Articles | Volume 17, issue 23
https://doi.org/10.5194/amt-17-6769-2024
© 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-17-6769-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Nov 2024
Research article |
| 29 Nov 2024
| 29 Nov 2024
Retrieval of cloud fraction and optical thickness of liquid water clouds over the ocean from multi-angle polarization observations
Meteorologisches Institut, Ludwig-Maximilians-Universität München, Munich, Germany
Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Germany
Veronika Pörtge
Meteorologisches Institut, Ludwig-Maximilians-Universität München, Munich, Germany
Mihail Manev
Meteorologisches Institut, Ludwig-Maximilians-Universität München, Munich, Germany
Bernhard Mayer
Meteorologisches Institut, Ludwig-Maximilians-Universität München, Munich, Germany
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Anna Weber, Gregor Köcher, and Bernhard Mayer
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A neural-network-based fast forward operator for polarized 3D radiative transfer is presented. The forward operator uses the new independent column local half-sphere approximation (IDEFAX). Polarized radiances simulated with IFEDAX and the forward operator were validated against full 3D radiative transfer simulations with MYSTIC and show a significantly improved representation of 3D radiative effects compared to the plane-parallel independent column approximation at comparable computation times.
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
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Lea Volkmer, Tobias Kölling, Tobias Zinner, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6807–6817, https://doi.org/10.5194/amt-17-6807-2024, https://doi.org/10.5194/amt-17-6807-2024, 2024
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Atmos. Meas. Tech., 17, 6025–6046, https://doi.org/10.5194/amt-17-6025-2024, https://doi.org/10.5194/amt-17-6025-2024, 2024
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Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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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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Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
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Ilias Fountoulakis, Alexandra Tsekeri, Stelios Kazadzis, Vassilis Amiridis, Angelos Nersesian, Maria Tsichla, Emmanouil Proestakis, Antonis Gkikas, Kyriakoula Papachristopoulou, Vasileios Barlakas, Claudia Emde, and Bernhard Mayer
Atmos. Chem. Phys., 24, 4915–4948, https://doi.org/10.5194/acp-24-4915-2024, https://doi.org/10.5194/acp-24-4915-2024, 2024
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In our study we provide an assessment, through a sensitivity study, of the limitations of models to calculate the dust direct radiative effect (DRE) due to the underrepresentation of its size, refractive index (RI), and shape. Our results indicate the necessity of including more realistic sizes and RIs for dust particles in dust models, in order to derive better estimations of the dust direct radiative effects.
Behrooz Keshtgar, Aiko Voigt, Bernhard Mayer, and Corinna Hoose
Atmos. Chem. Phys., 24, 4751–4769, https://doi.org/10.5194/acp-24-4751-2024, https://doi.org/10.5194/acp-24-4751-2024, 2024
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Cloud-radiative heating (CRH) affects extratropical cyclones but is uncertain in weather and climate models. We provide a framework to quantify uncertainties in CRH within an extratropical cyclone due to four factors and show that the parameterization of ice optical properties contributes significantly to uncertainty in CRH. We also argue that ice optical properties, by affecting CRH on spatial scales of 100 km, are relevant for the large-scale dynamics of extratropical cyclones.
Lea Volkmer, Veronika Pörtge, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 17, 1703–1719, https://doi.org/10.5194/amt-17-1703-2024, https://doi.org/10.5194/amt-17-1703-2024, 2024
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Three-dimensional radiative transfer simulations are used to evaluate the performance of retrieval algorithms in the derivation of cloud geometry (cloud top heights) and cloud droplet size distributions from two-dimensional polarized radiance measurements of the airborne spectrometer of the Munich Aerosol Cloud Scanner. The cloud droplet size distributions are derived for the effective radius and variance. The simulations are based on cloud data from highly resolved large-eddy simulations.
Anna Weber, Tobias Kölling, Veronika Pörtge, Andreas Baumgartner, Clemens Rammeloo, Tobias Zinner, and Bernhard Mayer
Atmos. Meas. Tech., 17, 1419–1439, https://doi.org/10.5194/amt-17-1419-2024, https://doi.org/10.5194/amt-17-1419-2024, 2024
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In this work, we introduce the 2D RGB polarization-resolving cameras of the airborne hyperspectral and polarized imaging system specMACS. A full characterization and calibration of the cameras including a geometric calibration as well as a radiometric characterization is provided, allowing for the computation of absolute calibrated, georeferenced Stokes vectors rotated into the scattering plane. We validate the calibration by comparing sunglint measurements to radiative transfer simulations.
James Barry, Stefanie Meilinger, Klaus Pfeilsticker, Anna Herman-Czezuch, Nicola Kimiaie, Christopher Schirrmeister, Rone Yousif, Tina Buchmann, Johannes Grabenstein, Hartwig Deneke, Jonas Witthuhn, Claudia Emde, Felix Gödde, Bernhard Mayer, Leonhard Scheck, Marion Schroedter-Homscheidt, Philipp Hofbauer, and Matthias Struck
Atmos. Meas. Tech., 16, 4975–5007, https://doi.org/10.5194/amt-16-4975-2023, https://doi.org/10.5194/amt-16-4975-2023, 2023
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Measured power data from solar photovoltaic (PV) systems contain information about the state of the atmosphere. In this work, power data from PV systems in the Allgäu region in Germany were used to determine the solar irradiance at each location, using state-of-the-art simulation and modelling. The results were validated using concurrent measurements of the incoming solar radiation in each case. If applied on a wider scale, this algorithm could help improve weather and climate models.
Philipp Gregor, Tobias Zinner, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 16, 3257–3271, https://doi.org/10.5194/amt-16-3257-2023, https://doi.org/10.5194/amt-16-3257-2023, 2023
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This work introduces MACIN, a model for short-term forecasting of direct irradiance for solar energy applications. MACIN exploits cloud images of multiple cameras to predict irradiance. The model is applied to artificial images of clouds from a weather model. The artificial cloud data allow for a more in-depth evaluation and attribution of errors compared with real data. Good performance of derived cloud information and significant forecast improvements over a baseline forecast were found.
