Articles | Volume 22, issue 21
https://doi.org/10.5194/acp-22-14095-2022
© Author(s) 2022. 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-22-14095-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Nov 2022
Research article |
| 03 Nov 2022
| 03 Nov 2022
Aerosol characteristics and polarimetric signatures for a deep convective storm over the northwestern part of Europe – modeling and observations
Meteorology Department, Institute of Geosciences, Bonn
University, Bonn, Germany
Jana Mendrok
Deutscher Wetterdienst, Offenbach, Germany
Dominik Brunner
Empa, Swiss Federal Laboratories for Materials Science and
Technology, Dübendorf, Switzerland
Related authors
Prabhakar Shrestha, Silke Trömel, Raquel Evaristo, and Clemens Simmer
Atmos. Chem. Phys., 22, 7593–7618, https://doi.org/10.5194/acp-22-7593-2022, https://doi.org/10.5194/acp-22-7593-2022, 2022
Short summary
Short summary
The study makes use of ensemble numerical simulations with forward operator to evaluate the simulated cloud and precipitation processes with radar observations. While comparing model data with radar has its own challenges due to errors in the forward operator and processed radar measurements, the model was generally found to underestimate the high reflectivity, width/magnitude (value) of ZDR columns and high precipitation.
Prabhakar Shrestha, Jana Mendrok, Velibor Pejcic, Silke Trömel, Ulrich Blahak, and Jacob T. Carlin
Geosci. Model Dev., 15, 291–313, https://doi.org/10.5194/gmd-15-291-2022, https://doi.org/10.5194/gmd-15-291-2022, 2022
Short summary
Short summary
The article focuses on the exploitation of radar polarimetry for model evaluation of stratiform precipitation. The model exhibited a low bias in simulated polarimetric moments at lower levels above the melting layer where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g. fragmentation due to ice–ice collisions) and use of more reliable snow-scattering models in the forward operator to draw valid conclusions.
Silke Trömel, Clemens Simmer, Ulrich Blahak, Armin Blanke, Sabine Doktorowski, Florian Ewald, Michael Frech, Mathias Gergely, Martin Hagen, Tijana Janjic, Heike Kalesse-Los, Stefan Kneifel, Christoph Knote, Jana Mendrok, Manuel Moser, Gregor Köcher, Kai Mühlbauer, Alexander Myagkov, Velibor Pejcic, Patric Seifert, Prabhakar Shrestha, Audrey Teisseire, Leonie von Terzi, Eleni Tetoni, Teresa Vogl, Christiane Voigt, Yuefei Zeng, Tobias Zinner, and Johannes Quaas
Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, https://doi.org/10.5194/acp-21-17291-2021, 2021
Short summary
Short summary
The article introduces the ACP readership to ongoing research in Germany on cloud- and precipitation-related process information inherent in polarimetric radar measurements, outlines pathways to inform atmospheric models with radar-based information, and points to remaining challenges towards an improved fusion of radar polarimetry and atmospheric modelling.
Nikolai Ponomarev, Michael Steiner, Erik Koene, Pascal Rubli, Stuart Grange, Lionel Constantin, Michel Ramonet, Leslie David, Lukas Emmenegger, and Dominik Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2025-3668, https://doi.org/10.5194/egusphere-2025-3668, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Urban inversions are gaining increasing attention, as cities are major contributors to anthropogenic emissions, making accurate emission estimates at this scale essential for supporting climate action plans and verifying reported emission reductions. We estimated carbon dioxide emissions in Zurich and Paris over one year by combining atmospheric observations with mesoscale model simulations. Our study shows how factors like city size, terrain, and measurement methods affect emission estimates.
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
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
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.
Luce Creman, Stuart K. Grange, Pascal Rubli, Andrea Fischer, Dominik Brunner, Christoph Hueglin, Lukas Emmenegger, and Leonie Bernet
EGUsphere, https://doi.org/10.5194/egusphere-2025-3425, https://doi.org/10.5194/egusphere-2025-3425, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
ZiCOS-L is a network of low-cost sensors in Zurich (Switzerland) to monitor carbon dioxide (CO2) concentrations. After correcting for drift and checking the sensor performance, we found that local factors like traffic, public events and vegetation affect CO2 levels. Even though the sensors have higher uncertainties than other sensors, the lower cost allows for a denser network with detailed insights into CO2 levels across the city, helping cities track emissions and support climate action plans.
Eleftherios Ioannidis, Antoon Meesters, Michael Steiner, Dominik Brunner, Friedemann Reum, Isabelle Pison, Antoine Berchet, Rona Thompson, Espen Sollum, Frank-Thomas Koch, Christoph Gerbig, Fenjuan Wang, Shamil Maksyutov, Aki Tsuruta, Maria Tenkanen, Tuula Aalto, Guillaume Monteil, Hong Lin, Ge Ren, Marko Scholze, and Sander Houweling
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-235, https://doi.org/10.5194/essd-2025-235, 2025
Preprint under review for ESSD
Short summary
Short summary
This paper describes a detailed study on CH4 European emissions, using different methodologies (9 total inverse models). The study spans over 15 years and provides detailed information on European CH4 emission trends and seasonality, using in-situ data, including ICOS network. Our results highlight the importance of improving details in the inversion setup, such as the treatment of lateral boundary conditions to narrow the uncertainty ranges further.
Gerrit Kuhlmann, Foteini Stavropoulou, Stefan Schwietzke, Daniel Zavala-Araiza, Andrew Thorpe, Andreas Hueni, Lukas Emmenegger, Andreea Calcan, Thomas Röckmann, and Dominik Brunner
Atmos. Chem. Phys., 25, 5371–5385, https://doi.org/10.5194/acp-25-5371-2025, https://doi.org/10.5194/acp-25-5371-2025, 2025
Short summary
Short summary
A measurement campaign in 2019 found that methane emissions from oil and gas in Romania were significantly higher than reported. In 2021, our follow-up campaign using airborne remote sensing showed a marked decreases in emissions by 20 %–60 % due to improved infrastructure. The study highlights the importance of measurement-based emission monitoring and illustrates the value of a multi-scale assessment integrating ground-based observations with large-scale airborne remote sensing campaigns.
Stavros Stagakis, Dominik Brunner, Junwei Li, Leif Backman, Anni Karvonen, Lionel Constantin, Leena Järvi, Minttu Havu, Jia Chen, Sophie Emberger, and Liisa Kulmala
Biogeosciences, 22, 2133–2161, https://doi.org/10.5194/bg-22-2133-2025, https://doi.org/10.5194/bg-22-2133-2025, 2025
Short summary
Short summary
The balance between CO2 uptake and emissions from urban green areas is still not well understood. This study evaluated for the first time the urban park CO2 exchange simulations with four different types of biosphere model by comparing them with observations. Even though some advantages and disadvantages of the different model types were identified, there was no strong evidence that more complex models performed better than simple ones.
Dominik Brunner, Ivo Suter, Leonie Bernet, Lionel Constantin, Stuart K. Grange, Pascal Rubli, Junwei Li, Jia Chen, Alessandro Bigi, and Lukas Emmenegger
EGUsphere, https://doi.org/10.5194/egusphere-2025-640, https://doi.org/10.5194/egusphere-2025-640, 2025
Short summary
Short summary
In order to support the city of Zurich in tracking its path to net-zero greenhouse gas emissions planned to be reached by 2040, a CO2 emission monitoring system was established. The system combines a dense network of CO2 sensors with a high-resolution atmospheric transport model GRAMM/GRAL. This study presents the setup of the model together with its numerous inputs and evaluates its performance in comparison with the observations from the CO2 sensor network.
Rainer Hilland, Josh Hashemi, Stavros Stagakis, Dominik Brunner, Lionel Constantin, Natascha Kljun, Betty Molinier, Samuel Hammer, Lukas Emmenegger, and Andreas Christen
EGUsphere, https://doi.org/10.5194/egusphere-2025-1088, https://doi.org/10.5194/egusphere-2025-1088, 2025
Short summary
Short summary
We present a study of simultaneously measured fluxes of carbon dioxide (CO2) and co-emitted species in the city of Zurich. Flux measurements of CO2 alone can’t be attributed to specific emission sectors, such as road transport or residential heating. We present a model which uses the measured ratios of CO2 to carbon monoxide (CO) and nitrogen oxides (NOx) as well as sector-specific reference ratios, to attribute measured fluxes to their emission sectors.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
Geosci. Model Dev., 18, 1505–1544, https://doi.org/10.5194/gmd-18-1505-2025, https://doi.org/10.5194/gmd-18-1505-2025, 2025
Short summary
Short summary
The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
Stuart K. Grange, Pascal Rubli, Andrea Fischer, Dominik Brunner, Christoph Hueglin, and Lukas Emmenegger
Atmos. Chem. Phys., 25, 2781–2806, https://doi.org/10.5194/acp-25-2781-2025, https://doi.org/10.5194/acp-25-2781-2025, 2025
Short summary
Short summary
Carbon dioxide (CO2) is a very important atmospheric pollutant, and to better understand the gas's source and sink dynamics, a mid-cost sensor network hosting 26 sites was deployed in and around Zurich, Switzerland. The sensor measurement performance was quantified, and natural and anthropogenic CO2 emission sources were explored with a focus on what drives high CO2 levels. The observations will be used further by others to validate what is thought to be known about CO2 emissions in the region.
