tech., 17, 5935–5955, https://doi.org/10.5194/amt-17-5935-2024, https://doi.org/10.5194/amt-17-5935-2024, 2024
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We describe the new feature mask (AEL-FM) and aerosol profile retrieval (AEL-PRO) algorithms developed for Aeolus lidar and present the evaluation of the Aeolus products using CALIPSO data for dust aerosols over Africa. We have found that Aeolus and CALIPSO show similar aerosol patterns in the collocated orbits and have good agreement for the extinction coefficients for the dust aerosols, especially for the cloud-free scenes. The finding is applicable to Aeolus L2A product Baseline 17.
David Patrick Donovan, Gerd-Jan van Zadelhoff, and Ping Wang
Atmos. Meas. Tech., 17, 5301–5340, https://doi.org/10.5194/amt-17-5301-2024, https://doi.org/10.5194/amt-17-5301-2024, 2024
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ATLID (atmospheric lidar) is the lidar to be flown on the Earth Clouds and Radiation Explorer satellite (EarthCARE). EarthCARE is a joint European–Japanese satellite mission that was launched in May 2024. ATLID is an advanced lidar optimized for cloud and aerosol property profile measurements. This paper describes some of the key novel algorithms being applied to this lidar to retrieve cloud and aerosol properties. Example results based on simulated data are presented and discussed.
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona M. Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schaefer, Vera Schemann, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
Atmos. Chem. Phys., 24, 8865–8892, https://doi.org/10.5194/acp-24-8865-2024, https://doi.org/10.5194/acp-24-8865-2024, 2024
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The Arctic is warming faster than the rest of the globe. Warm-air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold-air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to mid-latitude weather. In our article, we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented with state-of-the-art remote-sensing and in situ measurement devices.
Johannes Röttenbacher, André Ehrlich, Hanno Müller, Florian Ewald, Anna E. Luebke, Benjamin Kirbus, Robin J. Hogan, and Manfred Wendisch
Atmos. Chem. Phys., 24, 8085–8104, https://doi.org/10.5194/acp-24-8085-2024, https://doi.org/10.5194/acp-24-8085-2024, 2024
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Weather prediction models simplify the physical processes related to light scattering by clouds consisting of complex ice crystals. Whether these simplifications are the cause for uncertainties in their prediction can be evaluated by comparing them with measurement data. Here we do this for Arctic ice clouds over sea ice using airborne measurements from two case studies. The model performs well for thick ice clouds but not so well for thin ones. This work can be used to improve the model.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Karina McCusker, Anthony J. Baran, Chris Westbrook, Stuart Fox, Patrick Eriksson, Richard Cotton, Julien Delanoë, and Florian Ewald
Atmos. Meas. Tech., 17, 3533–3552, https://doi.org/10.5194/amt-17-3533-2024, https://doi.org/10.5194/amt-17-3533-2024, 2024
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Polarised radiative transfer simulations are performed using an atmospheric model based on in situ measurements. These are compared to large polarisation measurements to explore whether such measurements can provide information on cloud ice, e.g. particle shape and orientation. We find that using oriented particle models with shapes based on imagery generally allows for accurate simulations. However, results are sensitive to shape assumptions such as the choice of single crystals or aggregates.
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
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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.
Nicole Docter, Anja Hünerbein, David P. Donovan, Rene Preusker, Jürgen Fischer, Jan Fokke Meirink, Piet Stammes, and Michael Eisinger
Atmos. Meas. Tech., 17, 2507–2519, https://doi.org/10.5194/amt-17-2507-2024, https://doi.org/10.5194/amt-17-2507-2024, 2024
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MSI is the imaging spectrometer on board EarthCARE and will provide across-track information on clouds and aerosol properties. The MSI solar channels exhibit a spectral misalignment effect (SMILE) in the measurements. This paper describes and evaluates how the SMILE will affect the cloud and aerosol retrievals that do not account for it.
