Articles | Volume 18, issue 12
https://doi.org/10.5194/gmd-18-3965-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-18-3965-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
01 Jul 2025
Development and technical paper |
| 01 Jul 2025
| 01 Jul 2025
Numerical investigations on the modelling of ultrafine particles in SSH-aerosol-v1.3a: size resolution and redistribution
CEREA, École des Ponts, Institut Polytechnique de Paris, EdF R&D, IPSL, Marne-la-Vallée, France
CEREA, École des Ponts, Institut Polytechnique de Paris, EdF R&D, IPSL, Marne-la-Vallée, France
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Soo-Jin Park, Lya Lugon, Oscar Jacquot, Youngseob Kim, Alexia Baudic, Barbara D'Anna, Ludovico Di Antonio, Claudia Di Biagio, Fabrice Dugay, Olivier Favez, Véronique Ghersi, Aline Gratien, Julien Kammer, Jean-Eudes Petit, Olivier Sanchez, Myrto Valari, Jérémy Vigneron, and Karine Sartelet
Atmos. Chem. Phys., 25, 3363–3387, https://doi.org/10.5194/acp-25-3363-2025, https://doi.org/10.5194/acp-25-3363-2025, 2025
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To accurately represent the population exposure to outdoor concentrations of pollutants of interest to health (NO2, PM2.5, black carbon, and ultrafine particles), multi-scale modelling down to the street scale is set up and evaluated using measurements from field campaigns. An exposure scaling factor is defined, allowing regional-scale simulations to be corrected to evaluate population exposure. Urban heterogeneities strongly influence NO2, black carbon, and ultrafine particles but less strongly PM2.5.
Karine Sartelet, Zhizhao Wang, Youngseob Kim, Victor Lannuque, and Florian Couvidat
EGUsphere, https://doi.org/10.5194/egusphere-2025-2191, https://doi.org/10.5194/egusphere-2025-2191, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
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SSH-aerosol v2 simulates the evolution of primary and secondary pollutants via gas-phase chemistry, aerosol dynamics (including ultrafine particles), and intra-particle reactions. It uses a sectional approach for size and composition, includes a wall-loss module, and links gas-phase mechanisms of different complexity to secondary organic aerosol formation. Representation of particle phase composition allows viscosity and non-ideality to be taken into account.
Soo-Jin Park, Lya Lugon, Oscar Jacquot, Youngseob Kim, Alexia Baudic, Barbara D'Anna, Ludovico Di Antonio, Claudia Di Biagio, Fabrice Dugay, Olivier Favez, Véronique Ghersi, Aline Gratien, Julien Kammer, Jean-Eudes Petit, Olivier Sanchez, Myrto Valari, Jérémy Vigneron, and Karine Sartelet
Atmos. Chem. Phys., 25, 3363–3387, https://doi.org/10.5194/acp-25-3363-2025, https://doi.org/10.5194/acp-25-3363-2025, 2025
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To accurately represent the population exposure to outdoor concentrations of pollutants of interest to health (NO2, PM2.5, black carbon, and ultrafine particles), multi-scale modelling down to the street scale is set up and evaluated using measurements from field campaigns. An exposure scaling factor is defined, allowing regional-scale simulations to be corrected to evaluate population exposure. Urban heterogeneities strongly influence NO2, black carbon, and ultrafine particles but less strongly PM2.5.
Alexis Squarcioni, Yelva Roustan, Myrto Valari, Youngseob Kim, Karine Sartelet, Lya Lugon, Fabrice Dugay, and Robin Voitot
Atmos. Chem. Phys., 25, 93–117, https://doi.org/10.5194/acp-25-93-2025, https://doi.org/10.5194/acp-25-93-2025, 2025
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This study highlights the interest of using a street-network model to estimate pollutant concentrations of NOx, NO2, and PM2.5 in heterogeneous urban areas, particularly those adjacent to highways, compared with the subgrid-scale approach embedded in the 3D Eulerian model CHIMERE. However, the study also reveals comparable performances between the two approaches for the aforementioned pollutants in areas near the city center, where urban characteristics are more uniform.
Victor Lannuque and Karine Sartelet
Atmos. Chem. Phys., 24, 8589–8606, https://doi.org/10.5194/acp-24-8589-2024, https://doi.org/10.5194/acp-24-8589-2024, 2024
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Large uncertainties remain in understanding secondary organic aerosol (SOA) formation and speciation from naphthalene oxidation. This study details the development of the first near-explicit chemical scheme for naphthalene oxidation by OH, which includes kinetic and mechanistic data, and is able to reproduce most of the experimentally identified products in both gas and particle phases.