Hossein Maazallahi, Foteini Stavropoulou, Samuel Jonson Sutanto, Michael Steiner, Dominik Brunner, Mariano Mertens, Patrick Jöckel, Antoon Visschedijk, Hugo Denier van der Gon, Stijn Dellaert, Nataly Velandia Salinas, Stefan Schwietzke, Daniel Zavala-Araiza, Sorin Ghemulet, Alexandru Pana, Magdalena Ardelean, Marius Corbu, Andreea Calcan, Stephen A. Conley, Mackenzie L. Smith, and Thomas Röckmann
Atmos. Chem. Phys., 25, 1497–1511, https://doi.org/10.5194/acp-25-1497-2025, https://doi.org/10.5194/acp-25-1497-2025, 2025
Short summary
Short summary
This article presents insights from airborne in situ measurements collected during the ROmanian Methane Emissions from Oil and gas (ROMEO) campaign supported by two models. Results reveal Romania's oil and gas methane emissions were significantly under-reported to the United Nations Framework Convention on Climate Change (UNFCCC) in 2019. A large underestimation was also found in the Emissions Database for Global Atmospheric Research (EDGAR) v7.0 for the study domain in the same year.
Diego Santaren, Janne Hakkarainen, Gerrit Kuhlmann, Erik Koene, Frédéric Chevallier, Iolanda Ialongo, Hannakaisa Lindqvist, Janne Nurmela, Johanna Tamminen, Laia Amorós, Dominik Brunner, and Grégoire Broquet
Atmos. Meas. Tech., 18, 211–239, https://doi.org/10.5194/amt-18-211-2025, https://doi.org/10.5194/amt-18-211-2025, 2025
Short summary
Short summary
This study evaluates data-driven inversion methods for estimating CO2 emissions from local sources, such as power plants and cities, using meteorological data and XCO2 and NO2 satellite images rather than atmospheric transport modeling. We assess and compare the performance of five different methods using simulations of 1 year of satellite images, taken from the upcoming Copernicus CO2 Monitoring Mission, covering 15 power plants and the city of Berlin, Germany.
Michael Steiner, Luca Cantarello, Stephan Henne, and Dominik Brunner
Atmos. Chem. Phys., 24, 12447–12463, https://doi.org/10.5194/acp-24-12447-2024, https://doi.org/10.5194/acp-24-12447-2024, 2024
Short summary
Short summary
Atmospheric greenhouse gas inversions have great potential to independently check reported bottom-up emissions; however they are subject to large uncertainties. It is paramount to address and reduce the largest source of uncertainty, which stems from the representation of atmospheric transport in the models. In this study, we show that the use of a temporally varying flow-dependent atmospheric transport uncertainty can enhance the accuracy of emission estimation in an idealized experiment.
Ana Maria Roxana Petrescu, Glen P. Peters, Richard Engelen, Sander Houweling, Dominik Brunner, Aki Tsuruta, Bradley Matthews, Prabir K. Patra, Dmitry Belikov, Rona L. Thompson, Lena Höglund-Isaksson, Wenxin Zhang, Arjo J. Segers, Giuseppe Etiope, Giancarlo Ciotoli, Philippe Peylin, Frédéric Chevallier, Tuula Aalto, Robbie M. Andrew, David Bastviken, Antoine Berchet, Grégoire Broquet, Giulia Conchedda, Stijn N. C. Dellaert, Hugo Denier van der Gon, Johannes Gütschow, Jean-Matthieu Haussaire, Ronny Lauerwald, Tiina Markkanen, Jacob C. A. van Peet, Isabelle Pison, Pierre Regnier, Espen Solum, Marko Scholze, Maria Tenkanen, Francesco N. Tubiello, Guido R. van der Werf, and John R. Worden
Earth Syst. Sci. Data, 16, 4325–4350, https://doi.org/10.5194/essd-16-4325-2024, https://doi.org/10.5194/essd-16-4325-2024, 2024
Short summary
Short summary
This study provides an overview of data availability from observation- and inventory-based CH4 emission estimates. It systematically compares them and provides recommendations for robust comparisons, aiming to steadily engage more parties in using observational methods to complement their UNFCCC submissions. Anticipating improvements in atmospheric modelling and observations, future developments need to resolve knowledge gaps in both approaches and to better quantify remaining uncertainty.
Sandro Meier, Erik F. M. Koene, Maarten Krol, Dominik Brunner, Alexander Damm, and Gerrit Kuhlmann
Atmos. Chem. Phys., 24, 7667–7686, https://doi.org/10.5194/acp-24-7667-2024, https://doi.org/10.5194/acp-24-7667-2024, 2024
Short summary
Short summary
Nitrogen oxides (NOx = NO + NO2) are important air pollutants. This study addresses the challenge of accurately estimating NOx emissions from NO2 satellite observations. We develop a realistic model to convert NO2 to NOx by using simulated plumes from various power plants. We apply the model to satellite NO2 observations, significantly reducing biases in estimated NOx emissions. The study highlights the potential for a consistent, high-resolution estimation of NOx emissions using satellite data.
Gerrit Kuhlmann, Erik Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
Geosci. Model Dev., 17, 4773–4789, https://doi.org/10.5194/gmd-17-4773-2024, https://doi.org/10.5194/gmd-17-4773-2024, 2024
Short summary
Short summary
We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter notebooks included in the library.
Michael Steiner, Wouter Peters, Ingrid Luijkx, Stephan Henne, Huilin Chen, Samuel Hammer, and Dominik Brunner
Atmos. Chem. Phys., 24, 2759–2782, https://doi.org/10.5194/acp-24-2759-2024, https://doi.org/10.5194/acp-24-2759-2024, 2024
Short summary
Short summary
The Paris Agreement increased interest in estimating greenhouse gas (GHG) emissions of individual countries, but top-down emission estimation is not yet considered policy-relevant. It is therefore paramount to reduce large errors and to build systems that are based on the newest atmospheric transport models. In this study, we present the first application of ICON-ART in the inverse modeling of GHG fluxes with an ensemble Kalman filter and present our results for European CH4 emissions.
Robert Hanfland, Dominik Brunner, Christiane Voigt, Alina Fiehn, Anke Roiger, and Margit Pattantyús-Ábrahám
Atmos. Chem. Phys., 24, 2511–2534, https://doi.org/10.5194/acp-24-2511-2024, https://doi.org/10.5194/acp-24-2511-2024, 2024
Short summary
Short summary
To show that the three-dimensional dispersion of plumes simulated by the Atmospheric Radionuclide Transport Model within the planetary boundary layer agrees with real plumes, we identify the most important input parameters and analyse the turbulence properties of five different turbulence models in very unstable stratification conditions using their deviation from the well-mixed state. Simulations show that one model agrees slightly better in unstable stratification conditions.
Ioannis Katharopoulos, Dominique Rust, Martin K. Vollmer, Dominik Brunner, Stefan Reimann, Simon J. O'Doherty, Dickon Young, Kieran M. Stanley, Tanja Schuck, Jgor Arduini, Lukas Emmenegger, and Stephan Henne
Atmos. Chem. Phys., 23, 14159–14186, https://doi.org/10.5194/acp-23-14159-2023, https://doi.org/10.5194/acp-23-14159-2023, 2023
Short summary
Short summary
The effectiveness of climate change mitigation needs to be scrutinized by monitoring greenhouse gas (GHG) emissions. Countries report their emissions to the UN in a bottom-up manner. By combining atmospheric observations and transport models someone can independently validate emission estimates in a top-down fashion. We report Swiss emissions of synthetic GHGs based on kilometer-scale transport and inverse modeling, highlighting the role of appropriate resolution in complex terrain.