Hanno Müller, André Ehrlich, Evelyn Jäkel, Johannes Röttenbacher, Benjamin Kirbus, Michael Schäfer, Robin J. Hogan, and Manfred Wendisch
Atmos. Chem. Phys., 24, 4157–4175, https://doi.org/10.5194/acp-24-4157-2024, https://doi.org/10.5194/acp-24-4157-2024, 2024
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A weather model is used to compare solar radiation with measurements from an aircraft campaign in the Arctic. Model and observations agree on the downward radiation but show differences in the radiation reflected by the surface and the clouds, which in the model is too low above sea ice and too high above open ocean. The model–observation bias is reduced above open ocean by a realistic fraction of clouds and less cloud liquid water and above sea ice by less dark sea ice and more cloud droplets.
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
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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
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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.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
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The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Cheikh Dione, Martial Haeffelin, Frédéric Burnet, Christine Lac, Guylaine Canut, Julien Delanoë, Jean-Charles Dupont, Susana Jorquera, Pauline Martinet, Jean-François Ribaud, and Felipe Toledo
Atmos. Chem. Phys., 23, 15711–15731, https://doi.org/10.5194/acp-23-15711-2023, https://doi.org/10.5194/acp-23-15711-2023, 2023
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This paper documents the role of thermodynamics and turbulence in the fog life cycle over southwestern France. It is based on a unique dataset collected during the SOFOG3D field campaign in autumn and winter 2019–2020. The paper gives a threshold for turbulence driving the different phases of the fog life cycle and the role of advection in the night-time dissipation of fog. The results can be operationalised to nowcast fog and improve short-range forecasts in numerical weather prediction models.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
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The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Leonie Villiger, Marina Dütsch, Sandrine Bony, Marie Lothon, Stephan Pfahl, Heini Wernli, Pierre-Etienne Brilouet, Patrick Chazette, Pierre Coutris, Julien Delanoë, Cyrille Flamant, Alfons Schwarzenboeck, Martin Werner, and Franziska Aemisegger
Atmos. Chem. Phys., 23, 14643–14672, https://doi.org/10.5194/acp-23-14643-2023, https://doi.org/10.5194/acp-23-14643-2023, 2023
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This study evaluates three numerical simulations performed with an isotope-enabled weather forecast model and investigates the coupling between shallow trade-wind cumulus clouds and atmospheric circulations on different scales. We show that the simulations reproduce key characteristics of shallow trade-wind clouds as observed during the field experiment EUREC4A and that the spatial distribution of stable-water-vapour isotopes is shaped by the overturning circulation associated with these clouds.
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
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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.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
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The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Megan A. Stretton, William Morrison, Robin J. Hogan, and Sue Grimmond
Geosci. Model Dev., 16, 5931–5947, https://doi.org/10.5194/gmd-16-5931-2023, https://doi.org/10.5194/gmd-16-5931-2023, 2023
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Cities' materials and forms impact radiative fluxes. We evaluate the SPARTACUS-Urban multi-layer approach to modelling longwave radiation, describing realistic 3D geometry statistically using the explicit DART (Discrete Anisotropic Radiative Transfer) model. The temperature configurations used are derived from thermal camera observations. SPARTACUS-Urban accurately predicts longwave fluxes, with a low computational time (cf. DART), but has larger errors with sunlit/shaded surface temperatures.
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
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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.
Ulla Wandinger, Moritz Haarig, Holger Baars, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 4031–4052, https://doi.org/10.5194/amt-16-4031-2023, https://doi.org/10.5194/amt-16-4031-2023, 2023
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We introduce the algorithms that have been developed to derive cloud top height and aerosol layer products from observations with the Atmospheric Lidar (ATLID) onboard the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE). The products provide information on the uppermost cloud and geometrical and optical properties of aerosol layers in an atmospheric column. They can be used individually but also serve as input for algorithms that combine observations with EarthCARE’s lidar and imager.
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
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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.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
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The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Shannon L. Mason, Robin J. Hogan, Alessio Bozzo, and Nicola L. Pounder
Atmos. Meas. Tech., 16, 3459–3486, https://doi.org/10.5194/amt-16-3459-2023, https://doi.org/10.5194/amt-16-3459-2023, 2023
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We present a method for accurately estimating the contents and properties of clouds, snow, rain, and aerosols through the atmosphere, using the combined measurements of the radar, lidar, and radiometer instruments aboard the upcoming EarthCARE satellite, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
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We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as the C-CLD product.
