Articles | Volume 17, issue 9
https://doi.org/10.5194/amt-17-3011-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-3011-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 May 2024
Research article |
| 17 May 2024
| 17 May 2024
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
Valery Shcherbakov
CORRESPONDING AUTHOR
Université Clermont Auvergne, CNRS, UMR 6016, Laboratoire de Météorologie Physique, 63178 Aubière, France
Frédéric Szczap
Université Clermont Auvergne, CNRS, UMR 6016, Laboratoire de Météorologie Physique, 63178 Aubière, France
Guillaume Mioche
Université Clermont Auvergne, CNRS, UMR 6016, Laboratoire de Météorologie Physique, 63178 Aubière, France
Université Clermont Auvergne, Institut Universitaire de Technologie Clermont Auvergne – site de Montluçon, 03100 Montluçon, France
Céline Cornet
Université Lille, CNRS, UMR 8518 - LOA - Laboratoire d’Optique Atmosphérique, F-59000 Lille, France
Related authors
Valery Shcherbakov, Frédéric Szczap, Alaa Alkasem, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 15, 1729–1754, https://doi.org/10.5194/amt-15-1729-2022, https://doi.org/10.5194/amt-15-1729-2022, 2022
Short summary
Short summary
We performed extensive Monte Carlo (MC) simulations of lidar signals and developed an empirical model to account for the multiple scattering in the lidar signals. The simulations have taken into consideration four types of lidar configurations (the ground based, the airborne, the CALIOP, and the ATLID) and four types of particles (coarse aerosol, water cloud, jet-stream cirrus, and cirrus).
The empirical model has very good quality of MC data fitting for all considered cases.
Frédéric Szczap, Alaa Alkasem, Guillaume Mioche, Valery Shcherbakov, Céline Cornet, Julien Delanoë, Yahya Gour, Olivier Jourdan, Sandra Banson, and Edouard Bray
Atmos. Meas. Tech., 14, 199–221, https://doi.org/10.5194/amt-14-199-2021, https://doi.org/10.5194/amt-14-199-2021, 2021
Short summary
Short summary
Spaceborne lidar and radar are suitable tools to investigate cloud vertical properties on a global scale. This paper presents the McRALI code that provides simulations of lidar and radar signals from the EarthCARE mission. Regarding radar signals, cloud heterogeneity induces a severe bias in velocity estimates. Regarding lidar signals, multiple scattering is not negligible. Our results also give some insight into the reliability of lidar signal modeling using independent column approximation.
Manuel Moser, Christiane Voigt, Oliver Eppers, Johannes Lucke, Elena De La Torre Castro, Johanna Mayer, Regis Dupuy, Guillaume Mioche, Olivier Jourdan, Hans-Christian Clemen, Johannes Schneider, Philipp Joppe, Stephan Mertes, Bruno Wetzel, Stephan Borrmann, Marcus Klingebiel, Mario Mech, Christof Lüpkes, Susanne Crewell, André Ehrlich, Andreas Herber, and Manfred Wendisch
EGUsphere, https://doi.org/10.5194/egusphere-2025-3876, https://doi.org/10.5194/egusphere-2025-3876, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
In this study we analyzed Arctic mixed-phase clouds using airborne in-situ measurements in spring 2022. Based on microphysical properties, we show that within these clouds a distinction must be made between classic mixed-phase clouds and a mixed-phase haze regime. Instead of supercooled droplets, the haze regime contains large wet sea salt aerosols. These findings improve our understanding of Arctic low-level cloud processes.
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
Short summary
Short summary
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.