Foteini Stavropoulou, Katarina Vinković, Bert Kers, Marcel de Vries, Steven van Heuven, Piotr Korbeń, Martina Schmidt, Julia Wietzel, Pawel Jagoda, Jaroslav M. Necki, Jakub Bartyzel, Hossein Maazallahi, Malika Menoud, Carina van der Veen, Sylvia Walter, Béla Tuzson, Jonas Ravelid, Randulph Paulo Morales, Lukas Emmenegger, Dominik Brunner, Michael Steiner, Arjan Hensen, Ilona Velzeboer, Pim van den Bulk, Hugo Denier van der Gon, Antonio Delre, Maklawe Essonanawe Edjabou, Charlotte Scheutz, Marius Corbu, Sebastian Iancu, Denisa Moaca, Alin Scarlat, Alexandru Tudor, Ioana Vizireanu, Andreea Calcan, Magdalena Ardelean, Sorin Ghemulet, Alexandru Pana, Aurel Constantinescu, Lucian Cusa, Alexandru Nica, Calin Baciu, Cristian Pop, Andrei Radovici, Alexandru Mereuta, Horatiu Stefanie, Alexandru Dandocsi, Bas Hermans, Stefan Schwietzke, Daniel Zavala-Araiza, Huilin Chen, and Thomas Röckmann
Atmos. Chem. Phys., 23, 10399–10412, https://doi.org/10.5194/acp-23-10399-2023, https://doi.org/10.5194/acp-23-10399-2023, 2023
Short summary
Short summary
In this study, we quantify CH4 emissions from onshore oil production sites in Romania at source and facility level using a combination of ground- and drone-based measurement techniques. We show that the total CH4 emissions in our studied areas are much higher than the emissions reported to UNFCCC, and up to three-quarters of the detected emissions are related to operational venting. Our results suggest that oil and gas production infrastructure in Romania holds a massive mitigation potential.
Ana Maria Roxana Petrescu, Chunjing Qiu, Matthew J. McGrath, Philippe Peylin, Glen P. Peters, Philippe Ciais, Rona L. Thompson, Aki Tsuruta, Dominik Brunner, Matthias Kuhnert, Bradley Matthews, Paul I. Palmer, Oksana Tarasova, Pierre Regnier, Ronny Lauerwald, David Bastviken, Lena Höglund-Isaksson, Wilfried Winiwarter, Giuseppe Etiope, Tuula Aalto, Gianpaolo Balsamo, Vladislav Bastrikov, Antoine Berchet, Patrick Brockmann, Giancarlo Ciotoli, Giulia Conchedda, Monica Crippa, Frank Dentener, Christine D. Groot Zwaaftink, Diego Guizzardi, Dirk Günther, Jean-Matthieu Haussaire, Sander Houweling, Greet Janssens-Maenhout, Massaer Kouyate, Adrian Leip, Antti Leppänen, Emanuele Lugato, Manon Maisonnier, Alistair J. Manning, Tiina Markkanen, Joe McNorton, Marilena Muntean, Gabriel D. Oreggioni, Prabir K. Patra, Lucia Perugini, Isabelle Pison, Maarit T. Raivonen, Marielle Saunois, Arjo J. Segers, Pete Smith, Efisio Solazzo, Hanqin Tian, Francesco N. Tubiello, Timo Vesala, Guido R. van der Werf, Chris Wilson, and Sönke Zaehle
Earth Syst. Sci. Data, 15, 1197–1268, https://doi.org/10.5194/essd-15-1197-2023, https://doi.org/10.5194/essd-15-1197-2023, 2023
Short summary
Short summary
This study updates the state-of-the-art scientific overview of CH4 and N2O emissions in the EU27 and UK in Petrescu et al. (2021a). Yearly updates are needed to improve the different respective approaches and to inform on the development of formal verification systems. It integrates the most recent emission inventories, process-based model and regional/global inversions, comparing them with UNFCCC national GHG inventories, in support to policy to facilitate real-time verification procedures.
Dominik Brunner, Gerrit Kuhlmann, Stephan Henne, Erik Koene, Bastian Kern, Sebastian Wolff, Christiane Voigt, Patrick Jöckel, Christoph Kiemle, Anke Roiger, Alina Fiehn, Sven Krautwurst, Konstantin Gerilowski, Heinrich Bovensmann, Jakob Borchardt, Michal Galkowski, Christoph Gerbig, Julia Marshall, Andrzej Klonecki, Pascal Prunet, Robert Hanfland, Margit Pattantyús-Ábrahám, Andrzej Wyszogrodzki, and Andreas Fix
Atmos. Chem. Phys., 23, 2699–2728, https://doi.org/10.5194/acp-23-2699-2023, https://doi.org/10.5194/acp-23-2699-2023, 2023
Short summary
Short summary
We evaluated six atmospheric transport models for their capability to simulate the CO2 plumes from two of the largest power plants in Europe by comparing the models against aircraft observations collected during the CoMet (Carbon Dioxide and Methane Mission) campaign in 2018. The study analyzed how realistically such plumes can be simulated at different model resolutions and how well the planned European satellite mission CO2M will be able to quantify emissions from power plants.
Peter Bergamaschi, Arjo Segers, Dominik Brunner, Jean-Matthieu Haussaire, Stephan Henne, Michel Ramonet, Tim Arnold, Tobias Biermann, Huilin Chen, Sebastien Conil, Marc Delmotte, Grant Forster, Arnoud Frumau, Dagmar Kubistin, Xin Lan, Markus Leuenberger, Matthias Lindauer, Morgan Lopez, Giovanni Manca, Jennifer Müller-Williams, Simon O'Doherty, Bert Scheeren, Martin Steinbacher, Pamela Trisolino, Gabriela Vítková, and Camille Yver Kwok
Atmos. Chem. Phys., 22, 13243–13268, https://doi.org/10.5194/acp-22-13243-2022, https://doi.org/10.5194/acp-22-13243-2022, 2022
Short summary
Short summary
We present a novel high-resolution inverse modelling system, "FLEXVAR", and its application for the inverse modelling of European CH4 emissions in 2018. The new system combines a high spatial resolution of 7 km x 7 km with a variational data assimilation technique, which allows CH4 emissions to be optimized from individual model grid cells. The high resolution allows the observations to be better reproduced, while the derived emissions show overall good consistency with two existing models.
Simone M. Pieber, Béla Tuzson, Stephan Henne, Ute Karstens, Christoph Gerbig, Frank-Thomas Koch, Dominik Brunner, Martin Steinbacher, and Lukas Emmenegger
Atmos. Chem. Phys., 22, 10721–10749, https://doi.org/10.5194/acp-22-10721-2022, https://doi.org/10.5194/acp-22-10721-2022, 2022
Short summary
Short summary
Understanding regional greenhouse gas emissions into the atmosphere is a prerequisite to mitigate climate change. In this study, we investigated the regional contributions of carbon dioxide (CO2) at the location of the high Alpine observatory Jungfraujoch (JFJ, Switzerland, 3580 m a.s.l.). To this purpose, we combined receptor-oriented atmospheric transport simulations for CO2 concentration in the period 2009–2017 with stable carbon isotope (δ13C–CO2) information.
Prabhakar Shrestha, Silke Trömel, Raquel Evaristo, and Clemens Simmer
Atmos. Chem. Phys., 22, 7593–7618, https://doi.org/10.5194/acp-22-7593-2022, https://doi.org/10.5194/acp-22-7593-2022, 2022
Short summary
Short summary
The study makes use of ensemble numerical simulations with forward operator to evaluate the simulated cloud and precipitation processes with radar observations. While comparing model data with radar has its own challenges due to errors in the forward operator and processed radar measurements, the model was generally found to underestimate the high reflectivity, width/magnitude (value) of ZDR columns and high precipitation.
Randulph Morales, Jonas Ravelid, Katarina Vinkovic, Piotr Korbeń, Béla Tuzson, Lukas Emmenegger, Huilin Chen, Martina Schmidt, Sebastian Humbel, and Dominik Brunner
Atmos. Meas. Tech., 15, 2177–2198, https://doi.org/10.5194/amt-15-2177-2022, https://doi.org/10.5194/amt-15-2177-2022, 2022
Short summary
Short summary
Mapping trace gas emission plumes using in situ measurements from unmanned aerial vehicles (UAVs) is an emerging and attractive possibility to quantify emissions from localized sources. We performed an extensive controlled-release experiment to develop an optimal quantification method and to determine the related uncertainties under various environmental and sampling conditions. Our approach was successful in quantifying local methane sources from drone-based measurements.