Peter Ukkonen and Robin J. Hogan
Geosci. Model Dev., 16, 3241–3261, https://doi.org/10.5194/gmd-16-3241-2023, https://doi.org/10.5194/gmd-16-3241-2023, 2023
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Climate and weather models suffer from uncertainties resulting from approximated processes. Solar and thermal radiation is one example, as it is computationally too costly to simulate precisely. This has led to attempts to replace radiation codes based on physical equations with neural networks (NNs) that are faster but uncertain. In this paper we use global weather simulations to demonstrate that a middle-ground approach of using NNs only to predict optical properties is accurate and reliable.
Ulla Wandinger, Athena Augusta Floutsi, Holger Baars, Moritz Haarig, Albert Ansmann, Anja Hünerbein, Nicole Docter, David Donovan, Gerd-Jan van Zadelhoff, Shannon Mason, and Jason Cole
Atmos. Meas. Tech., 16, 2485–2510, https://doi.org/10.5194/amt-16-2485-2023, https://doi.org/10.5194/amt-16-2485-2023, 2023
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We introduce an aerosol classification model that has been developed for the Earth Clouds, Aerosols and Radiation Explorer (EarthCARE). The model provides a consistent description of microphysical, optical, and radiative properties of common aerosol types such as dust, sea salt, pollution, and smoke. It is used for aerosol classification and assessment of radiation effects based on the synergy of active and passive observations with lidar, imager, and radiometer of the multi-instrument platform.
Martin de Graaf, Karolina Sarna, Jessica Brown, Elma V. Tenner, Manon Schenkels, and David P. Donovan
Atmos. Chem. Phys., 23, 5373–5391, https://doi.org/10.5194/acp-23-5373-2023, https://doi.org/10.5194/acp-23-5373-2023, 2023
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Clouds over the oceans reflect sunlight and cool the earth. Simultaneous measurements were performed of cloud droplet sizes and smoke particles in and near the cloud base over Ascension Island, a remote island in the Atlantic Ocean, to determine the sensitivity of cloud droplets to smoke from the African continent. The smoke was found to reduce cloud droplet sizes, which makes the cloud droplets more susceptible to evaporation, reducing cloud lifetime.
Pavlos Kollias, Bernat Puidgomènech Treserras, Alessandro Battaglia, Paloma C. Borque, and Aleksandra Tatarevic
Atmos. Meas. Tech., 16, 1901–1914, https://doi.org/10.5194/amt-16-1901-2023, https://doi.org/10.5194/amt-16-1901-2023, 2023
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The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission developed by the European Space Agency (ESA) and Japan Aerospace Exploration Agency (JAXA) features the first spaceborne 94 GHz Doppler cloud-profiling radar (CPR) with Doppler capability. Here, we describe the post-processing algorithms that apply quality control and corrections to CPR measurements and derive key geophysical variables such as hydrometeor locations and best estimates of particle sedimentation fall velocities.
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
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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.
Pragya Vishwakarma, Julien Delanoë, Susana Jorquera, Pauline Martinet, Frederic Burnet, Alistair Bell, and Jean-Charles Dupont
Atmos. Meas. Tech., 16, 1211–1237, https://doi.org/10.5194/amt-16-1211-2023, https://doi.org/10.5194/amt-16-1211-2023, 2023
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Cloud observations are necessary to characterize the cloud properties at local and global scales. The observations must be translated to cloud geophysical parameters. This paper presents the estimation of liquid water content (LWC) using radar and microwave radiometer (MWR) measurements. Liquid water path from MWR scales LWC and retrieves the scaling factor (ln a). The retrievals are compared with in situ observations. A climatology of ln a is built to estimate LWC using only radar information.