André Ehrlich, Susanne Crewell, Andreas Herber, Marcus Klingebiel, Christof Lüpkes, Mario Mech, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Matthias Buschmann, Hans-Christian Clemen, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Andreas Giez, Sarah Grawe, Christophe Gourbeyre, Jörg Hartmann, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsófia Jurányi, Benjamin Kirbus, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Christian Mallaun, Johanna Mayer, Stephan Mertes, Guillaume Mioche, Manuel Moser, Hanno Müller, Veronika Pörtge, Nils Risse, Greg Roberts, Sophie Rosenburg, Johannes Röttenbacher, Michael Schäfer, Jonas Schaefer, Andreas Schäfler, Imke Schirmacher, Johannes Schneider, Sabrina Schnitt, Frank Stratmann, Christian Tatzelt, Christiane Voigt, Andreas Walbröl, Anna Weber, Bruno Wetzel, Martin Wirth, and Manfred Wendisch
Earth Syst. Sci. Data, 17, 1295–1328, https://doi.org/10.5194/essd-17-1295-2025, https://doi.org/10.5194/essd-17-1295-2025, 2025
Short summary
Short summary
This paper provides an overview of the HALO–(AC)3 aircraft campaign data sets, the campaign-specific instrument operation, data processing, and data quality. The data set comprises in situ and remote sensing observations from three research aircraft: HALO, Polar 5, and Polar 6. All data are published in the PANGAEA database by instrument-separated data subsets. It is highlighted how the scientific analysis of the HALO–(AC)3 data benefits from the coordinated operation of three aircraft.
Raphaël Peroni, Guillaume Penide, Céline Cornet, Olivier Pujol, and Clémence Pierangelo
EGUsphere, https://doi.org/10.5194/egusphere-2025-787, https://doi.org/10.5194/egusphere-2025-787, 2025
Short summary
Short summary
A retrieval algorithm for integrated water vapor above clouds, based on shortwave infrared observations, is developed and evaluated using idealized and realistic atmospheric profiles. It aims to improve the understanding of interactions between water vapor and clouds to enhance weather models and LES. Integrated into the C3IEL mission (2028), it uses a Bayesian approach and demonstrates good accuracy, except for optically thin or low-altitude clouds.
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna E. Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona M. Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schaefer, Vera Schemann, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
Atmos. Chem. Phys., 24, 8865–8892, https://doi.org/10.5194/acp-24-8865-2024, https://doi.org/10.5194/acp-24-8865-2024, 2024
Short summary
Short summary
The Arctic is warming faster than the rest of the globe. Warm-air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold-air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to mid-latitude weather. In our article, we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented with state-of-the-art remote-sensing and in situ measurement devices.
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
Short summary
Short summary
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.
Raphaël Peroni, Céline Cornet, Olivier Pujol, Guillaume Penide, Clémence Pierangelo, and François Thieuleux
EGUsphere, https://doi.org/10.5194/egusphere-2024-1560, https://doi.org/10.5194/egusphere-2024-1560, 2024
Preprint withdrawn
Short summary
Short summary
A new retrieval algorithm to measure integrated water vapor content above clouds using shortwave infrared (SWIR) observations has been developed and evaluated through both idealized and realistic atmospheric profiles. For the latter, the algorithm shows a positive bias in retrieving water vapor content above low/mid-level clouds, with an error margin of about 2.6 kg.m-2.