Gerrit Kuhlmann, Ka Lok Chan, Sebastian Donner, Ying Zhu, Marc Schwaerzel, Steffen Dörner, Jia Chen, Andreas Hueni, Duc Hai Nguyen, Alexander Damm, Annette Schütt, Florian Dietrich, Dominik Brunner, Cheng Liu, Brigitte Buchmann, Thomas Wagner, and Mark Wenig
Atmos. Meas. Tech., 15, 1609–1629, https://doi.org/10.5194/amt-15-1609-2022, https://doi.org/10.5194/amt-15-1609-2022, 2022
Short summary
Short summary
Nitrogen dioxide (NO2) is an air pollutant whose concentration often exceeds air quality guideline values, especially in urban areas. To map the spatial distribution of NO2 in Munich, we conducted the Munich NO2 Imaging Campaign (MuNIC), where NO2 was measured with stationary, mobile, and airborne in situ and remote sensing instruments. The campaign provides a unique dataset that has been used to compare the different instruments and to study the spatial variability of NO2 and its sources.
Prabhakar Shrestha, Jana Mendrok, Velibor Pejcic, Silke Trömel, Ulrich Blahak, and Jacob T. Carlin
Geosci. Model Dev., 15, 291–313, https://doi.org/10.5194/gmd-15-291-2022, https://doi.org/10.5194/gmd-15-291-2022, 2022
Short summary
Short summary
The article focuses on the exploitation of radar polarimetry for model evaluation of stratiform precipitation. The model exhibited a low bias in simulated polarimetric moments at lower levels above the melting layer where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g. fragmentation due to ice–ice collisions) and use of more reliable snow-scattering models in the forward operator to draw valid conclusions.
Alan J. Geer, Peter Bauer, Katrin Lonitz, Vasileios Barlakas, Patrick Eriksson, Jana Mendrok, Amy Doherty, James Hocking, and Philippe Chambon
Geosci. Model Dev., 14, 7497–7526, https://doi.org/10.5194/gmd-14-7497-2021, https://doi.org/10.5194/gmd-14-7497-2021, 2021
Short summary
Short summary
Satellite observations of radiation from the earth can have strong sensitivity to cloud and precipitation in the atmosphere, with applications in weather forecasting and the development of models. Computing the radiation received at the satellite sensor using radiative transfer theory requires a simulation of the optical properties of a volume containing a large number of cloud and precipitation particles. This article describes the physics used to generate these
bulkoptical properties.
Silke Trömel, Clemens Simmer, Ulrich Blahak, Armin Blanke, Sabine Doktorowski, Florian Ewald, Michael Frech, Mathias Gergely, Martin Hagen, Tijana Janjic, Heike Kalesse-Los, Stefan Kneifel, Christoph Knote, Jana Mendrok, Manuel Moser, Gregor Köcher, Kai Mühlbauer, Alexander Myagkov, Velibor Pejcic, Patric Seifert, Prabhakar Shrestha, Audrey Teisseire, Leonie von Terzi, Eleni Tetoni, Teresa Vogl, Christiane Voigt, Yuefei Zeng, Tobias Zinner, and Johannes Quaas
Atmos. Chem. Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, https://doi.org/10.5194/acp-21-17291-2021, 2021
Short summary
Short summary
The article introduces the ACP readership to ongoing research in Germany on cloud- and precipitation-related process information inherent in polarimetric radar measurements, outlines pathways to inform atmospheric models with radar-based information, and points to remaining challenges towards an improved fusion of radar polarimetry and atmospheric modelling.
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
Short summary
Short summary
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.
Antoine Berchet, Espen Sollum, Rona L. Thompson, Isabelle Pison, Joël Thanwerdas, Grégoire Broquet, Frédéric Chevallier, Tuula Aalto, Adrien Berchet, Peter Bergamaschi, Dominik Brunner, Richard Engelen, Audrey Fortems-Cheiney, Christoph Gerbig, Christine D. Groot Zwaaftink, Jean-Matthieu Haussaire, Stephan Henne, Sander Houweling, Ute Karstens, Werner L. Kutsch, Ingrid T. Luijkx, Guillaume Monteil, Paul I. Palmer, Jacob C. A. van Peet, Wouter Peters, Philippe Peylin, Elise Potier, Christian Rödenbeck, Marielle Saunois, Marko Scholze, Aki Tsuruta, and Yuanhong Zhao
Geosci. Model Dev., 14, 5331–5354, https://doi.org/10.5194/gmd-14-5331-2021, https://doi.org/10.5194/gmd-14-5331-2021, 2021
Short summary
Short summary
We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is a programming protocol to allow various inversion bricks to be exchanged among researchers.
The ensemble of bricks makes a flexible, transparent and open-source Python-based tool. We describe the main structure and functionalities and demonstrate it in a simple academic case.
Ana Maria Roxana Petrescu, Chunjing Qiu, Philippe Ciais, Rona L. Thompson, Philippe Peylin, Matthew J. McGrath, Efisio Solazzo, Greet Janssens-Maenhout, Francesco N. Tubiello, Peter Bergamaschi, Dominik Brunner, Glen P. Peters, Lena Höglund-Isaksson, Pierre Regnier, Ronny Lauerwald, David Bastviken, Aki Tsuruta, Wilfried Winiwarter, Prabir K. Patra, Matthias Kuhnert, Gabriel D. Oreggioni, Monica Crippa, Marielle Saunois, Lucia Perugini, Tiina Markkanen, Tuula Aalto, Christine D. Groot Zwaaftink, Hanqin Tian, Yuanzhi Yao, Chris Wilson, Giulia Conchedda, Dirk Günther, Adrian Leip, Pete Smith, Jean-Matthieu Haussaire, Antti Leppänen, Alistair J. Manning, Joe McNorton, Patrick Brockmann, and Albertus Johannes Dolman
Earth Syst. Sci. Data, 13, 2307–2362, https://doi.org/10.5194/essd-13-2307-2021, https://doi.org/10.5194/essd-13-2307-2021, 2021
Short summary
Short summary
This study is topical and provides a state-of-the-art scientific overview of data availability from bottom-up and top-down CH4 and N2O emissions in the EU27 and UK. The data integrate recent emission inventories with process-based model data and regional/global inversions for the European domain, aiming at reconciling them with official country-level UNFCCC national GHG inventories in support to policy and to facilitate real-time verification procedures.
Gerrit Kuhlmann, Dominik Brunner, Grégoire Broquet, and Yasjka Meijer
Atmos. Meas. Tech., 13, 6733–6754, https://doi.org/10.5194/amt-13-6733-2020, https://doi.org/10.5194/amt-13-6733-2020, 2020
Short summary
Short summary
The European CO2M mission is a proposed constellation of CO2 imaging satellites expected to monitor CO2 emissions of large cities. Using synthetic observations, we show that a constellation of two or more satellites should be able to quantify Berlin's annual emissions with 10–20 % accuracy, even when considering atmospheric transport model errors. We therefore expect that CO2M will make an important contribution to the monitoring and verification of CO2 emissions from cities worldwide.
Ying Zhu, Jia Chen, Xiao Bi, Gerrit Kuhlmann, Ka Lok Chan, Florian Dietrich, Dominik Brunner, Sheng Ye, and Mark Wenig
Atmos. Chem. Phys., 20, 13241–13251, https://doi.org/10.5194/acp-20-13241-2020, https://doi.org/10.5194/acp-20-13241-2020, 2020
Short summary
Short summary
Average NO2 concentration of on-street mobile measurements (MMs) near the monitoring stations (MSs) was found to be considerably higher than the MSs data. The common measurement height (H) and distance (D) of the MSs result in 27 % lower average concentrations in total than the concentration of our MMs. Another 21 % difference remained after correcting the influence of the measuring H and D. This result makes our city-wide measurements for capturing the full range of concentrations necessary.
Cited articles
Arino, O., Ramos Perez, J. J., Kalogirou, V., Bontemps,
S., Defourny, P., and
Van Bogaert, E.: Global Land Cover Map for 2009 (GlobCover 2009), © European Space Agency (ESA) & Université catholique de Louvain (UCL), PANGAEA [data set], https://doi.org/10.1594/PANGAEA.787668
2012. a
Ashby, S. F. and Falgout, R. D.: A parallel multigrid preconditioned
conjugate
gradient algorithm for groundwater flow simulations, Nucl. Sci.