Alistair Bell, Pauline Martinet, Olivier Caumont, Frédéric Burnet, Julien Delanoë, Susana Jorquera, Yann Seity, and Vinciane Unger
Atmos. Meas. Tech., 15, 5415–5438, https://doi.org/10.5194/amt-15-5415-2022, https://doi.org/10.5194/amt-15-5415-2022, 2022
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Cloud radars and microwave radiometers offer the potential to improve fog forecasts when assimilated into a high-resolution model. As this process can be complex, a retrieval of model variables is sometimes made as a first step. In this work, results from a 1D-Var algorithm for the retrieval of temperature, humidity and cloud liquid water content are presented. The algorithm is applied first to a synthetic dataset and then to a dataset of real measurements from a recent field campaign.
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
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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.
Meryl Wimmer, Gwendal Rivière, Philippe Arbogast, Jean-Marcel Piriou, Julien Delanoë, Carole Labadie, Quitterie Cazenave, and Jacques Pelon
Weather Clim. Dynam., 3, 863–882, https://doi.org/10.5194/wcd-3-863-2022, https://doi.org/10.5194/wcd-3-863-2022, 2022
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The effect of deep convection representation on the jet stream above the cold front of an extratropical cyclone is investigated in the global numerical weather prediction model ARPEGE. Two simulations using different deep convection schemes are compared with (re)analysis datasets and NAWDEX airborne observations. A deeper jet stream is observed with the less active scheme. The diabatic origin of this difference is interpreted by backward Lagrangian trajectories and potential vorticity budgets.
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
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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.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 15, 3121–3140, https://doi.org/10.5194/amt-15-3121-2022, https://doi.org/10.5194/amt-15-3121-2022, 2022
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Cloud shadows are observed by the TROPOMI satellite instrument as a result of its high spatial resolution. These shadows contaminate TROPOMI's air quality measurements, because shadows are generally not taken into account in the models that are used for aerosol and trace gas retrievals. We present the Detection AlgoRithm for CLOud Shadows (DARCLOS) for TROPOMI, which is the first cloud shadow detection algorithm for a satellite spectrometer.
Alessandro Battaglia, Paolo Martire, Eric Caubet, Laurent Phalippou, Fabrizio Stesina, Pavlos Kollias, and Anthony Illingworth
Atmos. Meas. Tech., 15, 3011–3030, https://doi.org/10.5194/amt-15-3011-2022, https://doi.org/10.5194/amt-15-3011-2022, 2022
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We present an instrument simulator for a new sensor, WIVERN (WInd VElocity Radar Nephoscope), a conically scanning radar payload with Doppler capabilities, recently down-selected as one of the four candidates for the European Space Agency Earth Explorer 11 program. The mission aims at measuring horizontal winds in cloudy areas. The simulator is instrumental in the definition and consolidation of the mission requirements and the evaluation of mission performances.
Sandrine Bony, Marie Lothon, Julien Delanoë, Pierre Coutris, Jean-Claude Etienne, Franziska Aemisegger, Anna Lea Albright, Thierry André, Hubert Bellec, Alexandre Baron, Jean-François Bourdinot, Pierre-Etienne Brilouet, Aurélien Bourdon, Jean-Christophe Canonici, Christophe Caudoux, Patrick Chazette, Michel Cluzeau, Céline Cornet, Jean-Philippe Desbios, Dominique Duchanoy, Cyrille Flamant, Benjamin Fildier, Christophe Gourbeyre, Laurent Guiraud, Tetyana Jiang, Claude Lainard, Christophe Le Gac, Christian Lendroit, Julien Lernould, Thierry Perrin, Frédéric Pouvesle, Pascal Richard, Nicolas Rochetin, Kevin Salaün, Alfons Schwarzenboeck, Guillaume Seurat, Bjorn Stevens, Julien Totems, Ludovic Touzé-Peiffer, Gilles Vergez, Jessica Vial, Leonie Villiger, and Raphaela Vogel
Earth Syst. Sci. Data, 14, 2021–2064, https://doi.org/10.5194/essd-14-2021-2022, https://doi.org/10.5194/essd-14-2021-2022, 2022
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The French ATR42 research aircraft participated in the EUREC4A international field campaign that took place in 2020 over the tropical Atlantic, east of Barbados. We present the extensive instrumentation of the aircraft, the research flights and the different measurements. We show that the ATR measurements of humidity, wind, aerosols and cloudiness in the lower atmosphere are robust and consistent with each other. They will make it possible to advance understanding of cloud–climate interactions.
