Articles | Volume 21, issue 6
https://doi.org/10.5194/acp-21-4285-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-4285-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
22 Mar 2021
Research article | Highlight paper |
| 22 Mar 2021
| 22 Mar 2021
The behavior of high-CAPE (convective available potential energy) summer convection in large-domain large-eddy simulations with ICON
German Meteorological Service, Offenbach am Main, Germany
Ulrike Burkhardt
Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Martin Köhler
German Meteorological Service, Offenbach am Main, Germany
Ioanna Arka
Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Luca Bugliaro
Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Ulrich Görsdorf
German Meteorological Service, Lindenberg, Germany
Ákos Horváth
Meteorological Institute, Universität Hamburg, Hamburg, Germany
Catrin I. Meyer
Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
Jens Reichardt
German Meteorological Service, Lindenberg, Germany
Axel Seifert
German Meteorological Service, Offenbach am Main, Germany
Johan Strandgren
Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
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Wolfgang A. Müller, Stephan Lorenz, Trang V. Pham, Andrea Schneidereit, Renate Brokopf, Victor Brovkin, Nils Brüggemann, Fatemeh Chegini, Dietmar Dommenget, Kristina Fröhlich, Barbara Früh, Veronika Gayler, Helmuth Haak, Stefan Hagemann, Moritz Hanke, Tatiana Ilyina, Johann Jungclaus, Martin Köhler, Peter Korn, Luis Kornblueh, Clarissa A. Kroll, Julian Krüger, Karel Castro-Morales, Ulrike Niemeier, Holger Pohlmann, Iuliia Polkova, Roland Potthast, Thomas Riddick, Manuel Schlund, Tobias Stacke, Roland Wirth, Dakuan Yu, and Jochem Marotzke
Geosci. Model Dev., 18, 9385–9415, https://doi.org/10.5194/gmd-18-9385-2025, https://doi.org/10.5194/gmd-18-9385-2025, 2025
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We provide a new Earth System model configuration framed into the ICON architecture, which provides the baseline for the next generation of climate predictions and projections (hereafter ICON XPP). Two resolutions of ICON XPP are presented that show high runtime performances making it suitable to run long integrations and large-ensemble experiments. ICON XPP similarly perform to CMIP6-class of climate models making it a good basis for climate forecasts and projections, and climate research.
Matteo Aricò, Dennis Piontek, Luca Bugliaro, Johanna Mayer, Richard Müller, Frank Kalinka, and Max Butter
Atmos. Meas. Tech., 18, 7129–7152, https://doi.org/10.5194/amt-18-7129-2025, https://doi.org/10.5194/amt-18-7129-2025, 2025
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The goal is to assess the feasibility of an ice crystal icing detection algorithm based exclusively on remote sensing data. Active measurements are used to train and validate a newly developed random forest algorithm that is applied to passive satellite imagery to estimate the ice crystal icing conditions probability. 83 % of ice crystal icing conditions are correctly detected, showing potential for an operational implementation to mitigate its negative effects on the fleet.
Maleen Hanst, Carmen G. Köhler, Axel Seifert, and Linda Schlemmer
Atmos. Chem. Phys., 25, 17253–17274, https://doi.org/10.5194/acp-25-17253-2025, https://doi.org/10.5194/acp-25-17253-2025, 2025
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Persistent condensation trails typically occur due to aircraft flying through certain cold and humid regions. In most cases, these contrails have a warming impact on the climate. Predicting these regions in advance allows flight planners to re-route airplanes. We show that an adaptation of the ice microphysics scheme in a certain weather forecast model improves the prediction of these regions. Running an ensemble of simulations with this scheme improves the prediction quality even further.
Johannes Speidel, Hannes Vogelmann, Andreas Behrendt, Diego Lange, Matthias Mauder, Jens Reichardt, and Kevin Wolz
Atmos. Meas. Tech., 18, 4923–4948, https://doi.org/10.5194/amt-18-4923-2025, https://doi.org/10.5194/amt-18-4923-2025, 2025
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Humidity transport from the Earth's surface into the atmosphere is relevant for many processes. However, knowledge of the actual distribution of humidity concentrations is sparse – mainly due to technological limitations. With the lidar presented herein, it is possible to measure humidity concentrations and their vertical fluxes up to altitudes of > 3 km with high spatiotemporal resolution, opening new possibilities for detailed process understanding and, ultimately, better model representation.
Ulrich Schumann and Axel Seifert
EGUsphere, https://doi.org/10.5194/egusphere-2025-4512, https://doi.org/10.5194/egusphere-2025-4512, 2025
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Contrails caused by aircraft flying in sufficiently cold and humid air masses, have a weather and a climate impact. For reduction of the climate impact one should avoid flights forming warming contrails. This requires good weather forecast of contrail formation conditions. We present a two-way coupling of the Contrail Cirrus Prediction model (CoCiP) with the global non-hydrostatic icosahedral numerical weather model ICON. We find that contrails are predictable – but only for a finite period.
Hemanth Kumar Alladi, Julian Quimbayo-Duarte, Luca Bugliaro, Johanna Mayer, and Juerg Schmidli
EGUsphere, https://doi.org/10.5194/egusphere-2025-4401, https://doi.org/10.5194/egusphere-2025-4401, 2025
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Thunderstorms can transport moisture into the lower stratosphere, affecting climate. Over mountains, models fail to represent them due to underrepresentation of turbulent mixing and cloud microphysics. This study evaluates the operational TKE and new 2TE turbulence schemes, with single and double moment microphysics, in the ICOsahedral Nonhydrostatic (ICON) model against observations. ICON-TKE shows stronger mixing than 2TE, the DM scheme shows taller storms with more ice and transport than SM.
Gholam Ali Hoshyaripour, Andreas Baer, Sascha Bierbauer, Julia Bruckert, Dominik Brunner, Jochen Foerstner, Arash Hamzehloo, Valentin Hanft, Corina Keller, Martina Klose, Pankaj Kumar, Patrick Ludwig, Enrico Metzner, Lisa Muth, Andreas Pauling, Nikolas Porz, Thomas Reddmann, Luca Reißig, Roland Ruhnke, Khompat Satitkovitchai, Axel Seifert, Miriam Sinnhuber, Michael Steiner, Stefan Versick, Heike Vogel, Michael Weimer, Sven Werchner, and Corinna Hoose
EGUsphere, https://doi.org/10.5194/egusphere-2025-3400, https://doi.org/10.5194/egusphere-2025-3400, 2025
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This paper presents recent advances in ICON-ART, a modeling system that simulates atmospheric composition—such as gases and particles—and their interactions with weather and climate. By integrating updated chemistry, emissions, and aerosol processes, ICON-ART enables detailed, scale-spanning simulations. It supports both scientific research and operational forecasts, contributing to improved air quality and climate predictions.
Josef Zink, Simon Unterstrasser, and Ulrike Burkhardt
EGUsphere, https://doi.org/10.5194/egusphere-2025-3704, https://doi.org/10.5194/egusphere-2025-3704, 2025
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The climate impact of aviation-induced contrail cirrus clouds is strongly influenced by the number of ice crystals that form in the wake of an aircraft under certain conditions. In this study, we systematically investigate the key processes that influence the number of ice crystals formed for hydrogen combustion. A large simulation data set provides the basis for integrating our results into large-scale models used to estimate the climate impact of contrail cirrus clouds.
Patrick Peter, Sigrun Matthes, Christine Frömming, Patrick Jöckel, Luca Bugliaro, Andreas Giez, Martina Krämer, and Volker Grewe
Atmos. Chem. Phys., 25, 5911–5934, https://doi.org/10.5194/acp-25-5911-2025, https://doi.org/10.5194/acp-25-5911-2025, 2025
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Our study examines how well the global climate model EMAC (ECHAM/MESSy Atmospheric Chemistry) predicts contrail formation by analysing temperature and humidity – two key factors for contrail development and persistence. The model underestimates temperature, leading to an overprediction of contrail formation and larger ice-supersaturated regions. Adjusting the model improves temperature accuracy but adds uncertainties. Better predictions of contrail formation areas can help optimise flight tracks to reduce aviation's climate effect.