Eng., 124, 145–159, 1996. a
Baklanov, A., Schlünzen, K., Suppan, P., Baldasano, J., Brunner, D.,
Aksoyoglu, S., Carmichael, G., Douros, J., Flemming, J., Forkel, R.,
Galmarini, S., Gauss, M., Grell, G., Hirtl, M., Joffre, S., Jorba, O., Kaas,
E., Kaasik, M., Kallos, G., Kong, X., Korsholm, U., Kurganskiy, A., Kushta,
J., Lohmann, U., Mahura, A., Manders-Groot, A., Maurizi, A., Moussiopoulos,
N., Rao, S. T., Savage, N., Seigneur, C., Sokhi, R. S., Solazzo, E.,
Solomos, S., Sørensen, B., Tsegas, G., Vignati, E., Vogel, B., and Zhang,
Y.: Online coupled regional meteorology chemistry models in Europe: current
status and prospects, Atmos. Chem. Phys., 14, 317–398,
https://doi.org/10.5194/acp-14-317-2014, 2014. 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, 2011. a
Bangert, M., Nenes, A., Vogel, B., Vogel, H., Barahona, D., Karydis, V. A.,
Kumar, P., Kottmeier, C., and Blahak, U.: Saharan dust event impacts on cloud
formation and radiation over Western Europe, Atmos. Chem. Phys., 12,
4045–4063, https://doi.org/10.5194/acp-12-4045-2012, 2012. a, b, c, d
Barahona, D. and Nenes, A.: Parameterization of cloud droplet formation in
large-scale models: Including effects of entrainment, J. Geophys.
Res.-Atmos., 112, D16206, https://doi.org/10.1029/2007JD008473, 2007. a
Barahona, D. and Nenes, A.: Parameterizing the competition between
homogeneous and heterogeneous freezing in cirrus cloud formation –
monodisperse ice nuclei, Atmos. Chem. Phys., 9, 369–381,
https://doi.org/10.5194/acp-9-369-2009, 2009. a
Barahona, D., West, R. E. L., Stier, P., Romakkaniemi, S., Kokkola, H., and
Nenes, A.: Comprehensively accounting for the effect of giant CCN in cloud
activation parameterizations, Atmos. Chem. Phys., 10, 2467–2473,
https://doi.org/10.5194/acp-10-2467-2010, 2010. a
Barros, A. P. , Shrestha, P., Chavez, S., and Duan, Y.: Modeling Aerosol-Cloud-Precipitation Interactions in Mountainous Regions: Challenges in the Representation of Indirect Microphysical Effects with Impacts at Subregional Scales, in: Rainfall – Extremes, Distribution and Properties, edited by: Abbot, J. and Hammond, A., IntechOpen, https://doi.org/10.5772/intechopen.80025, 2018. a
Blahak, U.: Towards a better representation of high density ice particles in
a
state-of-the-art two-moment bulk microphysical scheme, in: Proc. 15th Int.
Conf. Clouds and Precip., Cancun, Mexico, vol. 20208, 2008. a
Blahak, U.: RADAR_MIE_LM and RADAR_MIELIB - Calculation of Radar
Reflectivity from Model Output, COSMO Technical Report 28, Consortium
for
Small Scale Modeling (COSMO),
http://www.cosmo-model.org/content/model/cosmo/techReports/docs/techReport28.pdf (last access: 25 October 2022),
2016. a
Boersma, K. F., Eskes, H. J., Dirksen, R. J., van der A, R. J., Veefkind, J.
P., Stammes, P., Huijnen, V., Kleipool, Q. L., Sneep, M., Claas, J., Leitão,
J., Richter, A., Zhou, Y., and Brunner, D.: An improved tropospheric NO2
column retrieval algorithm for the Ozone Monitoring Instrument, Atmos. Meas.
Tech., 4, 1905–1928, https://doi.org/10.5194/amt-4-1905-2011, 2011. a
Boersma, K. F., Eskes, H. J., Richter, A., De Smedt, I., Lorente, A.,
Beirle, S., van Geffen, J. H. G. M., Zara, M., Peters, E., Van Roozendael,
M., Wagner, T., Maasakkers, J. D., van der A, R. J., Nightingale, J., De
Rudder, A., Irie, H., Pinardi, G., Lambert, J.-C., and Compernolle, S. C.:
Improving algorithms and uncertainty estimates for satellite NO2
retrievals: results from the quality assurance for the essential climate
variables (QA4ECV) project, Atmos. Meas. Tech., 11, 6651–6678,
https://doi.org/10.5194/amt-11-6651-2018, 2018. a
Craig, A., Valcke, S., and Coquart, L.: Development and performance of a new
version of the OASIS coupler, OASIS3-MCT_3.0, Geosci. Model Dev., 10,
3297–3308, https://doi.org/10.5194/gmd-10-3297-2017, 2017. a
De Smedt, I., Pinardi, G., Vigouroux, C., Compernolle, S., Bais, A.,
Benavent, N., Boersma, F., Chan, K.-L., Donner, S., Eichmann, K.-U., Hedelt,
P., Hendrick, F., Irie, H., Kumar, V., Lambert, J.-C., Langerock, B., Lerot,
C., Liu, C., Loyola, D., Piters, A., Richter, A., Rivera Cárdenas, C.,
Romahn, F., Ryan, R. G., Sinha, V., Theys, N., Vlietinck, J., Wagner, T.,
Wang, T., Yu, H., and Van Roozendael, M.: Comparative assessment of TROPOMI
and OMI formaldehyde observations and validation against MAX-DOAS network
column measurements, Atmos. Chem. Phys., 21, 12561–12593,
https://doi.org/10.5194/acp-21-12561-2021, 2021. a
Diederich, M., Ryzhkov, A., Simmer, C., Zhang, P., and Trömel, S.: Use of
specific attenuation for rainfall measurement at X-band radar wavelengths.
Part I: Radar calibration and partial beam blockage estimation, J.
Hydrometeorol., 16, 487–502, 2015a. a
Diederich, M., Ryzhkov, A., Simmer, C., Zhang, P., and Trömel, S.: Use of
specific attenuation for rainfall measurement at X-band radar wavelengths.
Part II: Rainfall estimates and comparison with rain gauges, J.
Hydrometeorol., 16, 503–516, 2015b. a
DWD (Deutscher Wetterdienst): Pamore – Abruf archivierter Daten der Vorhersagemodelle, DWD [data], https://www.dwd.de/DE/leistungen/pamore/pamore.html, last access: 22 October 2022. a
Emmons, L. K., Walters, S., Hess, P. G., Lamarque, J.-F., Pfister, G. G.,
Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando,
J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S. L., and Kloster, S.:
Description and evaluation of the Model for Ozone and Related chemical
Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3, 43–67,
https://doi.org/10.5194/gmd-3-43-2010, 2010. a
EMPA: C2SM/processing-chain, EMPA [code], https://github.com/C2SM/processing-chain, last access: 25 October 2022. a
Fan, J., Leung, L. R., Rosenfeld, D., Chen, Q., Li, Z., Zhang, J., and Yan,
H.:
Microphysical effects determine macrophysical response for aerosol impacts
on
deep convective clouds, P. Natl. Acad. Sci. USA, 110,
E4581–E4590, 2013. a
Fan, J., Wang, Y., Rosenfeld, D., and Liu, X.: Review of aerosol–cloud
interactions: Mechanisms, significance, and challenges, J.
Atmos. Sci., 73, 4221–4252, 2016. a
Fan, J., Rosenfeld, D., Zhang, Y., Giangrande, S. E., Li, Z., Machado, L. A., Martin, S. T., Yang, Y., Wang, J., Artaxo, P., Barbosa, H. M. J., Braga, R. C., Comstock, J. M., Feng, Z., Gao, W., Gomes, H. B., Mei, F., Pöhlker, C., Pöhlker, M. L., Pöschl, U., and Souza, R. A. F.:
Substantial convection
and precipitation enhancements by ultrafine aerosol particles, Science,
359,
411–418, 2018. a
Fountoukis, C. and Nenes, A.: Continued development of a cloud droplet
formation parameterization for global climate models, J. Geophys.
Res.-Atmos., 110, D11212, https://doi.org/10.1029/2004JD005591, 2005. a
Gasper, F., Goergen, K., Shrestha, P., Sulis, M., Rihani, J., Geimer, M., and
Kollet, S.: Implementation and scaling of the fully coupled Terrestrial
Systems Modeling Platform (TerrSysMP v1.0) in a massively parallel
supercomputing environment – a case study on JUQUEEN (IBM Blue Gene/Q),
Geosci. Model Dev., 7, 2531–2543, https://doi.org/10.5194/gmd-7-2531-2014, 2014 (data available at: https://www.terrsysmp.org/, last access: 27 October 2022). a, b
Gebhardt, C., Theis, S., Paulat, M., and Bouallègue, Z. B.: Uncertainties
in COSMO-DE precipitation forecasts introduced by model perturbations and
variation of lateral boundaries, Atmos. Res., 100, 168–177, 2011. a
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A.,
Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R.,
Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: Advancements in the
Aerosol Robotic Network (AERONET) Version 3 database – automated
near-real-time quality control algorithm with improved cloud screening for
Sun photometer aerosol optical depth (AOD) measurements, Atmos. Meas. Tech.,
12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019. a, b
Guo, J., Deng, M., Lee, S. S., Wang, F., Li, Z., Zhai, P., Liu, H., Lv, W.,
Yao, W., and Li, X.: Delaying precipitation and lightning by air pollution
over the Pearl River Delta. Part I: Observational analyses, J.