Beatriz M. Monge-Sanz, Alessio Bozzo, Nicholas Byrne, Martyn P. Chipperfield, Michail Diamantakis, Johannes Flemming, Lesley J. Gray, Robin J. Hogan, Luke Jones, Linus Magnusson, Inna Polichtchouk, Theodore G. Shepherd, Nils Wedi, and Antje Weisheimer
Atmos. Chem. Phys., 22, 4277–4302, https://doi.org/10.5194/acp-22-4277-2022, https://doi.org/10.5194/acp-22-4277-2022, 2022
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The stratosphere is emerging as one of the keys to improve tropospheric weather and climate predictions. This study provides evidence of the role the stratospheric ozone layer plays in improving weather predictions at different timescales. Using a new ozone modelling approach suitable for high-resolution global models that provide operational forecasts from days to seasons, we find significant improvements in stratospheric meteorological fields and stratosphere–troposphere coupling.
Cited articles
Barker, H. W., Cole, J. N. S., Qu, Z., Villefranque, N., and Shephard, M.:
Radiative closure assessment of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-DF product, Atmos. Meas. Tech., in preparation, 2023. a
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., 116, D19201, https://doi.org/10.1029/2011JD016142, 2011. a
Burns, D., Kollias, P., Tatarevic, A., Battaglia, A., and Tanelli, S.: The performance of the EarthCARE Cloud Profiling Radar in marine stratiform clouds, J. Geophys. Res.-Atmos., 121, 14525–14537, https://doi.org/10.1002/2016JD025090, 2016. a
Ceccaldi, M., Delanoë, J., Hogan, R. J., Pounder, N. L., Protat, A., and Pelon, J.: From CloudSat-CALIPSO to EarthCare: Evolution of the DARDAR cloud classification and its comparison to airborne radar-lidar observations, J. Geophys. Res.-Atmos., 118, 7962–7981, https://doi.org/10.1002/jgrd.50579, 2013. a, b, c, d
Chandra, A. S., Kollias, P., and Albrecht, B. A.: Multiyear Summertime Observations of Daytime Fair-Weather Cumuli at the ARM Southern Great Plains Facility, J. Climate, 26, 10031–10050, https://doi.org/10.1175/JCLI-D-12-00223.1, 2013. a
Delanoë, J. and Hogan, R. J.: Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds, J. Geophys. Res.-Atmos., 115, D00H29, https://doi.org/10.1029/2009JD012346, 2010. a, b, c
do Carmo, J. P., de Villele, G., Wallace, K., Lefebvre, A., Ghose, K., Kanitz, T., Chassat, F., Corselle, B., Belhadj, T., and Bravetti, P.: ATmospheric LIDar (ATLID): Pre-Launch Testing and Calibration of the European Space Agency Instrument That Will Measure Aerosols and Thin Clouds in the Atmosphere, Atmosphere, 12, 76, https://doi.org/10.3390/atmos12010076, 2021. a
Donovan, D. P., Klein Baltink, H., Henzing, J. S., de Roode, S. R., and Siebesma, A. P.: A depolarisation lidar-based method for the determination of liquid-cloud microphysical properties, Atmos. Meas. Tech., 8, 237–266, https://doi.org/10.5194/amt-8-237-2015, 2015. a
Fox, N. I. and Illingworth, A. J.: The Retrieval of Stratocumulus Cloud Properties by Ground-Based Cloud Radar, J. Appl. Meteorol., 36, 485–492, https://doi.org/10.1175/1520-0450(1997)036<0485:TROSCP>2.0.CO;2, 1997. a
Frisch, A. S., Fairall, C. W., and Snider, J. B.: Measurement of Stratus Cloud and Drizzle Parameters in ASTEX with a Kα-Band Doppler Radar and a Microwave Radiometer, J. Atmos. Sci., 52, 2788–2799, https://doi.org/10.1175/1520-0469(1995)052<2788:MOSCAD>2.0.CO;2, 1995. a