Veronika Pörtge, Tobias Kölling, Anna Weber, Lea Volkmer, Claudia Emde, Tobias Zinner, Linda Forster, and Bernhard Mayer
Atmos. Meas. Tech., 16, 645–667, https://doi.org/10.5194/amt-16-645-2023, https://doi.org/10.5194/amt-16-645-2023, 2023
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In this work, we analyze polarized cloudbow observations by the airborne camera system specMACS to retrieve the cloud droplet size distribution defined by the effective radius (reff) and the effective variance (veff). Two case studies of trade-wind cumulus clouds observed during the EUREC4A field campaign are presented. The results are combined into maps of reff and veff with a very high spatial resolution (100 m × 100 m) that allow new insights into cloud microphysics.
Behrooz Keshtgar, Aiko Voigt, Corinna Hoose, Michael Riemer, and Bernhard Mayer
Weather Clim. Dynam., 4, 115–132, https://doi.org/10.5194/wcd-4-115-2023, https://doi.org/10.5194/wcd-4-115-2023, 2023
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Forecasting extratropical cyclones is challenging due to many physical factors influencing their behavior. One such factor is the impact of heating and cooling of the atmosphere by the interaction between clouds and radiation. In this study, we show that cloud-radiative heating (CRH) increases the intensity of an idealized cyclone and affects its predictability. We find that CRH affects the cyclone mostly via increasing latent heat release and subsequent changes in the synoptic circulation.
Linda Forster and Bernhard Mayer
Atmos. Chem. Phys., 22, 15179–15205, https://doi.org/10.5194/acp-22-15179-2022, https://doi.org/10.5194/acp-22-15179-2022, 2022
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We present a novel retrieval using ground-based imaging observations of halo displays together with radiative transfer simulations to help improve our understanding of ice crystal properties representative of cirrus clouds. Analysis of 4400 calibrated HaloCam images featuring a 22° halo revealed aggregates of hexagonal columns of 20 µm effective radius with a mixture of about 37 % smooth and 63% severely roughened surfaces as the best match in general.
Huan Yu, Claudia Emde, Arve Kylling, Ben Veihelmann, Bernhard Mayer, Kerstin Stebel, and Michel Van Roozendael
Atmos. Meas. Tech., 15, 5743–5768, https://doi.org/10.5194/amt-15-5743-2022, https://doi.org/10.5194/amt-15-5743-2022, 2022
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In this study, we have investigated the impact of 3D clouds on the tropospheric NO2 retrieval from UV–visible sensors. We applied standard NO2 retrieval methods including cloud corrections to synthetic data generated by the 3D radiative transfer model. A sensitivity study was done for synthetic data, and dependencies on various parameters were investigated. Possible mitigation strategies were investigated and compared based on 3D simulations and observed data.
Arve Kylling, Claudia Emde, Huan Yu, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and Bernhard Mayer
Atmos. Meas. Tech., 15, 3481–3495, https://doi.org/10.5194/amt-15-3481-2022, https://doi.org/10.5194/amt-15-3481-2022, 2022
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Atmospheric trace gases such as nitrogen dioxide (NO2) may be measured by satellite instruments sensitive to solar ultraviolet–visible radiation reflected from Earth and its atmosphere. For a single pixel, clouds in neighbouring pixels may affect the radiation and hence the retrieved trace gas amount. We found that for a solar zenith angle less than about 40° this cloud-related NO2 bias is typically below 10 %, while for larger solar zenith angles the NO2 bias is on the order of tens of percent.
Luca Bugliaro, Dennis Piontek, Stephan Kox, Marius Schmidl, Bernhard Mayer, Richard Müller, Margarita Vázquez-Navarro, Daniel M. Peters, Roy G. Grainger, Josef Gasteiger, and Jayanta Kar
Nat. Hazards Earth Syst. Sci., 22, 1029–1054, https://doi.org/10.5194/nhess-22-1029-2022, https://doi.org/10.5194/nhess-22-1029-2022, 2022
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The monitoring of ash dispersion in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. We present an AI-based method that retrieves the spatial extension and properties of volcanic ash clouds with high temporal resolution during day and night by means of geostationary satellite measurements. This algorithm, trained on realistic observations simulated with a radiative transfer model, runs operationally at the German Weather Service.
Claudia Emde, Huan Yu, Arve Kylling, Michel van Roozendael, Kerstin Stebel, Ben Veihelmann, and Bernhard Mayer
Atmos. Meas. Tech., 15, 1587–1608, https://doi.org/10.5194/amt-15-1587-2022, https://doi.org/10.5194/amt-15-1587-2022, 2022
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Retrievals of trace gas concentrations from satellite observations can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. We used a Monte Carlo radiative transfer model to generate synthetic satellite observations, which we used to test retrieval algorithms and to quantify the error of retrieved NO2 vertical column density due to cloud scattering.
Heike Konow, Florian Ewald, Geet George, Marek Jacob, Marcus Klingebiel, Tobias Kölling, Anna E. Luebke, Theresa Mieslinger, Veronika Pörtge, Jule Radtke, Michael Schäfer, Hauke Schulz, Raphaela Vogel, Martin Wirth, Sandrine Bony, Susanne Crewell, André Ehrlich, Linda Forster, Andreas Giez, Felix Gödde, Silke Groß, Manuel Gutleben, Martin Hagen, Lutz Hirsch, Friedhelm Jansen, Theresa Lang, Bernhard Mayer, Mario Mech, Marc Prange, Sabrina Schnitt, Jessica Vial, Andreas Walbröl, Manfred Wendisch, Kevin Wolf, Tobias Zinner, Martin Zöger, Felix Ament, and Bjorn Stevens
Earth Syst. Sci. Data, 13, 5545–5563, https://doi.org/10.5194/essd-13-5545-2021, https://doi.org/10.5194/essd-13-5545-2021, 2021
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The German research aircraft HALO took part in the research campaign EUREC4A in January and February 2020. The focus area was the tropical Atlantic east of the island of Barbados. We describe the characteristics of the 15 research flights, provide auxiliary information, derive combined cloud mask products from all instruments that observe clouds on board the aircraft, and provide code examples that help new users of the data to get started.