Jens Reichardt, Felix Lauermann, and Oliver Behrendt
Atmos. Chem. Phys., 25, 5857–5892, https://doi.org/10.5194/acp-25-5857-2025, https://doi.org/10.5194/acp-25-5857-2025, 2025
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Optical remote sensing systems, so-called lidars, are used to learn more about aerosols, which play an important role in atmospheric processes. The present study demonstrates that lidars, which measure the backscattering behavior of aerosols over the entire visible wavelength range, can increase our knowledge of the spatial and temporal occurrence of aerosol layers, the type of aerosol, and their interaction with clouds. The focus of the publication is on wildfire aerosol and Saharan dust.
Tim Lüttmer, Peter Spichtinger, and Axel Seifert
Atmos. Chem. Phys., 25, 4505–4529, https://doi.org/10.5194/acp-25-4505-2025, https://doi.org/10.5194/acp-25-4505-2025, 2025
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We investigate ice formation pathways in idealized convective clouds using a novel microphysics scheme that distinguishes between five ice classes each with their own unique formation mechanism. Ice crystals from rime splintering form the lowermost layer of ice crystals around the updraft core. The majority of ice crystals in the anvil of the convective cloud stems from frozen droplets. Ice stemming from homogeneous and deposition nucleation was only relevant in the overshoot.
Ziming Wang, Luca Bugliaro, Klaus Gierens, Michaela I. Hegglin, Susanne Rohs, Andreas Petzold, Stefan Kaufmann, and Christiane Voigt
Atmos. Chem. Phys., 25, 2845–2861, https://doi.org/10.5194/acp-25-2845-2025, https://doi.org/10.5194/acp-25-2845-2025, 2025
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Upper-tropospheric relative humidity bias in the ERA5 weather model is corrected by 10 % by an artificial neural network using aircraft in-service humidity data and thermodynamic and dynamical variables. The improved skills of the weather model will advance cirrus research, weather forecasts, and measures for contrail reduction.
Pascal Hedelt, Jens Reichardt, Felix Lauermann, Benjamin Weiß, Nicolas Theys, Alberto Redondas, Africa Barreto, Omaira Garcia, and Diego Loyola
Atmos. Chem. Phys., 25, 1253–1272, https://doi.org/10.5194/acp-25-1253-2025, https://doi.org/10.5194/acp-25-1253-2025, 2025
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The 2021 volcanic eruption of Tajogaite on La Palma is investigated using ground-based and satellite measurements. In addition, the atmospheric transport of the volcanic cloud towards Europe is studied in detail. The amount of SO2 released during the eruption and the height of the volcanic plume are in excellent agreement among the different measurements. Furthermore, volcanic aerosol microphysical properties could be retrieved using a new retrieval approach based on lidar measurements.
Jingmin Li, Mattia Righi, Johannes Hendricks, Christof G. Beer, Ulrike Burkhardt, and Anja Schmidt
Atmos. Chem. Phys., 24, 12727–12747, https://doi.org/10.5194/acp-24-12727-2024, https://doi.org/10.5194/acp-24-12727-2024, 2024
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Aiming to understand underlying patterns and trends in aerosols, we characterize the spatial patterns and long-term evolution of lower tropospheric aerosols by clustering multiple aerosol properties from preindustrial times to the year 2050 under three Shared
Socioeconomic Pathway scenarios. The results provide a clear and condensed picture of the spatial extent and distribution of aerosols for different time periods and emission scenarios.
Socioeconomic Pathway scenarios. The results provide a clear and condensed picture of the spatial extent and distribution of aerosols for different time periods and emission scenarios.
Giulia Roccetti, Luca Bugliaro, Felix Gödde, Claudia Emde, Ulrich Hamann, Mihail Manev, Michael Fritz Sterzik, and Cedric Wehrum
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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The amount of sunlight reflected by the Earth’s surface (albedo) is vital for the Earth's radiative system. While satellite instruments offer detailed spatial and temporal albedo maps, they only cover seven wavelength bands. We generate albedo maps that fully span the visible and near-infrared range using a machine learning algorithm. These maps reveal how the reflectivity of different land surfaces varies throughout the year. Our dataset enhances the understanding of the Earth's energy balance.
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.
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.
Ziming Wang, Husi Letu, Huazhe Shang, and Luca Bugliaro
Atmos. Chem. Phys., 24, 7559–7574, https://doi.org/10.5194/acp-24-7559-2024, https://doi.org/10.5194/acp-24-7559-2024, 2024
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The supercooled liquid fraction (SLF) in mixed-phase clouds is retrieved for the first time using passive geostationary satellite observations based on differences in liquid droplet and ice particle radiative properties. The retrieved results are comparable to global distributions observed by active instruments, and the feasibility of the retrieval method to analyze the observed trends of the SLF has been validated.
Raphael Satoru Märkl, Christiane Voigt, Daniel Sauer, Rebecca Katharina Dischl, Stefan Kaufmann, Theresa Harlaß, Valerian Hahn, Anke Roiger, Cornelius Weiß-Rehm, Ulrike Burkhardt, Ulrich Schumann, Andreas Marsing, Monika Scheibe, Andreas Dörnbrack, Charles Renard, Maxime Gauthier, Peter Swann, Paul Madden, Darren Luff, Reetu Sallinen, Tobias Schripp, and Patrick Le Clercq
Atmos. Chem. Phys., 24, 3813–3837, https://doi.org/10.5194/acp-24-3813-2024, https://doi.org/10.5194/acp-24-3813-2024, 2024
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In situ measurements of contrails from a large passenger aircraft burning 100 % sustainable aviation fuel (SAF) show a 56 % reduction in contrail ice crystal numbers compared to conventional Jet A-1. Results from a climate model initialized with the observations suggest a significant decrease in radiative forcing from contrails. Our study confirms that future increased use of low aromatic SAF can reduce the climate impact from aviation.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Axel Seifert, Vanessa Bachmann, Florian Filipitsch, Jochen Förstner, Christian M. Grams, Gholam Ali Hoshyaripour, Julian Quinting, Anika Rohde, Heike Vogel, Annette Wagner, and Bernhard Vogel
Atmos. Chem. Phys., 23, 6409–6430, https://doi.org/10.5194/acp-23-6409-2023, https://doi.org/10.5194/acp-23-6409-2023, 2023
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We investigate how mineral dust can lead to the formation of cirrus clouds. Dusty cirrus clouds lead to a reduction in solar radiation at the surface and, hence, a reduced photovoltaic power generation. Current weather prediction systems are not able to predict this interaction between mineral dust and cirrus clouds. We have developed a new physical description of the formation of dusty cirrus clouds. Overall we can show a considerable improvement in the forecast quality of clouds and radiation.
Ziming Wang, Luca Bugliaro, Tina Jurkat-Witschas, Romy Heller, Ulrike Burkhardt, Helmut Ziereis, Georgios Dekoutsidis, Martin Wirth, Silke Groß, Simon Kirschler, Stefan Kaufmann, and Christiane Voigt
Atmos. Chem. Phys., 23, 1941–1961, https://doi.org/10.5194/acp-23-1941-2023, https://doi.org/10.5194/acp-23-1941-2023, 2023
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Differences in the microphysical properties of contrail cirrus and natural cirrus in a contrail outbreak situation during the ML-CIRRUS campaign over the North Atlantic flight corridor can be observed from in situ measurements. The cirrus radiative effect in the area of the outbreak, derived from satellite observation-based radiative transfer modeling, is warming in the early morning and cooling during the day.