Geerts, B. and Dawei, Y.: Classification and Characterization of Tropical Precipitation Based on High-Resolution Airborne Vertical Incidence Radar. Part I: Classification, J. Appl. Meteorol., 43, 1554–1566, 2004. a
Hu, Y., Vaughan, M., Liu, Z., Lin, B., Yang, P., Flittner, D., Hunt, B., Kuehn, R., Huang, J., Wu, D., Rodier, S., Powell, K., Trepte, C., and Winker, D.: The depolarization–attenuated backscatter relation: CALIPSO lidar measurements vs. theory, Opt. Express, 15, 5327–5332, 2007. a
Hu, Y., Winker, D., Vaughan, M., Lin, B., Omar, A., Trepte, C., Flittner, D., Yang, P., Nasiri, S. L., Baum, B., Holz, R., Sun, W., Liu, Z., Wang, Z., Young, S., Stamnes, K., Huang, J., and Kuehn, R.: CALIPSO/CALIOP Cloud Phase Discrimination Algorithm, J. Atmos. Ocean. Tech., 26, 2293–2309, https://doi.org/10.1175/2009JTECHA1280.1, 2009. a
Huang, Y., Siems, S. T., Manton, M. J., Protat, A., and Delanoë, J.: A study on the low-altitude clouds over the Southern Ocean using the DARDAR-MASK, J. Geophys. Res.-Atmos., 117, D18204, https://doi.org/10.1029/2012JD017800, 2012. a
Illingworth, A. J., Hogan, R. J., O'Connor, E. J., Bouniol, D., Delanoë, J., Pelon, J., Protat, A., Brooks, M. E., Gaussiat, N., Wilson, D. R., Donovan, D. P., Baltink, H. K., van Zadelhoff, G.-J., Eastment, J. D., Goddard, J. W. F., Wrench, C. L., Haeffelin, M., Krasnov, O. A., Russchenberg, H. W. J., Piriou, J.-M., Vinit, F., Seifert, A., Tompkins, A. M., and Willén, U.: Cloudnet, B. Am. Meteorol. Soc., 88, 883–898, https://doi.org/10.1175/BAMS-88-6-883, 2007. a, b
Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H., Clerbaux, N., Cole, J., Delanoë, J., Domenech, C., Donovan, D. P., Fukuda, S., Hirakata, M., Hogan, R. J., Huenerbein, A., Kollias, P., Kubota, T., Nakajima, T., Nakajima, T. Y., Nishizawa, T., Ohno, Y., Okamoto, H., Oki, R., Sato, K., Satoh, M., Shephard, M. W., Velázquez-Blázquez, A., Wandinger, U., Wehr, T., and van Zadelhoff, G.-J.: The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation, B. Am. Meteorol. Soc., 96, 1311–1332, https://doi.org/10.1175/BAMS-D-12-00227.1, 2015. a, b, c
Kollias, P., Miller, M. A., Johnson, K. L., Jensen, M. P., and Troyan, D. T.: Cloud, thermodynamic, and precipitation observations in West Africa during 2006, J. Geophys. Res.-Atmos., 114, D00E08, https://doi.org/10.1029/2008JD010641, 2009. a
Kollias, P., Rémillard, J., Luke, E., and Szyrmer, W.: Cloud radar Doppler spectra in drizzling stratiform clouds: 1. Forward modeling and remote sensing applications, J. Geophys. Res.-Atmos., 116, D13201, https://doi.org/10.1029/2010JD015237, 2011. a
Kollias, P., Jo, I., Borque, P., Tatarevic, A., Lamer, K., Bharadwaj, N., Widener, K., Johnson, K., and Clothiaux, E. E.: Scanning ARM Cloud Radars. Part II: Data Quality Control and Processing, J. Atmos. Ocean. Tech., 31, 583–598, https://doi.org/10.1175/JTECH-D-13-00045.1, 2014. a
Kollias, P., Puidgomènech Treserras, B., Battaglia, A., Borque, P., and Tatarevic, A.: Processing reflectivity and Doppler velocity from EarthCARE’s cloud profiling radar: the C-FMR, C-CD and C-APC products, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1284, 2022. a, b
Krasnov, O. and Russchenberg, H.: A synergetic radar-lidar technique for the LWC retrieval in water clouds description and application to cloudnet data, in: The 11th Conference on Mesoscale Processes and the 32nd Conference on Radar Meteorology, Albuquerque, New Mexico, 23–29 October 2005, American Meteorology Society, 1–13, 2005. a