Marc Schwaerzel, Dominik Brunner, Fabian Jakub, Claudia Emde, Brigitte Buchmann, Alexis Berne, and Gerrit Kuhlmann
Atmos. Meas. Tech., 14, 6469–6482, https://doi.org/10.5194/amt-14-6469-2021, https://doi.org/10.5194/amt-14-6469-2021, 2021
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NO2 maps from airborne imaging remote sensing often appear much smoother than one would expect from high-resolution model simulations of NO2 over cities, despite the small ground-pixel size of the sensors. Our case study over Zurich, using the newly implemented building module of the MYSTIC radiative transfer solver, shows that the 3D effect can explain part of the smearing and that building shadows cause a noticeable underestimation and noise in the measured NO2 columns.
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
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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.
Florian Ewald, Silke Groß, Martin Wirth, Julien Delanoë, Stuart Fox, and Bernhard Mayer
Atmos. Meas. Tech., 14, 5029–5047, https://doi.org/10.5194/amt-14-5029-2021, https://doi.org/10.5194/amt-14-5029-2021, 2021
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In this study, we show how solar radiance observations can be used to validate and further constrain ice cloud microphysics retrieved from the synergy of radar–lidar measurements. Since most radar–lidar retrievals rely on a global assumption about the ice particle shape, ice water content and particle size biases are to be expected in individual cloud regimes. In this work, we identify and correct these biases by reconciling simulated and measured solar radiation reflected from these clouds.
Nina Črnivec and Bernhard Mayer
Geosci. Model Dev., 14, 3663–3682, https://doi.org/10.5194/gmd-14-3663-2021, https://doi.org/10.5194/gmd-14-3663-2021, 2021
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This study aims to advance the cloud–radiation interplay treatment in global weather and climate prediction, focusing on cloud horizontal inhomogeneity misrepresentation. We explore the potential of the Tripleclouds method for diverse cloud types, namely the stratocumulus, cirrus and cumulonimbus. The validity of global cloud variability estimate with various condensate distribution assumptions is assessed. Optimizations for overcast and extremely heterogeneous cloudiness are further endorsed.
Daniel Zawada, Ghislain Franssens, Robert Loughman, Antti Mikkonen, Alexei Rozanov, Claudia Emde, Adam Bourassa, Seth Dueck, Hannakaisa Lindqvist, Didier Ramon, Vladimir Rozanov, Emmanuel Dekemper, Erkki Kyrölä, John P. Burrows, Didier Fussen, and Doug Degenstein
Atmos. Meas. Tech., 14, 3953–3972, https://doi.org/10.5194/amt-14-3953-2021, https://doi.org/10.5194/amt-14-3953-2021, 2021
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Satellite measurements of atmospheric composition often rely on computer tools known as radiative transfer models to model the propagation of sunlight within the atmosphere. Here we have performed a detailed inter-comparison of seven different radiative transfer models in a variety of conditions. We have found that the models agree remarkably well, at a level better than previously reported. This result provides confidence in our understanding of atmospheric radiative transfer.
Manuel Gutleben, Silke Groß, Martin Wirth, and Bernhard Mayer
Atmos. Chem. Phys., 20, 12313–12327, https://doi.org/10.5194/acp-20-12313-2020, https://doi.org/10.5194/acp-20-12313-2020, 2020
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Airborne lidar measurements in the vicinity of Barbados are used to investigate radiative effects of long-range-transported Saharan air layers. Derived atmospheric heating rates indicate that observed enhanced water vapor concentrations inside these layers are the main drivers for dust vertical mixing inside the layers. Additionally, they may play a major role for the suppression of subjacent convective cloud development.
Nina Črnivec and Bernhard Mayer
Atmos. Chem. Phys., 20, 10733–10755, https://doi.org/10.5194/acp-20-10733-2020, https://doi.org/10.5194/acp-20-10733-2020, 2020
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Unresolved interaction between clouds and atmospheric radiation is a source of uncertainty in weather and climate models. The present study highlights the potential of the state-of-the-art Tripleclouds radiative solver for shallow cumulus clouds, exposing the significance of properly representing subgrid cloud horizontal heterogeneity. The Tripleclouds concept was thereby incorporated in the widely employed δ-Eddington two-stream radiation scheme within the comprehensive libRadtran library.
Marc Schwaerzel, Claudia Emde, Dominik Brunner, Randulph Morales, Thomas Wagner, Alexis Berne, Brigitte Buchmann, and Gerrit Kuhlmann
Atmos. Meas. Tech., 13, 4277–4293, https://doi.org/10.5194/amt-13-4277-2020, https://doi.org/10.5194/amt-13-4277-2020, 2020
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Horizontal homogeneity is often assumed for trace gases remote sensing, although it is not valid where trace gas concentrations have high spatial variability, e.g., in cities. We show the importance of 3D effects for MAX-DOAS and airborne imaging spectrometers using 3D-box air mass factors implemented in the MYSTIC radiative transfer solver. In both cases, 3D information is invaluable for interpreting the measurements, as not considering 3D effects can lead to misinterpretation of measurements.