Emma Järvinen, Franziska Nehlert, Guanglang Xu, Fritz Waitz, Guillaume Mioche, Regis Dupuy, Olivier Jourdan, and Martin Schnaiter
Atmos. Chem. Phys., 23, 7611–7633, https://doi.org/10.5194/acp-23-7611-2023, https://doi.org/10.5194/acp-23-7611-2023, 2023
Short summary
Short summary
The Arctic is warming faster than other regions. Arctic low-level mixed-phase clouds, where ice crystals and liquid droplets co-exist, are thought to have an important role in Arctic warming. Here we show airborne measurements of vertical distribution of liquid and ice particles and their relative abundance. Ice particles are found in relative warm clouds, which can be explained by multiplication of existing ice crystals. However, the role of ice particles in redistributing sun light is minimal.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
Short summary
Short summary
The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Manuel Moser, Christiane Voigt, Tina Jurkat-Witschas, Valerian Hahn, Guillaume Mioche, Olivier Jourdan, Régis Dupuy, Christophe Gourbeyre, Alfons Schwarzenboeck, Johannes Lucke, Yvonne Boose, Mario Mech, Stephan Borrmann, André Ehrlich, Andreas Herber, Christof Lüpkes, and Manfred Wendisch
Atmos. Chem. Phys., 23, 7257–7280, https://doi.org/10.5194/acp-23-7257-2023, https://doi.org/10.5194/acp-23-7257-2023, 2023
Short summary
Short summary
This study provides a comprehensive microphysical and thermodynamic phase analysis of low-level clouds in the northern Fram Strait, above the sea ice and the open ocean, during spring and summer. Using airborne in situ cloud data, we show that the properties of Arctic low-level clouds vary significantly with seasonal meteorological situations and surface conditions. The observations presented in this study can help one to assess the role of clouds in the Arctic climate system.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Marc Nicolas
Atmos. Meas. Tech., 16, 3221–3243, https://doi.org/10.5194/amt-16-3221-2023, https://doi.org/10.5194/amt-16-3221-2023, 2023
Short summary
Short summary
The optimal estimation formalism is applied to OSIRIS airborne high-resolution multi-angular measurements to retrieve COT and Reff. The corresponding uncertainties related to measurement errors, which are up to 6 and 12 %, the non-retrieved parameters, which are less than 0.5 %, and the cloud model assumptions show that the heterogeneous vertical profiles and the 3D radiative transfer effects lead to average uncertainties of 5 and 4 % for COT and 13 and 9 % for Reff.
Huazhe Shang, Souichiro Hioki, Guillaume Penide, Céline Cornet, Husi Letu, and Jérôme Riedi
Atmos. Chem. Phys., 23, 2729–2746, https://doi.org/10.5194/acp-23-2729-2023, https://doi.org/10.5194/acp-23-2729-2023, 2023
Short summary
Short summary
We find that cloud profiles can be divided into four prominent patterns, and the frequency of these four patterns is related to intensities of cloud-top entrainment and precipitation. Based on these analyses, we further propose a cloud profile parameterization scheme allowing us to represent these patterns. Our results shed light on how to facilitate the representation of cloud profiles and how to link them to cloud entrainment or precipitating status in future remote-sensing applications.
Paolo Dandini, Céline Cornet, Renaud Binet, Laetitia Fenouil, Vadim Holodovsky, Yoav Y. Schechner, Didier Ricard, and Daniel Rosenfeld
Atmos. Meas. Tech., 15, 6221–6242, https://doi.org/10.5194/amt-15-6221-2022, https://doi.org/10.5194/amt-15-6221-2022, 2022
Short summary
Short summary
3D cloud envelope and development velocity are retrieved from realistic simulations of multi-view
CLOUD (C3IEL) images. Cloud development velocity is derived by finding matching features
between acquisitions separated by 20 s. The tie points are then mapped from image to space via 3D
reconstruction of the cloud envelope obtained from 2 simultaneous images. The retrieved cloud
topography as well as the velocities are in good agreement with the estimates obtained from the
physical models.
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
Short summary
Short summary
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.
Valery Shcherbakov, Frédéric Szczap, Alaa Alkasem, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 15, 1729–1754, https://doi.org/10.5194/amt-15-1729-2022, https://doi.org/10.5194/amt-15-1729-2022, 2022
Short summary
Short summary
We performed extensive Monte Carlo (MC) simulations of lidar signals and developed an empirical model to account for the multiple scattering in the lidar signals. The simulations have taken into consideration four types of lidar configurations (the ground based, the airborne, the CALIOP, and the ATLID) and four types of particles (coarse aerosol, water cloud, jet-stream cirrus, and cirrus).
The empirical model has very good quality of MC data fitting for all considered cases.