Geophys. Res.-Atmos., 121, 6472–6488, 2016. a
Guo, J., Liu, H., Li, Z., Rosenfeld, D., Jiang, M., Xu, W., Jiang, J. H., He,
J., Chen, D., Min, M., and Zhai, P.: Aerosol-induced changes in the vertical
structure of precipitation: a perspective of TRMM precipitation radar, Atmos.
Chem. Phys., 18, 13329–13343, https://doi.org/10.5194/acp-18-13329-2018, 2018. a
Holben, B. N., Eck, T. F., Slutsker, I. A., Tanre, D., Buis, J., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y., Nakajima, T., Lavenu, F., Janakowiak, I., and Smirnov, A.: AERONET – A
federated instrument network and data archive for aerosol characterization,
Remote Sens. Environ., 66, 1–16, 1998. a
Igel, A. L. and van den Heever, S. C.: Invigoration or Enervation of
Convective
Clouds by Aerosols?, Geophys. Res. Lett., 48, e2021GL093804, https://doi.org/10.1029/2021GL093804, 2021. a
Iguchi, T., Rutledge, S. A., Tao, W.-K., Matsui, T., Dolan, B., Lang, S. E.,
and Barnum, J.: Impacts of aerosol and environmental conditions on maritime
and continental deep convective systems using a bin microphysical model,
J. Geophys. Res.-Atmos., 125, e2019JD030952, https://doi.org/10.1029/2019JD030952,
2020. a, b
Ilotoviz, E., Khain, A., Ryzhkov, A. V., and Snyder, J. C.: Relationship
between aerosols, hail microphysics, and Z DR columns, J.
Atmos. Sci., 75, 1755–1781, 2018. a
Jähn, M., Kuhlmann, G., Mu, Q., Haussaire, J.-M., Ochsner, D., Osterried, K.,
Clément, V., and Brunner, D.: An online emission module for atmospheric
chemistry transport models: implementation in COSMO-GHG v5.6a and COSMO-ART
v5.1-3.1, Geosci. Model Dev., 13, 2379–2392, https://doi.org/10.5194/gmd-13-2379-2020,
2020 (data available at: https://github.com/C2SM-RCM/cosmo-emission-processing, last access: 27 October 2022). a, b, c
Jiang, J. H., Su, H., Huang, L., Wang, Y., Massie, S., Zhao, B., Omar, A.,
and
Wang, Z.: Contrasting effects on deep convective clouds by different types
of
aerosols, Nat. Commun., 9, 1–7, 2018. a
Jones, J. E. and Woodward, C. S.: Newton–Krylov-multigrid solvers for
large-scale, highly heterogeneous, variably saturated flow problems, Adv.
Water Resour., 24, 763–774, 2001. a
Jung, Y., Zhang, G., and Xue, M.: Assimilation of Simulated Polarimetric
Radar
Data for a Convective Storm Using the Ensemble Kalman Filter. Part I:
Observation Operators for Reflectivity and Polarimetric Variables, Mon.
Weather Rev., 136, 2228–2245, 2008. a
Khain, A., Rosenfeld, D., and Pokrovsky, A.: Aerosol impact on the dynamics
and
microphysics of deep convective clouds, Q. J. Roy.
Meteor. Soc., 131, 2639–2663, 2005. a
Knote, C. and Brunner, D.: An advanced scheme for wet scavenging and
liquid-phase chemistry in a regional online-coupled chemistry transport
model, Atmos. Chem. Phys., 13, 1177–1192, https://doi.org/10.5194/acp-13-1177-2013,
2013. a
Knote, C., Brunner, D., Vogel, H., Allan, J., Asmi, A., Äijälä, M., Carbone,
S., van der Gon, H. D., Jimenez, J. L., Kiendler-Scharr, A., Mohr, C.,
Poulain, L., Prévôt, A. S. H., Swietlicki, E., and Vogel, B.: Towards an
online-coupled chemistry-climate model: evaluation of trace gases and
aerosols in COSMO-ART, Geosci. Model Dev., 4, 1077–1102,
https://doi.org/10.5194/gmd-4-1077-2011, 2011. a, b, c
Kollet, S. J. and Maxwell, R. M.: Integrated surface–groundwater flow
modeling: A free-surface overland flow boundary condition in a parallel
groundwater flow model, Adv. Water Resour., 29, 945–958, 2006. a
Koren, I., Martins, J. V., Remer, L. A., and Afargan, H.: Smoke invigoration
versus inhibition of clouds over the Amazon, Science, 321, 946–949, 2008. a
Kreklow, J., Tetzlaff, B., Burkhard, B., and Kuhnt, G.: Radar-Based
Precipitation Climatology in Germany – Developments, Uncertainties and
Potentials, Atmosphere, 11, 217, https://doi.org/10.3390/atmos11020217, 2020. a
Krotkov, N. A., Lamsal, L. N., Marchenko, S. V., Bucsela, E. J., Swartz,
W. H.,
Joiner, J., and the OMI core team: OMI/Aura Nitrogen Dioxide (NO2)
Total and
Tropospheric Column 1-orbit L2 Swath 13×24 km V003,
10.5067/aura/omi/data2017, Goddard Earth Sciences Data and Information
Services Center (GES DISC) [data set],
https://doi.org/10.5067/Aura/OMI/DATA2017, 2019. a
Kuenen, J. J. P., Visschedijk, A. J. H., Jozwicka, M., and Denier van der
Gon, H. A. C.: TNO-MACC_II emission inventory; a multi-year (2003–2009)
consistent high-resolution European emission inventory for air quality
modelling, Atmos. Chem. Phys., 14, 10963–10976,
https://doi.org/10.5194/acp-14-10963-2014, 2014. a
Kuenen, J., Dellaert, S., Visschedijk, A., Jalkanen, J.-P., Super, I., and Denier van der Gon, H.: Copernicus Atmosphere Monitoring Service regional emissions version 5.1 business-as-usual 2020 (CAMS-REG-v5.1 BAU 2020), Copernicus Atmosphere Monitoring Service, ECCAD [data set], https://doi.org/10.24380/eptm-kn40, 2021.
Kuenen, J., Dellaert, S., Visschedijk, A., Jalkanen, J.-P., Super, I., and
Denier van der Gon, H.: CAMS-REG-v4: a state-of-the-art high-resolution
European emission inventory for air quality modelling, Earth Syst. Sci. Data,
14, 491–515, https://doi.org/10.5194/essd-14-491-2022, 2022. a
Kulmala, M., Asmi, A., Lappalainen, H. K., Baltensperger, U., Brenguier,
J.-L., Facchini, M. C., Hansson, H.-C., Hov, Ø., O'Dowd, C. D., Pöschl, U.,
Wiedensohler, A., Boers, R., Boucher, O., de Leeuw, G., Denier van der Gon,
H. A. C., Feichter, J., Krejci, R., Laj, P., Lihavainen, H., Lohmann, U.,
McFiggans, G., Mentel, T., Pilinis, C., Riipinen, I., Schulz, M., Stohl, A.,
Swietlicki, E., Vignati, E., Alves, C., Amann, M., Ammann, M., Arabas, S.,
Artaxo, P., Baars, H., Beddows, D. C. S., Bergström, R., Beukes, J. P.,
Bilde, M., Burkhart, J. F., Canonaco, F., Clegg, S. L., Coe, H., Crumeyrolle,
S., D'Anna, B., Decesari, S., Gilardoni, S., Fischer, M., Fjaeraa, A. M.,
Fountoukis, C., George, C., Gomes, L., Halloran, P., Hamburger, T., Harrison,
R. M., Herrmann, H., Hoffmann, T., Hoose, C., Hu, M., Hyvärinen, A., Hõrrak,
U., Iinuma, Y., Iversen, T., Josipovic, M., Kanakidou, M., Kiendler-Scharr,
A., Kirkevåg, A., Kiss, G., Klimont, Z., Kolmonen, P., Komppula, M.,
Kristjánsson, J.-E., Laakso, L., Laaksonen, A., Labonnote, L., Lanz, V. A.,
Lehtinen, K. E. J., Rizzo, L. V., Makkonen, R., Manninen, H. E., McMeeking,
G., Merikanto, J., Minikin, A., Mirme, S., Morgan, W. T., Nemitz, E.,
O'Donnell, D., Panwar, T. S., Pawlowska, H., Petzold, A., Pienaar, J. J.,
Pio, C., Plass-Duelmer, C., Prévôt, A. S. H., Pryor, S., Reddington, C. L.,
Roberts, G., Rosenfeld, D., Schwarz, J., Seland, Ø., Sellegri, K., Shen, X.