Linda Forster, Meinhard Seefeldner, Andreas Baumgartner, Tobias Kölling, and Bernhard Mayer
Atmos. Meas. Tech., 13, 3977–3991, https://doi.org/10.5194/amt-13-3977-2020, https://doi.org/10.5194/amt-13-3977-2020, 2020
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We present a procedure for both the geometric and absolute radiometric characterization of the weather-proof RGB camera HaloCamRAW, which is part of our automated halo observation system HaloCam, designed for the quantitative analysis of halo displays. By comparing the calibrated HaloCamRAW radiances of a 22° halo scene with radiative transfer simulations, we demonstrate the potential of developing a retrieval method for ice crystal properties, such as size, shape, and surface roughness.
Lucas Höppler, Felix Gödde, Manuel Gutleben, Tobias Kölling, Bernhard Mayer, and Tobias Zinner
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2020-49, https://doi.org/10.5194/amt-2020-49, 2020
Publication in AMT not foreseen
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Clouds are considered as two-dimensional in many climate and weather models. This approach creates errors due to wrongly calculated solar or terrestrial radiative transfer. In order to reduce these errors, realistic three-dimensional clouds need to be retrieved or reproduced. This paper shows an approach to retrieve realistic three-dimensional clouds from an airplane, by combining the strengths of several active and passive remote sensing instruments.
Paul Ockenfuß, Claudia Emde, Bernhard Mayer, and Germar Bernhard
Atmos. Chem. Phys., 20, 1961–1976, https://doi.org/10.5194/acp-20-1961-2020, https://doi.org/10.5194/acp-20-1961-2020, 2020
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We model solar radiation as it would be measured on the Earth's surface in the core shadow of a total solar eclipse. Subsequently, we compare our results to observations during the total eclipse 2017 for ultraviolet, visible and near-infrared wavelengths. Moreover, we analyze the effect of the surface reflectance, the ozone profile, aerosol and the topography and give a visualization of the prevailing photons paths in the atmosphere during the eclipse.
Pascal Polonik, Christoph Knote, Tobias Zinner, Florian Ewald, Tobias Kölling, Bernhard Mayer, Meinrat O. Andreae, Tina Jurkat-Witschas, Thomas Klimach, Christoph Mahnke, Sergej Molleker, Christopher Pöhlker, Mira L. Pöhlker, Ulrich Pöschl, Daniel Rosenfeld, Christiane Voigt, Ralf Weigel, and Manfred Wendisch
Atmos. Chem. Phys., 20, 1591–1605, https://doi.org/10.5194/acp-20-1591-2020, https://doi.org/10.5194/acp-20-1591-2020, 2020
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A realistic representation of cloud–aerosol interactions is central to accurate climate projections. Here we combine observations collected during the ACRIDICON-CHUVA campaign with chemistry-transport simulations to evaluate the model’s ability to represent the indirect effects of biomass burning aerosol on cloud microphysics. We find an upper limit for the model sensitivity on cloud condensation nuclei concentrations well below the levels reached during the burning season in the Amazon Basin.
Hans Grob, Claudia Emde, Matthias Wiegner, Meinhard Seefeldner, Linda Forster, and Bernhard Mayer
Atmos. Meas. Tech., 13, 239–258, https://doi.org/10.5194/amt-13-239-2020, https://doi.org/10.5194/amt-13-239-2020, 2020
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Polarimetry has been established as an enhancement to classical photometry in aerosol remote sensing over the past years. We propose a fast and exact radiometric and polarimetric calibration method for polarized photometers. Additionally, a technique for correcting an alt-azimuthal mount is introduced.
These methods are applied to measurements obtained with our SSARA instrument during the A-LIFE field campaign. For 2 d, the data are subjected to an inversion of aerosol optical properties.
Nina Črnivec and Bernhard Mayer
Atmos. Chem. Phys., 19, 8083–8100, https://doi.org/10.5194/acp-19-8083-2019, https://doi.org/10.5194/acp-19-8083-2019, 2019
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The interaction between radiation and clouds represents a source of uncertainty in numerical weather prediction (NWP), due to both intrinsic problems of one-dimensional radiation schemes and poor representation of clouds. The underlying question addressed in this study is how large the bias is of radiative heating rates in NWP models for shallow cumulus clouds and how it scales with various parameters, such as solar zenith angle, surface albedo, cloud cover and liquid water path.
Tobias Zinner, Ulrich Schwarz, Tobias Kölling, Florian Ewald, Evelyn Jäkel, Bernhard Mayer, and Manfred Wendisch
Atmos. Meas. Tech., 12, 1167–1181, https://doi.org/10.5194/amt-12-1167-2019, https://doi.org/10.5194/amt-12-1167-2019, 2019
Florian Ewald, Tobias Zinner, Tobias Kölling, and Bernhard Mayer
Atmos. Meas. Tech., 12, 1183–1206, https://doi.org/10.5194/amt-12-1183-2019, https://doi.org/10.5194/amt-12-1183-2019, 2019
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This paper presents a new method for gaining insights into the vertical evolution of cloud droplet effective radii by using reflected solar radiation from cloud sides. The paper investigates how bi-spectral effective radius retrievals are affected by unknown cloud surface orientations and presents a method to mitigate this effect. Based on these findings, this study develops a statistical effective radius retrieval for airborne, side-looking imaging sensors.
Tobias Kölling, Tobias Zinner, and Bernhard Mayer
Atmos. Meas. Tech., 12, 1155–1166, https://doi.org/10.5194/amt-12-1155-2019, https://doi.org/10.5194/amt-12-1155-2019, 2019
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Imaging technology allows us to quickly gather information on larger cloud fields. Unlike using lidar or radar, it is difficult to obtain accurate position information about the observed clouds. This work presents a method to retrieve the missing position information using RGB images from an airborne video camera. Using field campaign data, we observe and explain a median offset of 126 m compared to lidar data and show that systematic errors across the measurement swath are well below 50 m.