Jens Reichardt, Oliver Behrendt, and Felix Lauermann
Atmos. Meas. Tech., 16, 1–13, https://doi.org/10.5194/amt-16-1-2023, https://doi.org/10.5194/amt-16-1-2023, 2023
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The UVA spectrometer is the latest instrumental addition to the spectrometric fluorescence and Raman lidar RAMSES. The redesigned receiver and the data analysis of the fluorescence measurement are described. Furthermore, the effect of aerosol fluorescence on humidity measurements is studied. It turns out that Raman lidars equipped with a spectrometer show superior performance over those with one discrete fluorescence detection channel only. The cause is variability in the fluorescence spectrum.
Ákos Horváth, James L. Carr, Dong L. Wu, Julia Bruckert, Gholam Ali Hoshyaripour, and Stefan A. Buehler
Atmos. Chem. Phys., 22, 12311–12330, https://doi.org/10.5194/acp-22-12311-2022, https://doi.org/10.5194/acp-22-12311-2022, 2022
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We estimate plume heights for the April 2021 La Soufrière daytime eruptions using GOES-17 near-limb side views and GOES-16–MODIS stereo views. These geometric heights are then compared with brightness-temperature-based radiometric height estimates to characterize the biases of the latter. We also show that the side view method can be applied to infrared imagery and thus nighttime eruptions, albeit with larger uncertainty.
Pooja Verma and Ulrike Burkhardt
Atmos. Chem. Phys., 22, 8819–8842, https://doi.org/10.5194/acp-22-8819-2022, https://doi.org/10.5194/acp-22-8819-2022, 2022
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This paper investigates contrail ice formation within cirrus and the impact of natural cirrus on the contrail ice formation in the high-resolution ICON-LEM simulations over Germany. Contrail formation often leads to increases in cirrus ice crystal number concentration by a few orders of magnitude. Contrail formation is affected by pre-existing cirrus, leading to changes in contrail formation conditions and ice nucleation rates that can be significant in optically thick cirrus.
Mireia Papke Chica, Valerian Hahn, Tiziana Braeuer, Elena de la Torre Castro, Florian Ewald, Mathias Gergely, Simon Kirschler, Luca Bugliaro Goggia, Stefanie Knobloch, Martina Kraemer, Johannes Lucke, Johanna Mayer, Raphael Maerkl, Manuel Moser, Laura Tomsche, Tina Jurkat-Witschas, Martin Zoeger, Christian von Savigny, and Christiane Voigt
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2022-255, https://doi.org/10.5194/acp-2022-255, 2022
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The mixed-phase temperature regime in convective clouds challenges our understanding of microphysical and radiative cloud properties. We provide a rare and unique dataset of aircraft in situ measurements in a strong mid-latitude convective system. We find that mechanisms initiating ice nucleation and growth strongly depend on temperature, relative humidity, and vertical velocity and variate within the measured system, resulting in altitude dependent changes of the cloud liquid and ice fraction.
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.
Julia Bruckert, Gholam Ali Hoshyaripour, Ákos Horváth, Lukas O. Muser, Fred J. Prata, Corinna Hoose, and Bernhard Vogel
Atmos. Chem. Phys., 22, 3535–3552, https://doi.org/10.5194/acp-22-3535-2022, https://doi.org/10.5194/acp-22-3535-2022, 2022
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Volcanic emissions endanger aviation and public health and also influence weather and climate. Forecasting the volcanic-plume dispersion is therefore a critical yet sophisticated task. Here, we show that explicit treatment of volcanic-plume dynamics and eruption source parameters significantly improves volcanic-plume dispersion forecasts. We further demonstrate the lofting of the SO2 due to a heating of volcanic particles by sunlight with major implications for volcanic aerosol research.
Matthieu Plu, Guillaume Bigeard, Bojan Sič, Emanuele Emili, Luca Bugliaro, Laaziz El Amraoui, Jonathan Guth, Beatrice Josse, Lucia Mona, and Dennis Piontek
Nat. Hazards Earth Syst. Sci., 21, 3731–3747, https://doi.org/10.5194/nhess-21-3731-2021, https://doi.org/10.5194/nhess-21-3731-2021, 2021
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Volcanic eruptions that spread out ash over large areas, like Eyjafjallajökull in 2010, may have huge economic consequences due to flight cancellations. In this article, we demonstrate the benefits of source term improvement and of data assimilation for quantifying volcanic ash concentrations. The work, which was supported by the EUNADICS-AV project, is the first one, to our knowledge, that demonstrates the benefit of the assimilation of ground-based lidar data over Europe during an eruption.
Markus Karrer, Axel Seifert, Davide Ori, and Stefan Kneifel
Atmos. Chem. Phys., 21, 17133–17166, https://doi.org/10.5194/acp-21-17133-2021, https://doi.org/10.5194/acp-21-17133-2021, 2021
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Modeling precipitation is of great relevance, e.g., for mitigating damage caused by extreme weather. A key component in accurate precipitation modeling is aggregation, i.e., sticking together of snowflakes. Simulating aggregation is difficult due to multiple parameters that are not well-known. Knowing how these parameters affect aggregation can help its simulation. We put new parameters in the model and select a combination of parameters with which the model can simulate observations better.
Hugues Brenot, Nicolas Theys, Lieven Clarisse, Jeroen van Gent, Daniel R. Hurtmans, Sophie Vandenbussche, Nikolaos Papagiannopoulos, Lucia Mona, Timo Virtanen, Andreas Uppstu, Mikhail Sofiev, Luca Bugliaro, Margarita Vázquez-Navarro, Pascal Hedelt, Michelle Maree Parks, Sara Barsotti, Mauro Coltelli, William Moreland, Simona Scollo, Giuseppe Salerno, Delia Arnold-Arias, Marcus Hirtl, Tuomas Peltonen, Juhani Lahtinen, Klaus Sievers, Florian Lipok, Rolf Rüfenacht, Alexander Haefele, Maxime Hervo, Saskia Wagenaar, Wim Som de Cerff, Jos de Laat, Arnoud Apituley, Piet Stammes, Quentin Laffineur, Andy Delcloo, Robertson Lennart, Carl-Herbert Rokitansky, Arturo Vargas, Markus Kerschbaum, Christian Resch, Raimund Zopp, Matthieu Plu, Vincent-Henri Peuch, Michel Van Roozendael, and Gerhard Wotawa
Nat. Hazards Earth Syst. Sci., 21, 3367–3405, https://doi.org/10.5194/nhess-21-3367-2021, https://doi.org/10.5194/nhess-21-3367-2021, 2021
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The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986).
Matthieu Plu, Barbara Scherllin-Pirscher, Delia Arnold Arias, Rocio Baro, Guillaume Bigeard, Luca Bugliaro, Ana Carvalho, Laaziz El Amraoui, Kurt Eschbacher, Marcus Hirtl, Christian Maurer, Marie D. Mulder, Dennis Piontek, Lennart Robertson, Carl-Herbert Rokitansky, Fritz Zobl, and Raimund Zopp
Nat. Hazards Earth Syst. Sci., 21, 2973–2992, https://doi.org/10.5194/nhess-21-2973-2021, https://doi.org/10.5194/nhess-21-2973-2021, 2021
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Past volcanic eruptions that spread out ash over large areas, like Eyjafjallajökull in 2010, forced the cancellation of thousands of flights and had huge economic consequences.
In this article, an international team in the H2020 EU-funded EUNADICS-AV project has designed a probabilistic model approach to quantify ash concentrations. This approach is evaluated against measurements, and its potential use to mitigate the impact of future large-scale eruptions is discussed.