Bjorn Stevens, Sandrine Bony, David Farrell, Felix Ament, Alan Blyth, Christopher Fairall, Johannes Karstensen, Patricia K. Quinn, Sabrina Speich, Claudia Acquistapace, Franziska Aemisegger, Anna Lea Albright, Hugo Bellenger, Eberhard Bodenschatz, Kathy-Ann Caesar, Rebecca Chewitt-Lucas, Gijs de Boer, Julien Delanoë, Leif Denby, Florian Ewald, Benjamin Fildier, Marvin Forde, Geet George, Silke Gross, Martin Hagen, Andrea Hausold, Karen J. Heywood, Lutz Hirsch, Marek Jacob, Friedhelm Jansen, Stefan Kinne, Daniel Klocke, Tobias Kölling, Heike Konow, Marie Lothon, Wiebke Mohr, Ann Kristin Naumann, Louise Nuijens, Léa Olivier, Robert Pincus, Mira Pöhlker, Gilles Reverdin, Gregory Roberts, Sabrina Schnitt, Hauke Schulz, A. Pier Siebesma, Claudia Christine Stephan, Peter Sullivan, Ludovic Touzé-Peiffer, Jessica Vial, Raphaela Vogel, Paquita Zuidema, Nicola Alexander, Lyndon Alves, Sophian Arixi, Hamish Asmath, Gholamhossein Bagheri, Katharina Baier, Adriana Bailey, Dariusz Baranowski, Alexandre Baron, Sébastien Barrau, Paul A. Barrett, Frédéric Batier, Andreas Behrendt, Arne Bendinger, Florent Beucher, Sebastien Bigorre, Edmund Blades, Peter Blossey, Olivier Bock, Steven Böing, Pierre Bosser, Denis Bourras, Pascale Bouruet-Aubertot, Keith Bower, Pierre Branellec, Hubert Branger, Michal Brennek, Alan Brewer, Pierre-Etienne Brilouet, Björn Brügmann, Stefan A. Buehler, Elmo Burke, Ralph Burton, Radiance Calmer, Jean-Christophe Canonici, Xavier Carton, Gregory Cato Jr., Jude Andre Charles, Patrick Chazette, Yanxu Chen, Michal T. Chilinski, Thomas Choularton, Patrick Chuang, Shamal Clarke, Hugh Coe, Céline Cornet, Pierre Coutris, Fleur Couvreux, Susanne Crewell, Timothy Cronin, Zhiqiang Cui, Yannis Cuypers, Alton Daley, Gillian M. Damerell, Thibaut Dauhut, Hartwig Deneke, Jean-Philippe Desbios, Steffen Dörner, Sebastian Donner, Vincent Douet, Kyla Drushka, Marina Dütsch, André Ehrlich, Kerry Emanuel, Alexandros Emmanouilidis, Jean-Claude Etienne, Sheryl Etienne-Leblanc, Ghislain Faure, Graham Feingold, Luca Ferrero, Andreas Fix, Cyrille Flamant, Piotr Jacek Flatau, Gregory R. Foltz, Linda Forster, Iulian Furtuna, Alan Gadian, Joseph Galewsky, Martin Gallagher, Peter Gallimore, Cassandra Gaston, Chelle Gentemann, Nicolas Geyskens, Andreas Giez, John Gollop, Isabelle Gouirand, Christophe Gourbeyre, Dörte de Graaf, Geiske E. de Groot, Robert Grosz, Johannes Güttler, Manuel Gutleben, Kashawn Hall, George Harris, Kevin C. Helfer, Dean Henze, Calvert Herbert, Bruna Holanda, Antonio Ibanez-Landeta, Janet Intrieri, Suneil Iyer, Fabrice Julien, Heike Kalesse, Jan Kazil, Alexander Kellman, Abiel T. Kidane, Ulrike Kirchner, Marcus Klingebiel, Mareike Körner, Leslie Ann Kremper, Jan Kretzschmar, Ovid Krüger, Wojciech Kumala, Armin Kurz, Pierre L'Hégaret, Matthieu Labaste, Tom Lachlan-Cope, Arlene Laing, Peter Landschützer, Theresa Lang, Diego Lange, Ingo Lange, Clément Laplace, Gauke Lavik, Rémi Laxenaire, Caroline Le Bihan, Mason Leandro, Nathalie Lefevre, Marius Lena, Donald Lenschow, Qiang Li, Gary Lloyd, Sebastian Los, Niccolò Losi, Oscar