Ulrich Schumann and Bernhard Mayer
Atmos. Chem. Phys., 17, 13833–13848, https://doi.org/10.5194/acp-17-13833-2017, https://doi.org/10.5194/acp-17-13833-2017, 2017
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It is generally assumed that a positive radiative forcing of the atmosphere implies a warming of the Earth surface. This assumption is valid for well-mixed greenhouse gases but is not guaranteed for disturbances which cause a vertically variable radiative heating rate profile with warming in the upper troposphere and cooling near the surface. This conceptual study shows that the warming induced by contrail cirrus prevails only for fast vertical heat exchange by mixing within the troposphere.
Fabian Jakub and Bernhard Mayer
Atmos. Chem. Phys., 17, 13317–13327, https://doi.org/10.5194/acp-17-13317-2017, https://doi.org/10.5194/acp-17-13317-2017, 2017
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The formation of shallow cumulus cloud streets was historically attributed primarily to dynamics. Here, we focus on the interaction between radiatively induced surface heterogeneities and the resulting patterns in the flow. Our results suggest that solar radiative heating has the potential to organize clouds perpendicular to the sun's incidence angle.
Linda Forster, Meinhard Seefeldner, Matthias Wiegner, and Bernhard Mayer
Atmos. Meas. Tech., 10, 2499–2516, https://doi.org/10.5194/amt-10-2499-2017, https://doi.org/10.5194/amt-10-2499-2017, 2017
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Halo displays are produced by scattering of sunlight by smooth, hexagonal ice crystals. Consequently, the presence of a halo should contain information on particle shape. This study presents HaloCam, a novel sun-tracking camera system, and an automated detection algorithm to collect and evaluate long-term halo observations. Two-year HaloCam observations revealed that about 25 % of the detected cirrus clouds occurred together with a 22° halo indicating the presence of smooth, hexagonal crystals.
Carolin Klinger, Bernhard Mayer, Fabian Jakub, Tobias Zinner, Seung-Bu Park, and Pierre Gentine
Atmos. Chem. Phys., 17, 5477–5500, https://doi.org/10.5194/acp-17-5477-2017, https://doi.org/10.5194/acp-17-5477-2017, 2017
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Radiation is driving weather and climate. Yet, the effect of radiation on clouds is not fully understood and often only poorly represented in models. Better understanding and better parameterizations of the radiation–cloud interaction are therefore essential. Using our newly developed fast
neighboring column approximationfor 3-D thermal heating and cooling rates, we show that thermal radiation changes cloud circulation and causes organization and a deepening of the clouds.
Tobias Zinner, Petra Hausmann, Florian Ewald, Luca Bugliaro, Claudia Emde, and Bernhard Mayer
Atmos. Meas. Tech., 9, 4615–4632, https://doi.org/10.5194/amt-9-4615-2016, https://doi.org/10.5194/amt-9-4615-2016, 2016
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A new retrieval of optical thickness and effective particle size of ice clouds over a wide range of optical thickness from transmittance measurements is presented. A visible range spectral slope is used to resolve the transmittance optical thickness ambiguity. Retrieval sensitivity to ice crystal habit, aerosol, albedo, sensor accuracy and lookup table interpolation is presented as well as an application of the method and comparison to satellite products for 2 days.
Florian Ewald, Tobias Kölling, Andreas Baumgartner, Tobias Zinner, and Bernhard Mayer
Atmos. Meas. Tech., 9, 2015–2042, https://doi.org/10.5194/amt-9-2015-2016, https://doi.org/10.5194/amt-9-2015-2016, 2016
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The new spectrometer of the Munich Aerosol Cloud Scanner (specMACS) is a
multipurpose hyperspectral cloud and sky imager which is designated, but not limited, to investigations of cloud-aerosol interactions in Earth's atmosphere. This paper describes the specMACS instrument's hardware and software design and
characterizes the instrument performance. Initial measurements of cloud sides are presented which demonstrate the wide applicability of the instrument.
Claudia Emde, Robert Buras-Schnell, Arve Kylling, Bernhard Mayer, Josef Gasteiger, Ulrich Hamann, Jonas Kylling, Bettina Richter, Christian Pause, Timothy Dowling, and Luca Bugliaro
Geosci. Model Dev., 9, 1647–1672, https://doi.org/10.5194/gmd-9-1647-2016, https://doi.org/10.5194/gmd-9-1647-2016, 2016
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libradtran is a widely used software package for radiative transfer calculations. It allows one to compute (polarized) radiances, irradiance, and actinic fluxes in the solar and thermal spectral regions. This paper gives an overview of libradtran version 2.0 with focus on new features (e.g. polarization, Raman scattering, absorption parameterization, cloud and aerosol optical properties). libRadtran is freely available at http://www.libradtran.org.
Fabian Jakub and Bernhard Mayer
Geosci. Model Dev., 9, 1413–1422, https://doi.org/10.5194/gmd-9-1413-2016, https://doi.org/10.5194/gmd-9-1413-2016, 2016
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Radiative heating or cooling plays a vital role in the evolution and lifecycle of clouds. Due to the immense computational cost of 3-D radiative transfer, today's atmospheric models usually employ crude 1-D approximations which neglect any horizontal energy transport whatsoever and may introduce non-negligible errors. This paper documents the implementation and runtime characteristics of the new TenStream solver that enables us to study 3-D effects on large domains and extended periods of time.
N. Hanrieder, S. Wilbert, R. Pitz-Paal, C. Emde, J. Gasteiger, B. Mayer, and J. Polo
Atmos. Meas. Tech., 8, 3467–3480, https://doi.org/10.5194/amt-8-3467-2015, https://doi.org/10.5194/amt-8-3467-2015, 2015
A. Kylling, N. Kristiansen, A. Stohl, R. Buras-Schnell, C. Emde, and J. Gasteiger
Atmos. Meas. Tech., 8, 1935–1949, https://doi.org/10.5194/amt-8-1935-2015, https://doi.org/10.5194/amt-8-1935-2015, 2015
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Water and ice clouds affect detection and retrieval of volcanic ash clouds by satellite instruments. Synthetic infrared satellite images were generated for the Eyjafjallajokull 2010 and Grimsvotn 2011 eruptions by combining weather forecast, ash transport and radiative transfer modelling. Clouds decreased the number of pixels identified as ash and generally increased the retrieved ash-mass loading compared to the cloudless case; however, large differences were seen between scenes.