Ákos Horváth, James L. Carr, Olga A. Girina, Dong L. Wu, Alexey A. Bril, Alexey A. Mazurov, Dmitry V. Melnikov, Gholam Ali Hoshyaripour, and Stefan A. Buehler
Atmos. Chem. Phys., 21, 12189–12206, https://doi.org/10.5194/acp-21-12189-2021, https://doi.org/10.5194/acp-21-12189-2021, 2021
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We give a detailed description of a new technique to estimate the height of volcanic eruption columns from near-limb geostationary imagery. Such oblique angle observations offer spectacular side views of eruption columns protruding from the Earth ellipsoid and thereby facilitate a height-by-angle estimation method. Due to its purely geometric nature, the new technique is unaffected by the limitations of traditional brightness-temperature-based height retrievals.
Ákos Horváth, Olga A. Girina, James L. Carr, Dong L. Wu, Alexey A. Bril, Alexey A. Mazurov, Dmitry V. Melnikov, Gholam Ali Hoshyaripour, and Stefan A. Buehler
Atmos. Chem. Phys., 21, 12207–12226, https://doi.org/10.5194/acp-21-12207-2021, https://doi.org/10.5194/acp-21-12207-2021, 2021
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We demonstrate the side view plume height estimation technique described in Part 1 on seven volcanic eruptions from 2019 and 2020, including the 2019 Raikoke eruption. We explore the strengths and limitations of the new technique in comparison to height estimation from brightness temperatures, stereo observations, and ground-based video footage.
Ulrich Schumann, Ian Poll, Roger Teoh, Rainer Koelle, Enrico Spinielli, Jarlath Molloy, George S. Koudis, Robert Baumann, Luca Bugliaro, Marc Stettler, and Christiane Voigt
Atmos. Chem. Phys., 21, 7429–7450, https://doi.org/10.5194/acp-21-7429-2021, https://doi.org/10.5194/acp-21-7429-2021, 2021
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Short summary
The roughly 70 % reduction of air traffic during the COVID-19 pandemic from March–August 2020 compared to 2019 provides a test case for the relationship between air traffic density, contrails, and their radiative forcing of climate change. This paper investigates the induced traffic and contrail changes in a model study. Besides strong weather changes, the model results indicate aviation-induced cirrus and top-of-the-atmosphere irradiance changes, which can be tested with observations.
Yuefei Zeng, Alberto de Lozar, Tijana Janjic, and Axel Seifert
Geosci. Model Dev., 14, 1295–1307, https://doi.org/10.5194/gmd-14-1295-2021, https://doi.org/10.5194/gmd-14-1295-2021, 2021
Short summary
Short summary
A new integrated mass-flux adjustment filter is introduced and examined with an idealized setup for convective-scale radar data assimilation. It is found that the new filter slightly reduces the accuracy of background and analysis states; however, it preserves the main structure of cold pools and primary mesocyclone properties of supercells. More importantly, it successfully diminishes the imbalance in the analysis considerably and improves the forecasts.
Lukas O. Muser, Gholam Ali Hoshyaripour, Julia Bruckert, Ákos Horváth, Elizaveta Malinina, Sandra Wallis, Fred J. Prata, Alexei Rozanov, Christian von Savigny, Heike Vogel, and Bernhard Vogel
Atmos. Chem. Phys., 20, 15015–15036, https://doi.org/10.5194/acp-20-15015-2020, https://doi.org/10.5194/acp-20-15015-2020, 2020
Short summary
Short summary
Volcanic aerosols endanger aircraft and thus disrupt air travel globally. For aviation safety, it is vital to know the location and lifetime of such aerosols in the atmosphere. Here we show that the interaction of volcanic particles with each other eventually reduces their atmospheric lifetime. Moreover, we demonstrate that sunlight heats these particles, which lifts them several kilometers in the atmosphere. These findings support a more reliable forecast of volcanic aerosol dispersion.
Cited articles
Arakawa, A. and Wu, C.-M.: A Unified Representation of Deep Moist Convection in
Numerical Modeling of the Atmosphere. Part I, J. Atmos.
Sci., 70, 1977–1992, https://doi.org/10.1175/JAS-D-12-0330.1, 2013. a
Austin, R. T., Heymsfield, A. J., and Stephens, G. L.: Retrieval of ice cloud
microphysical parameters using the CloudSat millimeter-wave radar and
temperature, J. Geophys. Res.-Atmos., 114, D00A23,
https://doi.org/10.1029/2008JD010049, 2009. a
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and
Reinhardt, T.: Operational convective-scale numerical weather prediction with
the COSMO model: Description and sensitivities, Mon. Weather Rev., 139,
3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a, b
Barlakas, V., Deneke, H., and Macke, A.: The sub-adiabatic model as a concept for evaluating the representation and radiative effects of low-level clouds in a high-resolution atmospheric model, Atmos. Chem. Phys., 20, 303–322, https://doi.org/10.5194/acp-20-303-2020, 2020. a
Baum, B. A., Yang, P., Heymsfield, A. J., Schmitt, C. G., Xie, Y., Bansemer,
A., Hu, Y.-X., and Zhang, Z.: Improvements in Shortwave Bulk Scattering and
Absorption Models for the Remote Sensing of Ice Clouds, J. Appl.
Meteorol. Climatol., 50, 1037–1056, https://doi.org/10.1175/2010JAMC2608.1,
2011. a
Benas, N., Finkensieper, S., Stengel, M., van Zadelhoff, G.-J., Hanschmann, T., Hollmann, R., and Meirink, J. F.: The MSG-SEVIRI-based cloud property data record CLAAS-2, Earth Syst. Sci. Data, 9, 415–434, https://doi.org/10.5194/essd-9-415-2017, 2017. a, b
Beydoun, H. and Hoose, C.: Aerosol-Cloud-Precipitation Interactions in the
Context of Convective Self-Aggregation, J. Adv. Model. Earth
Sy., 11, 1066–1087, https://doi.org/10.1029/2018MS001523, 2019. a
Bony, S., Stevens, B., Coppin, D., Becker, T., Reed, K. A., Voigt, A., and
Medeiros, B.: Thermodynamic control of anvil cloud amount, P.
Natl. Acad. Sci. USA, 113, 8927–8932, https://doi.org/10.1073/pnas.1601472113,
2016. a
Boutle, I. A., Eyre, J. E. J., and Lock, A. P.: Seamless Stratocumulus
Simulation across the Turbulent Gray Zone, Mon. Weather Rev., 142,
1655–1668, https://doi.org/10.1175/MWR-D-13-00229.1, 2014. a
Bryan, G. H. and Morrison, H.: Sensitivity of a Simulated Squall Line to
Horizontal Resolution and Parameterization of Microphysics, Mon. Weather Rev., 140, 202–225, https://doi.org/10.1175/MWR-D-11-00046.1, 2012. a
Bryan, G. H., Wyngaard, J. C., and Fritsch, J. M.: Resolution Requirements for
the Simulation of Deep Moist Convection, Mon. Weather Rev., 131,
2394–2416, https://doi.org/10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2, 2003. a
Bugliaro, L., Zinner, T., Keil, C., Mayer, B., Hollmann, R., Reuter, M., and Thomas, W.: Validation of cloud property retrievals with simulated satellite radiances: a case study for SEVIRI, Atmos. Chem. Phys., 11, 5603–5624, https://doi.org/10.5194/acp-11-5603-2011, 2011. a, b, c
Cioni, G. and Hohenegger, C.: Effect of Soil Moisture on Diurnal Convection and
Precipitation in Large-Eddy Simulations, J. Hydrometeorol., 18,
1885–1903, https://doi.org/10.1175/JHM-D-16-0241.1, 2017. a
Cioni, G., Cerrai, D., and Klocke, D.: Investigating the predictability of a
Mediterranean tropical-like cyclone using a storm-resolving model, Q.