Lamer, K. and Kollias, P.: Observations of fair-weather cumuli over land: Dynamical factors controlling cloud size and cover, Geophys. Res. Lett., 42, 8693–8701, https://doi.org/10.1002/2015GL064534, 2015. a
Lebsock, M. D. and L'Ecuyer, T. S.: The retrieval of warm rain from CloudSat, J. Geophys. Res., 116, D20209, https://doi.org/10.1029/2011JD016076, 2011. a
Listowski, C., Delanoë, J., Kirchgaessner, A., Lachlan-Cope, T., and King, J.: Antarctic clouds, supercooled liquid water and mixed phase, investigated with DARDAR: geographical and seasonal variations, Atmos. Chem. Phys., 19, 6771–6808, https://doi.org/10.5194/acp-19-6771-2019, 2019. a, b
Listowski, C., Rojo, M., Claud, C., Delanoë, J., Rysman, J.-F., Cazenave, Q., and Noer, G.: New Insights Into the Vertical Structure of Clouds in Polar Lows, Using Radar-Lidar Satellite Observations, Geophys. Res. Lett., 47, e2020GL088785, https://doi.org/10.1029/2020GL088785, 2020. a, b
Liu, Y., Geerts, B., Miller, M., Daum, P., and McGraw, R.: Threshold radar reflectivity for drizzling clouds, Geophys. Res. Lett., 35, L03807, https://doi.org/10.1029/2007GL031201, 2008. a
Luke, E. P., Kollias, P., Johnson, K. L., and Clothiaux, E. E.: A Technique for the Automatic Detection of Insect Clutter in Cloud Radar Returns, J. Atmos. Ocean. Tech., 25, 1498–1513, https://doi.org/10.1175/2007JTECHA953.1, 2008. a
Mace, G. G. and Sassen, K.: A constrained algorithm for retrieval of stratocumulus cloud properties using solar radiation, microwave radiometer, and millimeter cloud radar data, J. Geophys. Res.-Atmos., 105, 29099–29108, https://doi.org/10.1029/2000JD900403, 2000. a, b
Mace, G. G. and Zhang, Q.: The CloudSat radar-lidar geometrical profile product (RL-GeoProf): Updates, improvements, and selected results, J. Geophys. Res.-Atmos., 119, 9441–9462, https://doi.org/10.1002/2013JD021374, 2014. a
Marchand, R., Mace, G. G., Ackerman, T., and Stephens, G.: Hydrometeor Detection Using Cloudsat—An Earth-Orbiting 94-GHz Cloud Radar, J. Atmos. Ocean. Tech., 25, 519–533, https://doi.org/10.1175/2007JTECHA1006.1, 2008. a
Mason, S., Jakob, C., Protat, A., and Delanoë, J.: Characterizing Observed Midtopped Cloud Regimes Associated with Southern Ocean Shortwave Radiation Biases, J. Climate, 27, 6189–6203, https://doi.org/10.1175/JCLI-D-14-00139.1, 2014. a
Mioche, G., Jourdan, O., Ceccaldi, M., and Delanoë, J.: Variability of mixed-phase clouds in the Arctic with a focus on the Svalbard region: a study based on spaceborne active remote sensing, Atmos. Chem. Phys., 15, 2445–2461, https://doi.org/10.5194/acp-15-2445-2015, 2015. a, b
Mioche, G., Jourdan, O., Delanoë, J., Gourbeyre, C., Febvre, G., Dupuy, R., Monier, M., Szczap, F., Schwarzenboeck, A., and Gayet, J.-F.: Vertical distribution of microphysical properties of Arctic springtime low-level mixed-phase clouds over the Greenland and Norwegian seas, Atmos. Chem. Phys., 17, 12845–12869, https://doi.org/10.5194/acp-17-12845-2017, 2017. 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, Geophys. Res. Lett., 42, 6502–6509, https://doi.org/10.1002/2015GL064604, 2015. a