A. Kreuter, R. Buras, B. Mayer, A. Webb, R. Kift, A. Bais, N. Kouremeti, and M. Blumthaler
Atmos. Chem. Phys., 14, 5989–6002, https://doi.org/10.5194/acp-14-5989-2014, https://doi.org/10.5194/acp-14-5989-2014, 2014
B. Reinhardt, R. Buras, L. Bugliaro, S. Wilbert, and B. Mayer
Atmos. Meas. Tech., 7, 823–838, https://doi.org/10.5194/amt-7-823-2014, https://doi.org/10.5194/amt-7-823-2014, 2014
M. von Hobe, S. Bekki, S. Borrmann, F. Cairo, F. D'Amato, G. Di Donfrancesco, A. Dörnbrack, A. Ebersoldt, M. Ebert, C. Emde, I. Engel, M. Ern, W. Frey, S. Genco, S. Griessbach, J.-U. Grooß, T. Gulde, G. Günther, E. Hösen, L. Hoffmann, V. Homonnai, C. R. Hoyle, I. S. A. Isaksen, D. R. Jackson, I. M. Jánosi, R. L. Jones, K. Kandler, C. Kalicinsky, A. Keil, S. M. Khaykin, F. Khosrawi, R. Kivi, J. Kuttippurath, J. C. Laube, F. Lefèvre, R. Lehmann, S. Ludmann, B. P. Luo, M. Marchand, J. Meyer, V. Mitev, S. Molleker, R. Müller, H. Oelhaf, F. Olschewski, Y. Orsolini, T. Peter, K. Pfeilsticker, C. Piesch, M. C. Pitts, L. R. Poole, F. D. Pope, F. Ravegnani, M. Rex, M. Riese, T. Röckmann, B. Rognerud, A. Roiger, C. Rolf, M. L. Santee, M. Scheibe, C. Schiller, H. Schlager, M. Siciliani de Cumis, N. Sitnikov, O. A. Søvde, R. Spang, N. Spelten, F. Stordal, O. Sumińska-Ebersoldt, A. Ulanovski, J. Ungermann, S. Viciani, C. M. Volk, M. vom Scheidt, P. von der Gathen, K. Walker, T. Wegner, R. Weigel, S. Weinbruch, G. Wetzel, F. G. Wienhold, I. Wohltmann, W. Woiwode, I. A. K. Young, V. Yushkov, B. Zobrist, and F. Stroh
Atmos. Chem. Phys., 13, 9233–9268, https://doi.org/10.5194/acp-13-9233-2013, https://doi.org/10.5194/acp-13-9233-2013, 2013
A. Kylling, R. Buras, S. Eckhardt, C. Emde, B. Mayer, and A. Stohl
Atmos. Meas. Tech., 6, 649–660, https://doi.org/10.5194/amt-6-649-2013, https://doi.org/10.5194/amt-6-649-2013, 2013
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Assessment of horizontally oriented ice crystals with a combination of multiangle polarization lidar and cloud Doppler radar
Benchmarking and improving algorithms for attributing satellite-observed contrails to flights
Riming-dependent snowfall rate and ice water content retrievals for W-band cloud radar
Radiative closure assessment of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-DF product
Satellite-based detection of deep-convective clouds: the sensitivity of infrared methods and implications for cloud climatology
Infrared radiometric image classification and segmentation of cloud structures using a deep-learning framework from ground-based infrared thermal camera observations
Classifying Thermodynamic Cloud Phase Using Machine Learning Models
Algorithm for continual monitoring of fog based on geostationary satellite imagery
Mitigation of satellite OCO-2 CO2 biases in the vicinity of clouds with 3D calculations using the Education and Research 3D Radiative Transfer Toolbox (EaR3T)
Wet-radome attenuation in ARM cloud radars and its utilization in radar calibration using disdrometer measurements
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
Synergy of millimeter-wave radar and radiometer measurements for retrieving frozen hydrometeors in deep convective systems
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products
Augmenting the German weather radar network with vertically pointing cloud radars: implications of resolution and attenuation
Peering into the heart of thunderstorm clouds: insights from cloud radar and spectral polarimetry
Vertical Wind and Drop Size Distribution Retrieval with the CloudCube G-band Doppler Radar
Improved Simulation of Thunderstorm Characteristics and Polarimetric Signatures with LIMA 2-Moment Microphysics in AROME
Harmonized Cloud Datasets for OMI and TROPOMI Using the O2‐O2 477 nm Absorption Band
Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Retrieving Vertical Profiles of Cloud Droplet Effective Radius using Multispectral Measurements from MODIS: Examples and Limitations
Extension of AVHRR-based climate data records: Exploring ways to simulate AVHRR radiances from Suomi-NPP VIIRS data
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
Simulations of Spectral Polarimetric Variables measured in rain at W-band
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
3D cloud masking across a broad swath using multi-angle polarimetry and deep learning
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks
Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations
Identification of multiple co-located hydrometeor types in Doppler spectra from scanning polarimetric cloud radar observations
PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations
An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI
Contrail altitude estimation using GOES-16 ABI data and deep learning
The Ice Cloud Imager: retrieval of frozen water column properties
Supercooled liquid water cloud classification using lidar backscatter peak properties
Marine cloud base height retrieval from MODIS cloud properties using machine learning
How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Simulation and detection efficiency analysis for measurements of polar mesospheric clouds using a spaceborne wide-field-of-view ultraviolet imager
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data
Bayesian cloud-top phase determination for Meteosat Second Generation
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Zhaolong Wu, Patric Seifert, Yun He, Holger Baars, Haoran Li, Cristofer Jimenez, Chengcai Li, and Albert Ansmann
Atmos. Meas. Tech., 18, 3611–3634, https://doi.org/10.5194/amt-18-3611-2025, https://doi.org/10.5194/amt-18-3611-2025, 2025
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This study introduces a novel method to detect horizontally oriented ice crystals (HOICs) using two ground-based polarization lidars at different zenith angles, based on a yearlong dataset collected in Beijing. Combined with cloud radar and reanalysis data, the fine categorization results reveal HOICs occur in calm winds and moderately cold temperatures and are influenced by turbulence near cloud bases. The results enhance our understanding of cloud processes and improve atmospheric models.