J. Roy. Meteor. Soc., 144, 1598–1610,
https://doi.org/10.1002/qj.3322, 2018. a
Connolly, P. J., Emersic, C., and Field, P. R.: A laboratory investigation into the aggregation efficiency of small ice crystals, Atmos. Chem. Phys., 12, 2055–2076, https://doi.org/10.5194/acp-12-2055-2012, 2012. a
Costa-Surós, M., Sourdeval, O., Acquistapace, C., Baars, H., Carbajal Henken, C., Genz, C., Hesemann, J., Jimenez, C., König, M., Kretzschmar, J., Madenach, N., Meyer, C. I., Schrödner, R., Seifert, P., Senf, F., Brueck, M., Cioni, G., Engels, J. F., Fieg, K., Gorges, K., Heinze, R., Siligam, P. K., Burkhardt, U., Crewell, S., Hoose, C., Seifert, A., Tegen, I., and Quaas, J.: Detection and attribution of aerosol–cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic model, Atmos. Chem. Phys., 20, 5657–5678, https://doi.org/10.5194/acp-20-5657-2020, 2020. a, b
Deng, M., Mace, G. G., Wang, Z., and Okamoto, H.: Tropical Composition, Cloud
and Climate Coupling Experiment validation for cirrus cloud profiling
retrieval using CloudSat radar and CALIPSO lidar, J. Geophys. Res.-Atmos., 115, D00J15, https://doi.org/10.1029/2009JD013104, 2010. a
Derbyshire, S. H., Beau, I., Bechtold, P., Grandpeix, J.-Y., Piriou, J.-M.,
Redelsperger, J.-L., and Soares, P. M. M.: Sensitivity of moist convection to
environmental humidity, Q. J. Roy. Meteor.
Soc., 130, 3055–3079, https://doi.org/10.1256/qj.03.130, 2004. a
Dipankar, A., Stevens, B., Heinze, R., Moseley, C., Zängl, G., Giorgetta, M.,
and Brdar, S.: Large eddy simulation using the general circulation model
ICON, J. Adv. Model. Earth Sy., 7, 963–986,
https://doi.org/10.1002/2015MS000431, 2015. a, b
Dotzek, N., Groenemeijer, P., Feuerstein, B., and Holzer, A.: Overview of
ESSL's severe convective storms research using the European severe weather
database ESWD, Atmos. Res., 93, 575–86,
https://doi.org/10.1016/j.atmosres.2008.10.020, 2009. a
Duncan, D. I. and Eriksson, P.: An update on global atmospheric ice estimates from satellite observations and reanalyses, Atmos. Chem. Phys., 18, 11205–11219, https://doi.org/10.5194/acp-18-11205-2018, 2018. a, b, c
Eichler, H., Ehrlich, A., Wendisch, M., Mioche, G., Gayet, J., Wirth, M., Emde,
C., and Minikin, A.: Influence of ice crystal shape on retrieval of cirrus
optical thickness and effective radius: A case study, J. Geophys.
Res.-Atmos., 114, D19203, https://doi.org/10.1029/2009JD012215, 2009. a
Eliasson, S., Buehler, S. A., Milz, M., Eriksson, P., and John, V. O.: Assessing observed and modelled spatial distributions of ice water path using satellite data, Atmos. Chem. Phys., 11, 375–391, https://doi.org/10.5194/acp-11-375-2011, 2011. a, b, c, d
Eliasson, S., Holl, G., Buehler, S. A., Kuhn, T., Stengel, M.,
Iturbide-Sanchez, F., and Johnston, M.: Systematic and random errors between
collocated satellite ice water path observations, J. Geophys. Res.-Atmos., 118, 2629–2642, https://doi.org/10.1029/2012JD018381, 2013. a, b
Eriksson, P., Rydberg, B., Mattioli, V., Thoss, A., Accadia, C., Klein, U., and Buehler, S. A.: Towards an operational Ice Cloud Imager (ICI) retrieval product, Atmos. Meas. Tech., 13, 53–71, https://doi.org/10.5194/amt-13-53-2020, 2020. a
Gasparini, B., Blossey, P. N., Hartmann, D. L., Lin, G., and Fan, J.: What
Drives the Life Cycle of Tropical Anvil Clouds?, J. Adv. Model. Earth Sy., 11, 2586–2605,
https://doi.org/10.1029/2019MS001736, 2019. a
Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.: Could
Machine Learning Break the Convection Parameterization Deadlock?, Geophys. Res. Lett., 45, 5742–5751, https://doi.org/10.1029/2018GL078202, 2018. a
Guichard, F. and Couvreux, F.: A short review of numerical cloud-resolving
models, Tellus A, 69, 1373578,
https://doi.org/10.1080/16000870.2017.1373578, 2017. a, b
Hande, L. B., Engler, C., Hoose, C., and Tegen, I.: Seasonal variability of Saharan desert dust and ice nucleating particles over Europe, Atmos. Chem. Phys., 15, 4389–4397, https://doi.org/10.5194/acp-15-4389-2015, 2015. a, b, c
Hande, L. B., Engler, C., Hoose, C., and Tegen, I.: Parameterizing cloud condensation nuclei concentrations during HOPE, Atmos. Chem. Phys., 16, 12059–12079, https://doi.org/10.5194/acp-16-12059-2016, 2016. a
Hanley, K. E., Plant, R. S., Stein, T. H. M., Hogan, R. J., Nicol, J. C., Lean,
H. W., Halliwell, C., and Clark, P. A.: Mixing-length controls on
high-resolution simulations of convective storms, Q. J.
Roy. Meteor. Soc., 141, 272–284, https://doi.org/10.1002/qj.2356, 2015. a
Heinze, R., Dipankar, A., Henken, C. C., Moseley, C., Sourdeval, O., Trömel,
S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A.,
Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H.,
Di Girolamo, P., Evaristo, R., Fischer, J., Frank, C., Friederichs, P.,
Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen, A., Hege, H.-C., Hoose,
C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S., Knippertz, P., Kuhn,
A., van Laar, T., Macke, A., Maurer, V., Mayer, B., Meyer, C. I., Muppa,
S. K., Neggers, R. A. J., Orlandi, E., Pantillon, F., Pospichal, B., Röber,
N., Scheck, L., Seifert, A., Seifert, P., Senf, F., Siligam, P., Simmer, C.,
Steinke, S., Stevens, B., Wapler, K., Weniger, M., Wulfmeyer, V., Zängl, G.,
Zhang, D., and Quaas, J.: Large-eddy simulations over Germany using ICON: a
comprehensive evaluation, Q. J. Roy. Meteor.
Soc., 143, 69–100, https://doi.org/10.1002/qj.2947, 2017. a, b
Hess, M., Koelemeijer, R. B. A., and Stammes, P.: Scattering matrices of
imperfect hexagonal ice crystals, J. Quant. Spectrosc. Ra., 60, 301–308,
1998. a
Heymsfield, A. J. and Kajikawa, M.: An Improved Approach to Calculating
Terminal Velocities of Plate-like Crystals and Graupel, J. Atmos. Sci., 44, 1088–1099,
https://doi.org/10.1175/1520-0469(1987)044<1088:AIATCT>2.0.CO;2, 1987. a
Hogan, R. J., Mittermaier, M. P., and Illingworth, A. J.: The Retrieval of Ice
Water Content from Radar Reflectivity Factor and Temperature and Its Use in
Evaluating a Mesoscale Model, J. Appl. Meteorol. Climatol.,
45, 301–317, https://doi.org/10.1175/JAM2340.1, 2006. a
Holl, G., Eliasson, S., Mendrok, J., and Buehler, S.: SPARE-ICE: Synergistic
ice water path from passive operational sensors, J. Geophys. Res.-Atmos., 119, 1504–1523, https://doi.org/10.1002/2013JD020759, 2014. a, b
Holloway, C. E., Woolnough, S. J., and Lister, G. M. S.: The Effects of
Explicit versus Parameterized Convection on the MJO in a Large-Domain
High-Resolution Tropical Case Study. Part I: Characterization of Large-Scale
Organization and Propagation, J. Atmos. Sci., 70,
1342–1369, https://doi.org/10.1175/JAS-D-12-0227.1, 2013. a
Hong, Y. and Liu, G.: The Characteristics of Ice Cloud Properties Derived from
CloudSat and CALIPSO Measurements, J. Climate, 28, 3880–3901,
https://doi.org/10.1175/JCLI-D-14-00666.1, 2015. a
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation
for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica
D, 230, 112–126,
https://doi.org/10.1016/j.physd.2006.11.008, 2007. a
Illingworth, A. J., Hogan, R. J., O'Connor, E., Bouniol, D., Brooks, M. E.,
Delanoé, J., Donovan, D. P., Eastment, J. D., Gaussiat, N., Goddard, J.