Lovell, Christopher Luneau, Przemyslaw Makuch, Szymon Malinowski, Gaston Manta, Eleni Marinou, Nicholas Marsden, Sebastien Masson, Nicolas Maury, Bernhard Mayer, Margarette Mayers-Als, Christophe Mazel, Wayne McGeary, James C. McWilliams, Mario Mech, Melina Mehlmann, Agostino Niyonkuru Meroni, Theresa Mieslinger, Andreas Minikin, Peter Minnett, Gregor Möller, Yanmichel Morfa Avalos, Caroline Muller, Ionela Musat, Anna Napoli, Almuth Neuberger, Christophe Noisel, David Noone, Freja Nordsiek, Jakub L. Nowak, Lothar Oswald, Douglas J. Parker, Carolyn Peck, Renaud Person, Miriam Philippi, Albert Plueddemann, Christopher Pöhlker, Veronika Pörtge, Ulrich Pöschl, Lawrence Pologne, Michał Posyniak, Marc Prange, Estefanía Quiñones Meléndez, Jule Radtke, Karim Ramage, Jens Reimann, Lionel Renault, Klaus Reus, Ashford Reyes, Joachim Ribbe, Maximilian Ringel, Markus Ritschel, Cesar B. Rocha, Nicolas Rochetin, Johannes Röttenbacher, Callum Rollo, Haley Royer, Pauline Sadoulet, Leo Saffin, Sanola Sandiford, Irina Sandu, Michael Schäfer, Vera Schemann, Imke Schirmacher, Oliver Schlenczek, Jerome Schmidt, Marcel Schröder, Alfons Schwarzenboeck, Andrea Sealy, Christoph J. Senff, Ilya Serikov, Samkeyat Shohan, Elizabeth Siddle, Alexander Smirnov, Florian Späth, Branden Spooner, M. Katharina Stolla, Wojciech Szkółka, Simon P. de Szoeke, Stéphane Tarot, Eleni Tetoni, Elizabeth Thompson, Jim Thomson, Lorenzo Tomassini, Julien Totems, Alma Anna Ubele, Leonie Villiger, Jan von Arx, Thomas Wagner, Andi Walther, Ben Webber, Manfred Wendisch, Shanice Whitehall, Anton Wiltshire, Allison A. Wing, Martin Wirth, Jonathan Wiskandt, Kevin Wolf, Ludwig Worbes, Ethan Wright, Volker Wulfmeyer, Shanea Young, Chidong Zhang, Dongxiao Zhang, Florian Ziemen, Tobias Zinner, and Martin Zöger
Earth Syst. Sci. Data, 13, 4067–4119, https://doi.org/10.5194/essd-13-4067-2021, https://doi.org/10.5194/essd-13-4067-2021, 2021
Short summary
Short summary
The EUREC4A field campaign, designed to test hypothesized mechanisms by which clouds respond to warming and benchmark next-generation Earth-system models, is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. It was the first campaign that attempted to characterize the full range of processes and scales influencing trade wind clouds.
Frédéric Szczap, Alaa Alkasem, Guillaume Mioche, Valery Shcherbakov, Céline Cornet, Julien Delanoë, Yahya Gour, Olivier Jourdan, Sandra Banson, and Edouard Bray
Atmos. Meas. Tech., 14, 199–221, https://doi.org/10.5194/amt-14-199-2021, https://doi.org/10.5194/amt-14-199-2021, 2021
Short summary
Short summary
Spaceborne lidar and radar are suitable tools to investigate cloud vertical properties on a global scale. This paper presents the McRALI code that provides simulations of lidar and radar signals from the EarthCARE mission. Regarding radar signals, cloud heterogeneity induces a severe bias in velocity estimates. Regarding lidar signals, multiple scattering is not negligible. Our results also give some insight into the reliability of lidar signal modeling using independent column approximation.