Aaron Sarna, Vincent Meijer, Rémi Chevallier, Allie Duncan, Kyle McConnaughay, Scott Geraedts, and Kevin McCloskey
Atmos. Meas. Tech., 18, 3495–3532, https://doi.org/10.5194/amt-18-3495-2025, https://doi.org/10.5194/amt-18-3495-2025, 2025
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Contrails, the clouds formed by aircraft, are have a substantial climate impact. Determining which flight formed each contrail is a critical step to decreasing this impact. We introduce a dataset of synthetic contrail observations with known flight attributions that can be used to develop and assess geostationary-satellite-based contrail-to-flight attribution systems. We additionally introduce a new attribution algorithm and show that it outperforms previous methods.
Nina Maherndl, Alessandro Battaglia, Anton Kötsche, and Maximilian Maahn
Atmos. Meas. Tech., 18, 3287–3304, https://doi.org/10.5194/amt-18-3287-2025, https://doi.org/10.5194/amt-18-3287-2025, 2025
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Accurate measurements of ice water content (IWC) and snowfall rate (SR) are challenging due to high spatial variability and limitations of our measurement techniques. Here, we present a novel method to derive IWC and SR from W-band cloud radar observations, considering the degree of riming. We also investigate the use of the liquid water path (LWP) as a proxy for the occurrence of riming. LWP is easier to measure, so that the method can be applied to both ground-based and space-based instruments.
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.
Andrzej Z. Kotarba and Izabela Wojciechowska
Atmos. Meas. Tech., 18, 2721–2738, https://doi.org/10.5194/amt-18-2721-2025, https://doi.org/10.5194/amt-18-2721-2025, 2025
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The research investigates methods for detecting deep convective clouds (DCCs) using satellite infrared data, essential for understanding long-term climate trends. By validating three popular detection methods against lidar–radar data, it found moderate accuracy (below 75 %), emphasizing the importance of fine-tuning thresholds regionally. The study shows how small threshold changes significantly affect the climatology of severe storms.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
Atmos. Meas. Tech., 18, 2083–2101, https://doi.org/10.5194/amt-18-2083-2025, https://doi.org/10.5194/amt-18-2083-2025, 2025
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Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests based on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observation experiments.
Lexie Goldberger, Maxwell Levin, Carlandra Harris, Andrew Geiss, Matthew D. Shupe, and Damao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1501, https://doi.org/10.5194/egusphere-2025-1501, 2025
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This study leverages machine learning models to classify cloud thermodynamic phases using multi-sensor remote sensing data collected at the Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. We evaluate model performance, feature importance, application of the model to another observatory, and quantify how the models respond to instrument outages.
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
Atmos. Meas. Tech., 18, 1927–1941, https://doi.org/10.5194/amt-18-1927-2025, https://doi.org/10.5194/amt-18-1927-2025, 2025
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Fog and low stratus (FLS) are both persistent clouds close to the Earth's surface. This study introduces a new machine-learning-based algorithm developed for the Meteosat Second Generation geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 h day cycle.
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
Atmos. Meas. Tech., 18, 1859–1884, https://doi.org/10.5194/amt-18-1859-2025, https://doi.org/10.5194/amt-18-1859-2025, 2025
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CO2 column-averaged dry-air mole fractions can be retrieved from space using spectrometers like OCO-2. However, nearby clouds induce spectral distortions that bias these retrievals beyond the accuracy needed for global CO2 source and sink assessments. This study employs a physics-based linearization approach to represent 3D cloud effects and introduces radiance-level mitigation techniques for actual OCO-2 data, enabling the operational implementation of these corrections.
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025, https://doi.org/10.5194/amt-18-1641-2025, 2025
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A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.
Yuli Liu and Ian Stuart Adams
Atmos. Meas. Tech., 18, 1659–1674, https://doi.org/10.5194/amt-18-1659-2025, https://doi.org/10.5194/amt-18-1659-2025, 2025
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This paper presents our latest development in tomographic reconstruction algorithms that use multi-angle (sub)millimeter-wave brightness temperature to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomography technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the technique effectively increases detection sensitivity for small ice cloud particles.
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech., 18, 1621–1640, https://doi.org/10.5194/amt-18-1621-2025, https://doi.org/10.5194/amt-18-1621-2025, 2025
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The study examines the use of the spheroidal shape approximation for calculating cloud radar observables in rain and identifies some limitations. To address these, it introduces the empirical scattering model (ESM) based on high-quality Doppler spectra from a 94 GHz radar. The ESM offers improved accuracy and directly incorporates natural rain's microphysical effects. This new model can enhance retrieval and calibration methods, benefiting cloud radar polarimetry experts and scattering modelers.
Keiichi Ohara and Hirohiko Masunaga
EGUsphere, https://doi.org/10.5194/egusphere-2025-173, https://doi.org/10.5194/egusphere-2025-173, 2025
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Ice particles (e.g., cloud ice, snow and graupel) in convective clouds play key roles in cloud and precipitation formation. This study combines satellite millimeter-wave radar and radiometer observations to estimate the vertical distributions of physical parameters of ice particles such as mass, size, and number densities. CPR radar and GPM radiometer observations together reduce the estimation errors of the physical parameters and provide information on the optimal ice particle shape.