W. F., Haeffelin, M., Baltink, H. K., Krasnov, O. A., Pelon, J., Piriou,
J.-M., Protat, A., Russchenberg, H. W. J., Seifert, A., Tompkins, A. M., van
Zadelhoff, G.-J., Vinit, F., Willén, U., Wilson, D. R., and Wrench, C. L.:
Cloudnet, B. Am. Meteorol. Soc., 88, 883–898,
https://doi.org/10.1175/BAMS-88-6-883, 2007. a, b, c
Keil, C., Heinlein, F., and Craig, G. C.: The convective adjustment time-scale
as indicator of predictability of convective precipitation, Q. J. Roy. Meteor. Soc., 140, 480–490, https://doi.org/10.1002/qj.2143,
2014. a
Khairoutdinov, M. F., Krueger, S. K., Moeng, C.-H., Bogenschutz, P. A., and
Randall, D. A.: Large-Eddy Simulation of Maritime Deep Tropical Convection,
J. Adv. Model. Earth Sy., 1, 15,
https://doi.org/10.3894/JAMES.2009.1.15, 2009. a
Kneifel, S. and Moisseev, D.: Long-Term Statistics of Riming in Nonconvective
Clouds Derived from Ground-Based Doppler Cloud Radar Observations, J. Atmos. Sci., 77, 3495–3508, https://doi.org/10.1175/JAS-D-20-0007.1,
2020. a
Kärcher, B. and Lohmann, U.: A parameterization of cirrus cloud formation:
Homogeneous freezing of supercooled aerosols, J. Geophys. Res.-Atmos., 107, AAC 4-1–AAC 4-10, https://doi.org/10.1029/2001JD000470,
2002. a
Kärcher, B., Hendricks, J., and Lohmann, U.: Physically based parameterization
of cirrus cloud formation for use in global atmospheric models, J. Geophys. Res.-Atmos., 111, D01205, https://doi.org/10.1029/2005JD006219, 2006. a
Leon, D. C., French, J. R., Lasher-Trapp, S., Blyth, A. M., Abel, S. J.,
Ballard, S., Barrett, A., Bennett, L. J., Bower, K., Brooks, B., Brown, P.,
Charlton-Perez, C., Choularton, T., Clark, P., Collier, C., Crosier, J., Cui,
Z., Dey, S., Dufton, D., Eagle, C., Flynn, M. J., Gallagher, M., Halliwell,
C., Hanley, K., Hawkness-Smith, L., Huang, Y., Kelly, G., Kitchen, M.,
Korolev, A., Lean, H., Liu, Z., Marsham, J., Moser, D., Nicol, J., Norton,
E. G., Plummer, D., Price, J., Ricketts, H., Roberts, N., Rosenberg, P. D.,
Simonin, D., Taylor, J. W., Warren, R., Williams, P. I., and Young, G.: The
Convective Precipitation Experiment (COPE): Investigating the Origins of
Heavy Precipitation in the Southwestern United Kingdom, B.
Am. Meteorol. Soc., 97, 1003–1020,
https://doi.org/10.1175/BAMS-D-14-00157.1, 2016. a
Leuenberger, D., Koller, M., Fuhrer, O., and Schär, C.: A Generalization of
the SLEVE Vertical Coordinate, Mon. Weather Rev., 138, 3683–3689,
https://doi.org/10.1175/2010MWR3307.1, 2010. a
Li, J.-L. F., Waliser, D. E., Chen, W.-T., Guan, B., Kubar, T., Stephens, G.,
Ma, H.-Y., Deng, M., Donner, L., Seman, C., and Horowitz, L.: An
observationally based evaluation of cloud ice water in CMIP3 and CMIP5 GCMs
and contemporary reanalyses using contemporary satellite data, J. Geophys. Res.-Atmos., 117, D16105, https://doi.org/10.1029/2012JD017640, 2012. a
Li, J.-L. F., Waliser, D. E., Stephens, G., and Lee, S.: Characterizing and
Understanding Cloud Ice and Radiation Budget Biases in Global Climate Models
and Reanalysis, Meteor. Mon., 56, 13.1–13.20,
https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0007.1, 2016. a
Li, X., Tao, W.-K., Khain, A. P., Simpson, J., and Johnson, D. E.: Sensitivity
of a Cloud-Resolving Model to Bulk and Explicit Bin Microphysical Schemes.
Part II: Cloud Microphysics and Storm Dynamics Interactions, J. Atmos. Sci., 66, 22–40, https://doi.org/10.1175/2008JAS2647.1, 2009. a
Lilly, D. K.: On the numerical simulation of buoyant convection, Tellus, 14,
148–172, https://doi.org/10.1111/j.2153-3490.1962.tb00128.x, 1962. a
Lin, Y.-L., Farley, R. D., and Orville, H. D.: Bulk Parameterization of the
Snow Field in a Cloud Model, J. Clim. Appl. Meteoro., 22,
1065–1092, https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2, 1983. a
Milbrandt, J. A. and Yau, M. K.: A Multimoment Bulk Microphysics
Parameterization. Part IV: Sensitivity Experiments, J. Atmos. Sci., 63, 3137–3159, https://doi.org/10.1175/JAS3817.1, 2006. a
Minnis, P., Nguyen, L., Palikonda, R., W. Heck, P., A Spangenberg, D.,
R. Doelling, D., Ayers, J., Smith Sr, W., M. Khaiyer, M., Trepte, Q., A Avey,
L., Chang, F.-L., Yost, C., Chee, T., and Sun-Mack, S.: Near-real time cloud
retrievals from operational and research meteorological satellites,
Proc. SPIE – The International Society for Optical Engineering,
7107, https://doi.org/10.1117/12.800344, 2008. a
Minnis, P., Hong, G., Sun-Mack, S., Smith Jr., W. L., Chen, Y., and Miller,
S. D.: Estimating nocturnal opaque ice cloud optical depth from MODIS
multispectral infrared radiances using a neural network method, J. Geophys. Res.-Atmos., 121, 4907–4932,
https://doi.org/10.1002/2015JD024456, 2016. a
Minnis, P., Sun-Mack, S., Chen, Y., Chang, F., Yost, C. R., Smith,
W. L., Heck, P. W., Arduini, R. F., Bedka, S. T., Yi, Y., Hong, G.,
Jin, Z., Painemal, D., Palikonda, R., Scarino, B. R., Spangenberg,
D. A., Smith, R. A., Trepte, Q. Z., Yang, P., and Xie, Y.: CERES
MODIS Cloud Product Retrievals for Edition 4 – Part I: Algorithm Changes, IEEE T. Geosci. Remote, 1–37,
https://doi.org/10.1109/TGRS.2020.3008866, 2020. a, b
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.:
Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997. a
Moseley, C., Pscheidt, I., Cioni, G., and Heinze, R.: Impact of resolution on large-eddy simulation of midlatitude summertime convection, Atmos. Chem. Phys., 20, 2891–2910, https://doi.org/10.5194/acp-20-2891-2020, 2020. a
Nakajima, T. and King, M. D.: Determination of the Optical Thickness and
Effective Particle Radius of Clouds from Reflected Solar Radiation
Measurements. Part I: Theory, J. Atmos. Sci., 47,
1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990. a, b, c, d
Nomokonova, T., Ebell, K., Löhnert, U., Maturilli, M., Ritter, C., and O'Connor, E.: Statistics on clouds and their relation to thermodynamic conditions at Ny-Ålesund using ground-based sensor synergy, Atmos. Chem. Phys., 19, 4105–4126, https://doi.org/10.5194/acp-19-4105-2019, 2019. a
Ori, D., Schemann, V., Karrer, M., Dias Neto, J., von Terzi, L., Seifert, A.,
and Kneifel, S.: Evaluation of ice particle growth in ICON using statistics
of multi-frequency Doppler cloud radar observations, Q. J.