J., Shiraiwa, M., Siebert, H., Sierau, B., Simpson, D., Sun, J. Y., Topping,
D., Tunved, P., Vaattovaara, P., Vakkari, V., Veefkind, J. P., Visschedijk,
A., Vuollekoski, H., Vuolo, R., Wehner, B., Wildt, J., Woodward, S., Worsnop,
D. R., van Zadelhoff, G.-J., Zardini, A. A., Zhang, K., van Zyl, P. G.,
Kerminen, V.-M., S Carslaw, K., and Pandis, S. N.: General overview: European
Integrated project on Aerosol Cloud Climate and Air Quality interactions
(EUCAARI) – integrating aerosol research from nano to global scales, Atmos.
Chem. Phys., 11, 13061–13143, https://doi.org/10.5194/acp-11-13061-2011, 2011. a
Kumar, P., Sokolik, I. N., and Nenes, A.: Parameterization of cloud droplet
formation for global and regional models: including adsorption activation
from insoluble CCN, Atmos. Chem. Phys., 9, 2517–2532,
https://doi.org/10.5194/acp-9-2517-2009, 2009. a
Kumjian, M. R.: Principles and applications of dual-polarization weather
radar. Part I: Description of the polarimetric radar variables, Journal of
Operational Meteorology, 1, 226–242, https://doi.org/10.15191/nwajom.2013.0119,
2013. a, b
Kumjian, M. R. and Ryzhkov, A. V.: Polarimetric signatures in supercell
thunderstorms, J. Appl. Meteorol. Clim., 47,
1940–1961, 2008. a
Lamsal, L. N., Krotkov, N. A., Vasilkov, A., Marchenko, S., Qin, W., Yang,
E.-S., Fasnacht, Z., Joiner, J., Choi, S., Haffner, D., Swartz, W. H.,
Fisher, B., and Bucsela, E.: Ozone Monitoring Instrument (OMI) Aura nitrogen
dioxide standard product version 4.0 with improved surface and cloud
treatments, Atmos. Meas. Tech., 14, 455–479, https://doi.org/10.5194/amt-14-455-2021,
2021a. a
Lamsal, L. N., Krotkov, N. A., Vasilkov, A., Marchenko, S., Qin, W., Yang,
E.-S., Fasnacht, Z., Joiner, J., Choi, S., Haffner, D., Swartz, W. H.,
Fisher, B., and Bucsela, E.: Ozone Monitoring Instrument (OMI) Aura nitrogen
dioxide standard product version 4.0 with improved surface and cloud
treatments, Atmos. Meas. Tech., 14, 455–479, https://doi.org/10.5194/amt-14-455-2021,
2021b. a
Lebo, Z. J. and Seinfeld, J. H.: Theoretical basis for convective
invigoration due to increased aerosol concentration, Atmos. Chem. Phys., 11,
5407–5429, https://doi.org/10.5194/acp-11-5407-2011, 2011. a
Levy, R. and Hsu, C.: MODIS Atmosphere L2 Aerosol Product.,
10.5067/modis/mod04_l2.061, NASA MODIS Adaptive Processing System, Goddard
Space Flight Center [data set], USA,
https://doi.org/10.5067/MODIS/MOD04_L2.061, 2015. a
Li, Z., Niu, F., Fan, J., Liu, Y., Rosenfeld, D., and Ding, Y.: Long-term
impacts of aerosols on the vertical development of clouds and
precipitation,
Nat. Geosci., 4, 888–894, 2011. a
Löhnert, U., Schween, J. H., Acquistapace, C., Ebell, K., Maahn, M., Barrera-Verdejo, M., Hirsikko, A., Bohn, B., Knaps, A., O’Connor, E., Simmer, C., Wahner, A., and Crewell, S.: JOYCE: Jülich observatory for cloud evolution, B.
Am. Meteorol. Soc., 96, 1157–1174, 2015. a
Majewski, D., Liermann, D., Prohl, P., Ritter, B., Buchhold, M., Hanisch, T.,
Paul, G., Wergen, W., and Baumgardner, J.: The operational global
icosahedral–hexagonal gridpoint model GME: Description and high-resolution
tests, Mon. Weather Rev., 130, 319–338, 2002. a
Martin, S. T., Andreae, M. O., Artaxo, P., Baumgardner, D., Chen, Q., Goldstein, A. H., Guenther, A., Heald, C. L., Mayol‐Bracero, O. L., McMurry, P. H., and Pauliquevis, T.: Sources and properties of Amazonian aerosol particles,
Rev.
Geophys., 48, RG2002, https://doi.org/10.1029/2008RG000280,
2010. a
Maxwell, R. M.: A terrain-following grid transform and preconditioner for
parallel, large-scale, integrated hydrologic modeling, Adv. Water
Resour., 53, 109–117, 2013. a
Milbrandt, J. A., Morrison, H., Dawson II, D. T., and Paukert, M.: A
triple-moment representation of ice in the Predicted Particle Properties
(P3)
microphysics scheme, J. Atmos. Sci., 78, 439–458, 2021. a
Mishchenko, M. I.: Calculation of the amplitude matrix for a nonspherical
particle in a fixed orientation, Appl. Opt., 39, 1026–1031,
https://doi.org/10.1364/AO.39.001026, 2000. a
Morrison, H.: On the robustness of aerosol effects on an idealized supercell
storm simulated with a cloud system-resolving model, Atmos. Chem. Phys., 12,
7689–7705, https://doi.org/10.5194/acp-12-7689-2012, 2012. a
Morrison, H. and Milbrandt, J. A.: Parameterization of cloud microphysics
based
on the prediction of bulk ice particle properties. Part I: Scheme
description
and idealized tests, J. Atmos. Sci., 72, 287–311, 2015. a
Oleson, K. W., Niu, G. Y., Yang, Z. L., Lawrence, D. M., Thornton, P. E., Lawrence, P. J., Stöckli, R., Dickinson, R. E., Bonan, G. B., Levis, S., and Dai, A.: Improvements to
the Community Land Model and their impact on the hydrological cycle, J.
Geophys. Res.-Biogeo., 113, G01021, https://doi.org/10.1029/2007JG000563, 2008. a
Peralta, C., Ben Bouallègue, Z., Theis, S., Gebhardt, C., and Buchhold,
M.:
Accounting for initial condition uncertainties in COSMO-DE-EPS, J.
Geophys. Res.-Atmos., 117, D07108, https://doi.org/10.1029/2011JD016581, 2012. a
Poll, S., Shrestha, P., and Simmer, C.: Modelling convectively induced
secondary circulations in the terra incognita with TerrSysMP, Q.
J. Roy. Meteor. Soc., 143, 2352–2361, 2017. a
Poll, S., Shrestha, P., and Simmer, C.: Grid resolution dependency of land
surface heterogeneity effects on boundary-layer structure, Q.
J. Roy. Meteor. Soc., 148, 141–158, 2022. a
Putaud, J. P., Van Dingenen, R., Alastuey, A., Bauer, H., Birmili, W., Cyrys, J., Flentje, H., Fuzzi, S., Gehrig, R., Hansson, H. C., and Harrison, R. M.: A European
aerosol phenomenology–3: Physical and chemical characteristics of
particulate matter from 60 rural, urban, and kerbside sites across Europe,
Atmos. Environ., 44, 1308–1320, 2010. a
Ramsauer, T., Weiß, T., and Marzahn, P.: Comparison of the GPM IMERG
final
precipitation product to RADOLAN weather radar data over the
topographically
and climatically diverse Germany, Remote Sensing, 10, 2029, https://doi.org/10.3390/rs10122029, 2018. a
Rieger, D., Bangert, M., Kottmeier, C., Vogel, H., and Vogel, B.: Impact of
aerosol on post-frontal convective clouds over Germany, Tellus B, 66,
22528, https://doi.org/10.3402/tellusb.v66.22528, 2014. a
Rosenfeld, D.: Suppression of rain and snow by urban and industrial air
pollution, science, 287, 1793–1796, 2000. a
Rosenfeld, D., Lohmann, U., Raga, G. B., O'Dowd, C. D., Kulmala, M., Fuzzi,
S.,
Reissell, A., and Andreae, M. O.: Flood or drought: how do aerosols affect
precipitation?, Science, 321, 1309–1313, 2008. a
Rosenfeld, D., Andreae, M. O., Asmi, A., Chin, M., de Leeuw, G., Donovan, D. P., Kahn, R., Kinne, S., Kivekäs, N., Kulmala, M., and Lau, W.: Global
observations of aerosol-cloud-precipitation-climate interactions, Rev.