Cited articles
Alkasem, A., Szczap, F., Cornet, C., Shcherbakov, V., Gour, Y., Jourdan, O., Labonnote, L. C., and Mioche, G.: Effects of cirrus heterogeneity on lidar CALIOP/CALIPSO data, J. Quant. Spectrosc. Ra., 202, 38–49, https://doi.org/10.1016/j.jqsrt.2017.07.005, 2017.
Bicher, M., Wastian, M., Brunmeir, D., and Popper N.: Review on Monte Carlo simulation stopping rules: how many samples are really enough?, SNE Simul. Notes Eur., 32, 1–8, https://doi.org/10.11128/sne.32.on.10591, 2022.
Bissonnette, L. R.: Lidar and Multiple Scattering, in: Lidar, edited by: Weitkamp, C., Springer Series in Optical Sciences, Springer, New York, NY, Vol. 102, 43–103, https://doi.org/10.1007/0-387-25101-4_3, 2005.
Bissonnette, L. R., Bruscaglioni, P., Ismaelli, A., Zaccanti, G., Cohen, A., Benayahu, Y., Kleiman, M., Egert, S., Flesia, C., Schwendimann, P., Starkov, A. V., Noormohammadian, M., Oppel, U. G., Winker, D. M., Zege, E. P., Katsev, I. L., and Polonsky, I. N.: LIDAR multiple scattering from clouds, Appl. Phys. B., 60, 355–362, https://doi.org/10.1007/BF01082271, 1995.
Cornet, C., Labonnote, L. C., and Szczap, F.: Three-dimensional polarized Monte Carlo atmospheric radiative transfer model (3DMCPOL): 3D effects on polarized visible reflectances of a cirrus cloud, J. Quant. Spectrosc. Ra., 111, 174–186, https://doi.org/10.1016/j.jqsrt.2009.06.013, 2010.
Donovan, D. P.: The expected impact of multiple scattering on ATLID signals, EPJ Web of Conferences, 119, 01006, https://doi.org/10.1051/epjconf/201611901006, 2016.
Donovan, D. P. and Van Lammeren, A. C. A. P.: Cloud effective particle size and water content profile retrievals using combined lidar and radar observations: 1. Theory and examples, J. Geophys. Res., 106, 27425–27448, https://doi.org/10.1029/2001JD900243, 2001.
Eloranta, E.: Practical model for the calculation of multiply scattered lidar returns, Appl. Opt., 37, 2464–2472, https://doi.org/10.1364/AO.37.002464, 1998.
Eloranta, E. W.: A Practical Model for the Calculation of Multiply Scattered Lidar Returns-Errata and Extensions to Applied Optics paper, https://lidar.ssec.wisc.edu/multiple_scatter/AO_errata_16.pdf (last access: 22 April 2023), 2000.
Flesia, C. and Starkov, A. V.: Multiple scattering from clear atmosphere obscured by transparent crystal clouds in satellite-borne lidar sensing, Appl. Opt. 35, 2637–2641, https://doi.org/10.1364/AO.35.002637, 1996.
Freville, P., Montoux, N., Baray, J.-L., Chauvigné, A., Réveret, F., Hervo, M., Dionisi, D., Payen, G., and Sellegri, K.: LIDAR Developments at Clermont-Ferrand – France for Atmospheric Observation, Sensors, 15, 3041–3069, https://doi.org/10.3390/s150203041, 2015.