Roy. Meteor. Soc., 146, 3830–3849, https://doi.org/10.1002/qj.3875, 2020. a
Platnick, S., Meyer, K., King, M. D., Wind, G., Amarasinghe, N., Marchant, B.,
Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway,
W. L., and Riedi, J. C.: The MODIS Cloud Optical and Microphysical
Products: Collection 6 Updates and Examples From Terra and Aqua, IEEE
T. Geosc. Remote, 55, 502–525,
https://doi.org/10.1109/TGRS.2016.2610522, 2017. a, b
Praz, C., Roulet, Y.-A., and Berne, A.: Solid hydrometeor classification and riming degree estimation from pictures collected with a Multi-Angle Snowflake Camera, Atmos. Meas. Tech., 10, 1335–1357, https://doi.org/10.5194/amt-10-1335-2017, 2017. a
Pscheidt, I., Senf, F., Heinze, R., Deneke, H., Trömel, S., and Hohenegger,
C.: How organized is deep convection over Germany?, Q. J.
Roy. Meteor. Soc., 145, 2366–2384, https://doi.org/10.1002/qj.3552,
2019. a, b
Rabier, F., Järvinen, H., Klinker, E., Mahfouf, J.-F., and Simmons, A.: The
ECMWF operational implementation of four-dimensional variational
assimilation. I: Experimental results with simplified physics, Q.
J. Roy. Meteor. Soc., 126, 1143–1170,
https://doi.org/10.1002/qj.49712656415, 2000. a
Reichardt, J.: Cloud and Aerosol Spectroscopy with Raman Lidar, J.
Atmos. Ocean. Tech., 31, 1946–1963,
https://doi.org/10.1175/JTECH-D-13-00188.1, 2014. a
Reichardt, J., Wandinger, U., Klein, V., Mattis, I., Hilber, B., and Begbie,
R.: RAMSES: German Meteorological Service autonomous Raman lidar for water
vapor, temperature, aerosol, and cloud measurements, Appl. Optics, 51,
8111–8131, https://doi.org/10.1364/AO.51.008111, 2012. a, b
Reichardt, J., Lauermann, F., Horváth, Á., Strandgren, J., and Bugliaro, L.: Comparison of ice water content retrievals from satellite-borne radiometers and ground-based lidar, in preparation, 2021. a
Roebeling, R. A., Feijt, A. J., and Stammes, P.: Cloud property retrievals for
climate monitoring: Implications of differences between Spinning Enhanced
Visible and Infrared Imager (SEVIRI) on METEOSAT-8 and Advanced Very High
Resolution Radiometer (AVHRR) on NOAA-17, J. Geophys. Res.-Atmos., 111, D20210, https://doi.org/10.1029/2005JD006990, 2006. a, b
Rybka, H.: High-CAPE summer convection in large-domain large- eddy simulations
with ICON – model and observational data sets, https://doi.org/10.5281/zenodo.3629457,
2020. a
Satoh, M., Stevens, B., Judt, F., Khairoutdinov, M., Lin, S.-J., Putman, W. M.,
and Düben, P.: Global Cloud-Resolving Models, Curr. Clim. Change Rep., 5,
172–184, https://doi.org/10.1007/s40641-019-00131-0, 2019. a
Saunders, R., Matricardi, M., and Brunel, P.: An improved fast radiative
transfer model for assimilation of satellite radiance observations, Q.
J. Roy. Meteor. Soc., 125, 1407–1425,
https://doi.org/10.1002/qj.1999.49712555615, 1999. a
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018. a
Schemann, V. and Ebell, K.: Simulation of mixed-phase clouds with the ICON large-eddy model in the complex Arctic environment around Ny-Ålesund, Atmos. Chem. Phys., 20, 475–485, https://doi.org/10.5194/acp-20-475-2020, 2020. a, b
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and
Ratier, A.: An introduction to Meteosat Second Generation (MSG), B.
Am. Meteorol. Soc., 83, 977–992,
https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2, 2002. a
Schraff, C., Reich, H., Rhodin, A., Schomburg, A., Stephan, K., Periáñez, A.,
and Potthast, R.: Kilometre-scale ensemble data assimilation for the COSMO
model (KENDA), Q. J. Roy. Meteor. Soc., 142,
1453–1472, https://doi.org/10.1002/qj.2748, 2016. a
Seifert, A. and Beheng, K.: A two-moment cloud microphysics parameterization
for mixed-phase clouds. Part 1: Model description, Meteorol.
Atmos. Phys., 92, 45–66, https://doi.org/10.1007/s00703-005-0112-4, 2006a. a, b
Seifert, A. and Beheng, K.: A two-moment cloud microphysics parameterization
for mixed-phase clouds. Part 2: Maritime vs. continental deep convective
storms, Meteorol. Atmos. Phys., 92, 67–82,
https://doi.org/10.1007/s00703-005-0113-3, 2006b. a
Seifert, A., Köhler, C., and Beheng, K. D.: Aerosol-cloud-precipitation effects over Germany as simulated by a convective-scale numerical weather prediction model, Atmos. Chem. Phys., 12, 709–725, https://doi.org/10.5194/acp-12-709-2012, 2012. a
Selz, T. and Craig, G. C.: Upscale Error Growth in a High-Resolution Simulation
of a Summertime Weather Event over Europe, Mon. Weather Rev., 143,
813–827, https://doi.org/10.1175/MWR-D-14-00140.1, 2015. a
Senf, F., Klocke, D., and Brueck, M.: Size-Resolved Evaluation of Simulated
Deep Tropical Convection, Mon. Weather Rev., 146, 2161–2182,
https://doi.org/10.1175/MWR-D-17-0378.1, 2018. a
Senf, F., Brueck, M., and Klocke, D.: Pair Correlations and Spatial Statistics
of Deep Convection over the Tropical Atlantic, J. Atmos. Sci., 76, 3211–3228, https://doi.org/10.1175/JAS-D-18-0326.1, 2019. a
Stein, T. H. M., Delanoë, J., and Hogan, R. J.: A Comparison among Four
Different Retrieval Methods for Ice-Cloud Properties Using Data from
CloudSat, CALIPSO, and MODIS, J. Appl. Meteorol. Climatol.,
50, 1952–1969, https://doi.org/10.1175/2011JAMC2646.1, 2011. a
Stevens, B. and Bony, S.: What are Climate Models missing?, Science, 340, 1053–1054, 2013. a
Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C., Chen, X.,
Düben, P., Judt, F., Khairoutdinov, M., Klocke, D., Kodama, C., Kornblueh,
L., Lin, S.-J., Neumann, P., Putman, W. M., Röber, N., Shibuya, R.,
Vanniere, B., Vidale, P. L., Wedi, N., and Zhou, L.: DYAMOND: the DYnamics of
the Atmospheric general circulation Modeled On Non-hydrostatic Domains,
Prog. Earth Planet. Sci., 6, 2197–4284,
https://doi.org/10.1186/s40645-019-0304-z, 2019. a
Stevens, B., Acquistapace, C., Hansen, A., Heinze, R., Klinger, C., Klocke, D.,
Rybka, H., Schubotz, W., Windmiller, J., Adamidis, P., Arka, I., Barlakas,
V., Biercamp, J., Brueck, M., Brune, S., Buehler, S., Burkhardt, U., Cioni,
G., Costa-Surós, M., Crewell, S., Crueger, T., Deneke, H., Friederichs, P.,
Carbajal Henken, C., Hohenegger, C., Jacob, M., Jakub, F., Kalthoff, N.,