Geophys., 52, 750–808, 2014. a
Ryzhkov, A.: Interpretation of Polarimetric Radar Covariance Matrix for
Meteorological Scatterers: Theoretical Analysis, J. Atmos.
Ocean. Tech., 18, 315–328,
https://doi.org/10.1175/1520-0426(2001)018<0315:IOPRCM>2.0.CO;2,
2001. a
Ryzhkov, A., Pinsky, M., Pokrovsky, A., and Khain, A.: Polarimetric Radar
Observation Operator for a Cloud Model with Spectral Microphysics, J.
Appl. Meteorol. Clim., 50, 873–894,
https://doi.org/10.1175/2010JAMC2363.1, 2011. a, b, c
Ryzhkov, A. V. and Zrnic, D. S.: Polarimetric Characteristics of Deep
Convective Storms, in: Radar Polarimetry for Weather Observations,
269–307, Springer, https://doi.org/10.1007/978-3-030-05093-1_8, 2019. a, b, c
Ryzhkov, A. V., Kumjian, M. R., Ganson, S. M., and Khain, A. P.:
Polarimetric
Radar Characteristics of Melting Hail. Part I: Theoretical Simulations
Using
Spectral Microphysical Modeling, J. Appl. Meteorol.
Clim., 52, 2849–2870, https://doi.org/10.1175/JAMC-D-13-073.1, 2013. a
Seifert, A. and Beheng, K. D.: A two-moment cloud microphysics
parameterization
for mixed-phase clouds. Part 1: Model description, Meteorol.
Atmos. Phys., 92, 45–66, 2006. 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
Shrestha, P.: High resolution hydrological simulations over Bonn Radar Domain, CRC/TR32 Database (TR32DB) [data set], https://doi.org/10.5880/TR32DB.40, 2021b. a
Shrestha, P.: Pre-processing and analysis of the TSMP-ART and polarimetric rdar data: v1.0 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7246808, 2022. a
Shrestha, P. and Barros, A. P.: Joint spatial variability of aerosol, clouds
and rainfall in the Himalayas from satellite data, Atmos. Chem. Phys., 10,
8305–8317, https://doi.org/10.5194/acp-10-8305-2010, 2010. a
Shrestha, P., Barros, A. P., and Khlystov, A.: CCN estimates from bulk
hygroscopic growth factors of ambient aerosols during the pre-monsoon
season
over Central Nepal, Atmos. Environ., 67, 120–129, 2013. a
Shrestha, P., Mendrok, J., Pejcic, V., Trömel, S., Blahak, U., and Carlin, J.
T.: Evaluation of the COSMO model (v5.1) in polarimetric radar space – impact
of uncertainties in model microphysics, retrievals and forward operators,
Geosci. Model Dev., 15, 291–313, https://doi.org/10.5194/gmd-15-291-2022, 2022. a
Snyder, J. C., Bluestein, H. B., Zhang, G., and Frasier, S. J.: Attenuation
correction and hydrometeor classification of high-resolution, X-band,
dual-polarized mobile radar measurements in severe convective storms, J.
Atmos. Ocean. Tech., 27, 1979–2001, 2010. a
Snyder, J. C., Ryzhkov, A. V., Kumjian, M. R., Khain, A. P., and Picca, J.: A
Z
DR column detection algorithm to examine convective storm updrafts, Weather
Forecast., 30, 1819–1844, 2015. a
Snyder, J. C., Bluestein, H. B., Dawson II, D. T., and Jung, Y.: Simulations of
Polarimetric, X-Band Radar Signatures in Supercells. Part II: ZDR Columns
and
Rings and KDP Columns, J. Appl. Meteorol. Climatol., 56,
2001–2026, https://doi.org/10.1175/JAMC-D-16-0139.1, 2017b. a, b
Steppeler, J., Doms, G., Schättler, U., Bitzer, H., Gassmann, A.,
Damrath,
U., and Gregoric, G.: Meso-gamma scale forecasts using the nonhydrostatic
model LM, Meteorol. Atmos. Phys., 82, 75–96, 2003. a
Stevens, B. and Feingold, G.: Untangling aerosol effects on clouds and
precipitation in a buffered system, Nature, 461, 607–613, 2009. a
Storer, R., Van den Heever, S., and L'Ecuyer, T.: Observations of
aerosol-induced convective invigoration in the tropical east Atlantic,
J. Geophys. Res.-Atmos., 119, 3963–3975, 2014. a
Storer, R. L., Van Den Heever, S. C., and Stephens, G. L.: Modeling aerosol
impacts on convective storms in different environments, J.
Atmos. Sci., 67, 3904–3915, 2010. a
Tao, W.-K., Li, X., Khain, A., Matsui, T., Lang, S., and Simpson, J.: Role of
atmospheric aerosol concentration on deep convective precipitation:
Cloud-resolving model simulations, J. Geophys. Res.-Atmos.,
112, D24S18, https://doi.org/10.1029/2007JD008728, 2007. a
Tao, W.-K., Chen, J.-P., Li, Z., Wang, C., and Zhang, C.: Impact of aerosols
on
convective clouds and precipitation, Rev. Geophys., 50, RG2001, https://doi.org/10.1029/2011RG000369, 2012. a
Trömel, S., Kumjian, M. R., Ryzhkov, A. V., Simmer, C., and Diederich,
M.:
Backscatter differential phase—Estimation and variability, J.
Appl. Meteorol. Clim., 52, 2529–2548, 2013. a
Trömel, S., Simmer, C., Blahak, U., Blanke, A., Doktorowski, S., Ewald, F.,
Frech, M., Gergely, M., Hagen, M., Janjic, T., Kalesse-Los, H., Kneifel, S.,
Knote, C., Mendrok, J., Moser, M., Köcher, G., Mühlbauer, K., Myagkov, A.,
Pejcic, V., Seifert, P., Shrestha, P., Teisseire, A., von Terzi, L., Tetoni,
E., Vogl, T., Voigt, C., Zeng, Y., Zinner, T., and Quaas, J.: Overview:
Fusion of radar polarimetry and numerical atmospheric modelling towards an
improved understanding of cloud and precipitation processes, Atmos. Chem.
Phys., 21, 17291–17314, https://doi.org/10.5194/acp-21-17291-2021, 2021. a, b, c
Vogel, B., Vogel, H., Bäumer, D., Bangert, M., Lundgren, K., Rinke, R., and
Stanelle, T.: The comprehensive model system COSMO-ART – Radiative impact of
aerosol on the state of the atmosphere on the regional scale, Atmos. Chem.
Phys., 9, 8661–8680, https://doi.org/10.5194/acp-9-8661-2009, 2009 (data available at: https://www.imk-tro.kit.edu/english/5224.php, last access: 27 October 2022).
a, b, c
Wolfensberger, D. and Berne, A.: From model to radar variables: a new forward
polarimetric radar operator for COSMO, Atmos. Meas. Tech., 11, 3883–3916,
https://doi.org/10.5194/amt-11-3883-2018, 2018. a
Yuan, T., Remer, L. A., Pickering, K. E., and Yu, H.: Observational evidence
of
aerosol enhancement of lightning activity and convective invigoration,
Geophys. Res. Lett., 38, L04701, https://doi.org/10.1029/2010GL046052, 2011. a
Yuter, S. E. and Houze Jr., R. A.: Three-dimensional kinematic and
microphysical
evolution of Florida cumulonimbus. Part II: Frequency distributions of
vertical velocity, reflectivity, and differential reflectivity, Mon.
Weather Rev., 123, 1941–1963, 1995. a
Zeng, Y., Blahak, U., and Jerger, D.: An efficient modular volume-scanning
radar forward operator for NWP models: description and coupling to the
COSMO
model, Q. J. Roy. Meteor. Soc., 142,
3234–3256, https://doi.org/10.1002/qj.2904, 2016. a
Zhang, Y., Fan, J., Li, Z., and Rosenfeld, D.: Impacts of cloud microphysics
parameterizations on simulated aerosol–cloud interactions for deep convective
clouds over Houston, Atmos. Chem. Phys., 21, 2363–2381,
https://doi.org/10.5194/acp-21-2363-2021, 2021. a, b
Short summary
The study extends the Terrestrial Systems Modeling Platform with gas-phase chemistry aerosol dynamics and a radar forward operator to enable detailed studies of aerosol–cloud–precipitation interactions. This is demonstrated using a case study of a deep convective storm, which showed that the strong updraft in the convective core of the storm produced aerosol-tower-like features, which affected the size of the hydrometeors and the simulated polarimetric features (e.g., ZDR and KDP columns).
The study extends the Terrestrial Systems Modeling Platform with gas-phase chemistry aerosol...
Altmetrics
Final-revised paper
Preprint