Garnier, A., Pelon, J., Vaughan, M. A., Winker, D. M., Trepte, C. R., and Dubuisson, P.: Lidar multiple scattering factors inferred from CALIPSO lidar and IIR retrievals of semi-transparent cirrus cloud optical depths over oceans, Atmos. Meas. Tech., 8, 2759–2774, https://doi.org/10.5194/amt-8-2759-2015, 2015.
Giannakaki, E., Balis, D. S., Amiridis, V., and Kazadzis, S.: Optical and geometrical characteristics of cirrus clouds over a Southern European lidar station, Atmos. Chem. Phys., 7, 5519–5530, https://doi.org/10.5194/acp-7-5519-2007, 2007.
Hogan, R. J.: Fast approximate calculation of multiply scattered lidar returns, Appl. Opt., 45, 5984–5992, https://doi.org/10.1364/AO.45.005984, 2006.
Hogan, R. J.: Fast lidar and radar multiple-scattering models: Part 1: Small-angle scattering using the photon variance-covariance method, J. Atmos. Sci., 65, 3621–3635, https://doi.org/10.1175/2008JAS2642.1, 2008.
JCGM 100:2008: Evaluation of measurement data – Guide to the expression of uncertainty in measurement, (GUM 1995 with minor corrections), Joint Committee for Guides in Metrology, https://www.bipm.org/documents/20126/2071204/JCGM_100_2008_E.pdf/cb0ef43f-baa5-11cf-3f85-4dcd86f77bd6 (last access: 13 May 2024), 2008.
Kalos, M. H. and Whitlock, P. A.: Monte Carlo Methods, Wiley-VCH Verlag GmbH & Co. KGaA, ISBN 9783527407606, 2008.
Marchuk, G. I., Mikhailov, G. A., Nazareliev, M., Darbinjan, R. A., Kargin, B. A., and Elepov, B. S.: The Monte Carlo methods in atmospheric optics, Springer-Verlag, Berlin, Heidelberg, Germany, https://doi.org/10.1007/978-3-540-35237-2, 2013.
Miller, S. D. and Stephens, G. L.: Multiple scattering effects in the lidar pulse stretching problem, J. Geophys. Res., 104, 22205–22219, https://doi.org/10.1029/1999JD900481, 1999.
Neto, F. D. M. and Neto, A. J. S.: An introduction to inverse problems with applications, Springer, Berlin Heidelberg, ISBN 978-3-642-32557-1, 2013.
Nakoudi, K., Stachlewska, I., and Ritter, C.: An extended lidar-based cirrus cloud retrieval scheme: first application over an Arctic site, Opt. Express, 29, 8553–8580, https://doi.org/10.1364/OE.414770, 2021.
NOAA: U.S. Standard Atmosphere: 1976, National Oceanographic and Atmospheric Administration, Washington, DC, USA, NOAA-S/T 76-1562, ASIN: B000MMZKDS, 1976.
Reverdy, M., Chepfer, H., Donovan, D., Noel, V., Cesana, G., Hoareau, C., Chiriaco, M., and Bastin, S.: An EarthCARE/ATLID simulator to evaluate cloud description in climate models, J. Geophys. Res.-Atmos., 120, 11090–11113, https://doi.org/10.1002/2015JD023919, 2015.
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding: Theory and Practice, World Scientific Pub. Co. Inc, ISBN 978-9810227401, 2000.
Rubino, G. and Tuffin, B. Introduction to Rare Event Simulation, in: Rare Event Simulation using Monte Carlo Methods, edited by: Rubino, G. and Tuffin, B., John Wiley & Sons, Ltd., ISBN 978-0-470-77269-0, 2009.
Seifert, P., Ansmann, A., Muller, D., Wandinger, U., Althausen, D., Heymsfield, A. J., Massie, S. T., and Schmitt, C.: Cirrus optical properties observed with lidar, radiosonde, and satellite over the tropical Indian Ocean during the aerosol-polluted northeast and clean maritime southwest monsoon, J. Geophys. Res., 112, D17205. https://doi.org/10.1029/2006JD008352, 2007.