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025, https://doi.org/10.5194/amt-18-1325-2025, 2025
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This study introduces the JAXA EarthCARE Level 2 (L2) cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation are quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
Christian Heske, Florian Ewald, and Silke Groß
EGUsphere, https://doi.org/10.5194/egusphere-2025-691, https://doi.org/10.5194/egusphere-2025-691, 2025
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This study proposes a new technique to augment polarimetric radar measurements of the national German operational radar network with vertically looking cloud radars. The method is tested in two different case studies by comparison to dedicated scanning radars revealing promising results. Future usage of the method is motivated by analyzing the coverage of the operational radar network finding advantageous locations with good radar coverage.
Ho Yi Lydia Mak and Christine Unal
Atmos. Meas. Tech., 18, 1209–1242, https://doi.org/10.5194/amt-18-1209-2025, https://doi.org/10.5194/amt-18-1209-2025, 2025
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The dynamics of thunderclouds are studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
Nitika Yadlapalli Yurk, Matthew Lebsock, Juan Socuellamos, Raquel Rodriguez Monje, Ken Cooper, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-618, https://doi.org/10.5194/egusphere-2025-618, 2025
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Current knowledge of the link between clouds and climate is limited by 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 and elevation.
Cloé David, Clotilde Augros, Benoît Vié, François Bouttier, and Tony Le Bastard
EGUsphere, https://doi.org/10.5194/egusphere-2025-685, https://doi.org/10.5194/egusphere-2025-685, 2025
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Simulations of storm characteristics and associated radar signatures were improved, especially under the freezing level, using an advanced cloud scheme. Discrepancies between observations and forecasts at and above the melting layer highlighted issues in both the radar forward operator and the microphysics. To overcome part of these issues, different parametrizations of the operator were suggested. This work aligns with the future integration of polarimetric data into assimilation systems.
Huan Yu, Isabelle De Smedt, Nicolas Theys, Maarten Sneep, Pepijn Veefkind, and Michel Van Roozendael
EGUsphere, https://doi.org/10.5194/egusphere-2025-478, https://doi.org/10.5194/egusphere-2025-478, 2025
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We introduce a new cloud retrieval algorithm using the O2-O2 absorption band at 477 nm to generate harmonized cloud datasets from OMI and TROPOMI. The algorithm improves upon the OMI O2-O2 operational cloud algorithm in several aspects. The new approach improves consistency in cloud parameters and NO2 retrievals between two sensors.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025, https://doi.org/10.5194/amt-18-773-2025, 2025
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In satellite remote sensing, estimating cloud-base height (CBH) is more challenging than estimating cloud-top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI on JAXA’s GCOM-C satellite to estimate CBHs together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Andrew John Buggee and Peter Andrew Pilewskie
EGUsphere, https://doi.org/10.5194/egusphere-2025-546, https://doi.org/10.5194/egusphere-2025-546, 2025
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A constrained optimal estimation technique was developed to utilize space-borne hyperspectral measurements of reflected solar radiation for retrieving a vertical profile of cloud droplet size, providing insight into the internal structure of a cloud. The improved accuracy and, to a lesser extent, the enhanced spectral sampling provided by next-generation space-borne spectrometers are essential for extracting vertically resolved droplet size information from moderately thick, warm clouds.
Karl-Göran Karlsson, Nina Håkansson, Salomon Eliasson, Erwin Wolters, and Ronald Scheirer
EGUsphere, https://doi.org/10.5194/egusphere-2025-379, https://doi.org/10.5194/egusphere-2025-379, 2025
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The topic is finding methods to extend climate data records from single-instrument satellite observations, in this case the Advanced Very High Resolution Radiometer (AVHRR). Several modern instruments include AVHRR-heritage channels but some corrections are necessary to account for some differences. We have simulated AVHRR data from the VIIIRS sensor on NOAA polar satellites. We find that methods based on machine learning are capable of performing these corrections.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025, https://doi.org/10.5194/amt-18-487-2025, 2025
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We present a technique to discriminate between the Cloudnet target classification of "drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over 2 years at the BCO is presented.
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.
Ioanna Tsikoudi, Alessandro Battaglia, Christine Unal, and Eleni Marinou
EGUsphere, https://doi.org/10.5194/egusphere-2024-3164, https://doi.org/10.5194/egusphere-2024-3164, 2025
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The study simulates spectral polarimetric variables for raindrops as observed by a cloud radar. Raindrops are modelled as oblate spheroids and backscattering properties are computed via the T-matrix method including noise, turbulence and spectral averaging effects. When comparing simulations to measurements, differences on the amplitudes of polarimetric variables are noted. This shows the challenge of using simplified shapes to model raindrop polarimetric variables when moving to mm wavelengths.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
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This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 2024
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Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
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.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024, https://doi.org/10.5194/amt-17-6707-2024, 2024
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This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
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This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Majid Hajipour, Patric Seifert, Hannes Griesche, Kevin Ohneiser, and Martin Radenz
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-173, https://doi.org/10.5194/amt-2024-173, 2024
Revised manuscript accepted for AMT
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This study presents an approach that enables the detection of the shape and orientation of multiple types of co-located hydrometeors in mixed-phase cloud systems. This information is key for improving the understanding of these clouds, as they do contain ice and liquid water simultaneously, making them relevant for the precipitation budget and radiative balance of the Earth's atmosphere. The retrieval is based on elevation scans of polarimetric cloud radars and can therefore be flexibly applied.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
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In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
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This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
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Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
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The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
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Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
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Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
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ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
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Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
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The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
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This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
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.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
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This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
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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.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
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Short summary
We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid...