Qu, Z., Donovan, D. P., Barker, H. W., Cole, J. N. S., Shephard, M. W., and Huijnen, V.: Numerical Model Generation of Test Frames for Pre-launch Studies of EarthCARE's Retrieval Algorithms and Data Management System, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2022-300, in review, 2022. a, b, c, d
Stephens, G., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C., L’Ecuyer, T., and Lebsock, M.: CloudSat and CALIPSO within the A-Train: Ten Years of Actively Observing the Earth System, B. Am. Meteorol. Soc., 99, 569–581, https://doi.org/10.1175/BAMS-D-16-0324.1, 2018. a
Stephens, G. G. L., Vane, D. D. G., Tanelli, S., Im, E., Durden, S., Rokey, M., Reinke, D., Partain, P., Mace, G. G., Austin, R., L'Ecuyer, T., Haynes, J., Lebsock, M., Suzuki, K., Waliser, D., Wu, D., Kay, J., Gettelman, A., Wang, Z., and Marchand, R.: CloudSat mission: Performance and early science after the first year of operation, J. Geophys. Res., 113, D00A18, https://doi.org/10.1029/2008JD009982, 2008. a
van Zadelhoff, G.-J., Barker, H. W., Baudrez, E., Bley, S., Clerbaux, N., Cole, J. N. S., de Kloe, J., Docter, N., Domenech, C., Donovan, D. P., Dufresne, J.-L., Eisinger, M., Fischer, J., García-Marañón, R., Haarig, M., Hogan, R. J., Hünerbein, A., Kollias, P., Koopman, R., Madenach, N., Mason, S. L., Preusker, R., Puigdomènech Treserras, B., Qu, Z., Ruiz-Saldaña, M., Shephard, M., Velázquez-Blazquez, A., Villefranque, N., Wandinger, U., Wang, P., and Wehr, T.: EarthCARE level-2 demonstration products from simulated scenes, Version 10.10, Zenodo [data set], https://doi.org/10.5281/zenodo.7728948, 2023a. a
van Zadelhoff, G.-J., Donovan, D. P., and Wang, P.: Detection of aerosol and cloud features for the EarthCARE lidar ATLID: the A-FM product, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-145, 2023b. a, b
Vérèmes, H., Listowski, C., Delanoë, J., Barthe, C., Tulet, P., Bonnardot, F., and Roy, D.: Spatial and seasonal variability of clouds over the southwest Indian Ocean based on the DARDAR mask product, Q. J. Roy. Meteor. Soc., 145, 3561–3576, https://doi.org/10.1002/qj.3640, 2019.
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Wandinger, U., Floutsi, A. A., Baars, H., Haarig, M., Ansmann, A., Hünerbein, A., Docter, N., Donovan, D., van Zadelhoff, G.-J., Mason, S., and Cole, J.: HETEAC – the Hybrid End-To-End Aerosol Classification model for EarthCARE, Atmos. Meas. Tech., 16, 2485–2510, https://doi.org/10.5194/amt-16-2485-2023, 2023. 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, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1476, 2023. a, b, c
Winker, D. M., Pelon, J., Coakley, J. A., Ackerman, S. A., Charlson, R. J., Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T. L.,
Le Treut, H., Mccormick, M. P., Mégie, G., Poole, L., Powell, K., Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO Mission, B. Am. Meteorol. Soc., 91, 1211–1230, https://doi.org/10.1175/2010BAMS3009.1, 2010. a
Zhu, Z., Kollias, P., Luke, E., and Yang, F.: New insights on the prevalence of drizzle in marine stratocumulus clouds based on a machine learning algorithm applied to radar Doppler spectra, Atmos. Chem. Phys., 22, 7405–7416, https://doi.org/10.5194/acp-22-7405-2022, 2022. a
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.
The Cloud Profiling Radar (CPR) and ATmospheric LIDar (ATLID) aboard the EarthCARE satellite are...
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