Shcherbakov, V., Szczap, F., Alkasem, A., Mioche, G., and Cornet, C.: Empirical model of multiple-scattering effect on single-wavelength lidar data of aerosols and clouds, Atmos. Meas. Tech., 15, 1729–1754, https://doi.org/10.5194/amt-15-1729-2022, 2022.
Szczap, F., Alkasem, A., Mioche, G., Shcherbakov, V., Cornet, C., Delanoë, J., Gour, Y., Jourdan, O., Banson, S., and Bray, E.: McRALI: a Monte Carlo high-spectral-resolution lidar and Doppler radar simulator for three-dimensional cloudy atmosphere remote sensing, Atmos. Meas. Tech., 14, 199–221, https://doi.org/10.5194/amt-14-199-2021, 2021.
Van de Hulst, H. C.: Light scattering by small particles, Dover Publications Inc., New York, ISBN 9780486642284, 1981.
Vaughan, M. A., Powell, K. A., Winker, D. M., Hostetler, C. A., Kuehn, R. E., Hunt, W. H., Getzewich, B. J., Young, S. A., Liu, Z., and McGill, M. J.: Fully automated detection of cloud and aerosol layers in the CALIPSO lidar measurements, J. Atmos. Ocean. Tech., 26, 2034–2050, https://doi.org/10.1175/2009JTECHA1228.1, 2009.
Wang, Z, Zhang, J., and Gao, H.: Impacts of laser beam divergence on lidar multiple scattering polarization returns from water clouds, J. Quant. Spectrosc. Ra., 268, 107618, https://doi.org/10.1016/j.jqsrt.2021.107618, 2021.
Weinman, J. A.: Axially symmetric transfer of light through a cloud of anisotropically scattering particles, Icarus, 9, 67-73, https://doi.org/10.1016/0019-1035(68)90006-7, 1968.
Whiteman, D. N., Evans, K. D., Demoz, B., Starr, D. O'C., Eloranta, E. W., Tobin, D., Feltz, W., Jedlovec, G. J., Gutman, S. I., Schwemmer, G. K., Cadirola, M., Melfi, S. H., and Schmidlin, F. J.: Raman lidar measurements of water vapor and cirrus clouds during the passage of Hurricane Bonnie, J. Geophys. Res., 106, 5211–5225, https://doi.org/10.1029/2000JD900621, 2001.
Winker, D. M.: Accounting for multiple scattering in retrievals from space lidar, Proc. SPIE Int. Soc. Opt. Eng., 5059, 128–139, https://doi.org/10.1117/12.512352, 2003.
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt, W. H., and Young, S. A.: Overview of the CALIPSO mission and CALIOP data processing algorithms, J. Atmos. Ocean. Tech., 26, 2310–2323, https://doi.org/10.1175/2009JTECHA1281.1, 2009.
Yang, P. and Liou K. N.: Geometric-optics-integral-equation method for light scattering by nonspherical ice crystals, Appl. Opt., 35, 6568–84, https://doi.org/10.1364/AO.35.006568, 1996.
Young, S. A. and Vaughan, M. A.: The retrieval of profiles of particulate extinction from Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) data: Algorithm description, J. Atmos. Ocean. Tech., 26, 1105–1119, https://doi.org/10.1175/2008JTECHA1221.1, 2009.
Young, S. A., Vaughan, M. A., Kuehn, R. E., and Winker, D. M.: The Retrieval of Profiles of Particulate Extinction from Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Data: Uncertainty and Error Sensitivity Analyses, J. Atmos. Ocean. Tech., 30, 395–428, https://doi.org/10.1175/JTECH-D-12-00046.1, 2013.
Zhou, C. and Yang, P.: Backscattering peak of ice cloud particles, Opt. Express, 23, 11995–12003, https://doi.org/10.1364/OE.23.011995, 2015.
Short summary
We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered...