Kohler, M., Li, P., Lohnert, U., Macke, A., Madenach, N., Mayer, B., Nam, C.,
Naumann, A., Peters, K., Poll, S., Quaas, J., Rober, N., Rochetin, N.,
Scheck, L., Schemann, V., Schnitt, S., Seifert, A., Senf, F., Shapkalijevski,
M., Simmer, C., Singh, S., Sourdeval, O., Spickermann, D., Strandgren, J.,
Tessiot, O., Laar, T. V., Vercauteren, N., Vial, J., Voigt, A., and Zangl,
G.: The Added Value of Large-eddy and Storm-resolving Models for Simulating
Clouds and Precipitation, J. Meteorol. Soc. Japan, 98, 395–435,
https://doi.org/10.2151/jmsj.2020-021, 2020. a, b, c, d
Strandgren, J.: The life cycle of anvil cirrus clouds from a combination of
passive and active satellite remote sensing,
available at: http://nbn-resolving.de/urn:nbn:de:bvb:19-227892 (last access: 8 March 2021), 2018. a
Strandgren, J., Bugliaro, L., Sehnke, F., and Schröder, L.: Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks, Atmos. Meas. Tech., 10, 3547–3573, https://doi.org/10.5194/amt-10-3547-2017, 2017a. a, b, c, d
Strandgren, J., Fricker, J., and Bugliaro, L.: Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI, Atmos. Meas. Tech., 10, 4317–4339, https://doi.org/10.5194/amt-10-4317-2017, 2017b. a
Tomita, H., Miura, H., Iga, S., Nasuno, T., and Satoh, M.: A global
cloud-resolving simulation: Preliminary results from an aqua planet
experiment, Geophys. Res. Lett., 32, L08805,
https://doi.org/10.1029/2005GL022459, 2005. a
Trepte, Q. Z., Minnis, P., Sun-Mack, S., Yost, C. R., Chen, Y., Jin, Z., Hong,
G., Chang, F.-L., Smith Jr., W. L., Bedka, K. M., and Chee, T. L.: Global
cloud detection for CERES Edition 4 using Terra and Aqua MODIS data, IEEE T. Geosci. Remote, 57, 9410–9449,
https://doi.org/10.1109/TGRS.2019.2926620, 2019. a, b
van Stratum, B. J. H. and Stevens, B.: The Impact of Vertical Mixing Biases in
Large-Eddy Simulation on Nocturnal Low Clouds, J. Adv. Model. Earth Sy., 10, 1290–1303, https://doi.org/10.1029/2017MS001239, 2018. a
Van Weverberg, K., Vogelmann, A. M., Morrison, H., and Milbrandt, J. A.:
Sensitivity of Idealized Squall-Line Simulations to the Level of Complexity
Used in Two-Moment Bulk Microphysics Schemes, Mon. Weather Rev., 140,
1883–1907, https://doi.org/10.1175/MWR-D-11-00120.1, 2012. a
Vial, J., Vogel, R., Bony, S., Stevens, B., Winker, D. M., Cai, X., Hohenegger,
C., Naumann, A. K., and Brogniez, H.: A New Look at the Daily Cycle of Trade
Wind Cumuli, J. Adv. Model. Earth Sy., 11, 3148–3166,
https://doi.org/10.1029/2019MS001746, 2019. a
Vidot, J., Baran, A. J., and Brunel, P.: A new ice cloud parameterization for
infrared radiative transfer simulation of cloudy radiances: Evaluation and
optimization with IIR observations and ice cloud profile retrieval products,
J. Geophys. Res.-Atmos., 120, 6937–6951,
https://doi.org/10.1002/2015JD023462, 2015.
a
Waliser, D. E., Li, J.-L. F., Woods, C. P., Austin, R. T., Bacmeister, J.,
Chern, J., Del Genio, A., Jiang, J. H., Kuang, Z., Meng, H., Minnis, P.,
Platnick, S., Rossow, W. B., Stephens, G. L., Sun-Mack, S., Tao, W.-K.,
Tompkins, A. M., Vane, D. G., Walker, C., and Wu, D.: Cloud ice: A climate
model challenge with signs and expectations of progress, J. Geophys. Res.-Atmos., 114, D00A21, https://doi.org/10.1029/2008JD010015, 2009. a, b, c, d
Waliser, D. E., Li, J.-L. F., L'Ecuyer, T. S., and Chen, W.-T.: The impact of
precipitating ice and snow on the radiation balance in global climate models,
Geophys. Res. Lett., 38, L06802, https://doi.org/10.1029/2010GL046478, 2011. a
Wang, Y.: An Explicit Simulation of Tropical Cyclones with a Triply Nested
Movable Mesh Primitive Equation Model: TCM3. Part II: Model Refinements and
Sensitivity to Cloud Microphysics Parameterization, Mon. Weather Rev.,
130, 3022–3036, https://doi.org/10.1175/1520-0493(2002)130<3022:AESOTC>2.0.CO;2, 2002. a
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. Technol., 26, 2310–2323,
https://doi.org/10.1175/2009JTECHA1281.1, 2009. a
Wolke, R., Knoth, O., Hellmuth, O., Schröder, W., and Renner, E.: The parallel
model system LM-MUSCAT for chemistry-transport simulations: Coupling scheme,
parallelization and applications, in: Parallel Computing, edited by: Joubert,
G., Nagel, W., Peters, F., and Walter, W., vol. 13 of Advances in
Parallel Computing, North-Holland, 363–369,
https://doi.org/10.1016/S0927-5452(04)80048-0, 2004. a
Wolke, R., Schröder, W., Schrödner, R., and Renner, E.: Influence of grid
resolution and meteorological forcing on simulated European air quality: A
sensitivity study with the modeling system COSMO–MUSCAT, Atmos.
Environ., 53, 110–130,
https://doi.org/10.1016/j.atmosenv.2012.02.085, 2012. a
Yang, P., Bi, L., Baum, B. A., Liou, K.-N., Kattawar, G. W., Mishchenko, M. I.,
and Cole, B.: Spectrally Consistent Scattering, Absorption, and Polarization
Properties of Atmospheric Ice Crystals at Wavelengths from 0.2 to 100 µm,
J. Atmos. Sci., 70, 330–347,
https://doi.org/10.1175/JAS-D-12-039.1, 2013. a
Yost, C. R., Minnis, P., Sun-Mack, S., Chen, Y., and Smith, W. L.:
CERES MODIS Cloud Product Retrievals for Edition 4 – Part II: Comparisons to
CloudSat and CALIPSO, IEEE T. Geosci. Remote,
1–30, 2020. a
Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral
Non-hydrostatic) modelling framework of DWD and MPI-M: Description of the
non-hydrostatic dynamical core, Q. J. Roy. Meteor.
Soc., 141, 563–579, https://doi.org/10.1002/qj.2378, 2015. a, b
Short summary
Estimating the impact of convection on the upper-tropospheric water budget remains a problem for models employing resolutions of several kilometers or more. A sub-kilometer high-resolution model is used to study summertime convection. The results suggest mostly close agreement with ground- and satellite-based observational data while slightly overestimating total frozen water path and anvil lifetime. The simulations are well suited to supplying information for parameterization development.
Estimating the impact of convection on the upper-tropospheric water budget remains a problem for...
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