Alice Maison, Lya Lugon, Soo-Jin Park, Alexia Baudic, Christopher Cantrell, Florian Couvidat, Barbara D'Anna, Claudia Di Biagio, Aline Gratien, Valérie Gros, Carmen Kalalian, Julien Kammer, Vincent Michoud, Jean-Eudes Petit, Marwa Shahin, Leila Simon, Myrto Valari, Jérémy Vigneron, Andrée Tuzet, and Karine Sartelet
Atmos. Chem. Phys., 24, 6011–6046, https://doi.org/10.5194/acp-24-6011-2024, https://doi.org/10.5194/acp-24-6011-2024, 2024
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This study presents the development of a bottom-up inventory of urban tree biogenic emissions. Emissions are computed for each tree based on their location and characteristics and are integrated in the regional air quality model WRF-CHIMERE. The impact of these biogenic emissions on air quality is quantified for June–July 2022. Over Paris city, urban trees increase the concentrations of particulate organic matter by 4.6 %, of PM2.5 by 0.6 %, and of ozone by 1.0 % on average over 2 months.
Evangelia Kostenidou, Baptiste Marques, Brice Temime-Roussel, Yao Liu, Boris Vansevenant, Karine Sartelet, and Barbara D'Anna
Atmos. Chem. Phys., 24, 2705–2729, https://doi.org/10.5194/acp-24-2705-2024, https://doi.org/10.5194/acp-24-2705-2024, 2024
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Secondary organic aerosol (SOA) from gasoline vehicles can be a significant source of particulate matter in urban areas. Here the chemical composition of secondary volatile organic compounds and SOA produced by photo-oxidation of Euro 5 gasoline vehicle emissions was studied. The volatility of the SOA formed was calculated. Except for the temperature and the concentration of the aerosol, additional parameters may play a role in the gas-to-particle partitioning.
Victor Lannuque, Barbara D'Anna, Evangelia Kostenidou, Florian Couvidat, Alvaro Martinez-Valiente, Philipp Eichler, Armin Wisthaler, Markus Müller, Brice Temime-Roussel, Richard Valorso, and Karine Sartelet
Atmos. Chem. Phys., 23, 15537–15560, https://doi.org/10.5194/acp-23-15537-2023, https://doi.org/10.5194/acp-23-15537-2023, 2023
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Large uncertainties remain in understanding secondary organic aerosol (SOA) formation from toluene oxidation. In this study, speciation measurements in gaseous and particulate phases were carried out, providing partitioning and volatility data on individual toluene SOA components at different temperatures. A new detailed oxidation mechanism was developed to improve modeled speciation, and effects of different processes involved in gas–particle partitioning at the molecular scale are explored.
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev., 16, 5281–5303, https://doi.org/10.5194/gmd-16-5281-2023, https://doi.org/10.5194/gmd-16-5281-2023, 2023
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A new version of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is developed to represent heterogeneities of concentrations in streets. The street volume is discretized vertically and horizontally to limit the artificial dilution of emissions and concentrations. This new version is applied to street networks in Copenhagen and Paris. The comparisons to observations are improved, with higher concentrations of pollutants emitted by traffic at the bottom of the street.
Chao Lin, Yunyi Wang, Ryozo Ooka, Cédric Flageul, Youngseob Kim, Hideki Kikumoto, Zhizhao Wang, and Karine Sartelet
Atmos. Chem. Phys., 23, 1421–1436, https://doi.org/10.5194/acp-23-1421-2023, https://doi.org/10.5194/acp-23-1421-2023, 2023
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In this study, SSH-aerosol, a modular box model that simulates the evolution of gas, primary, and secondary aerosols, is coupled with the computational fluid dynamics (CFD) software, OpenFOAM and Code_Saturne. The transient dispersion of pollutants emitted from traffic in a street canyon of Greater Paris is simulated. The coupled model achieved better agreement in NO2 and PM10 with measurement data than the conventional CFD simulation which regards pollutants as passive scalars.
Zhizhao Wang, Florian Couvidat, and Karine Sartelet
Geosci. Model Dev., 15, 8957–8982, https://doi.org/10.5194/gmd-15-8957-2022, https://doi.org/10.5194/gmd-15-8957-2022, 2022
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Air quality models need to reliably predict secondary organic aerosols (SOAs) at a reasonable computational cost. Thus, we developed GENOA v1.0, a mechanism reduction algorithm that preserves the accuracy of detailed gas-phase chemical mechanisms for SOA formation, thereby improving the practical use of actual chemistry in SOA models. With GENOA, a near-explicit chemical scheme was reduced to 2 % of its original size and computational time, with an average error of less than 3 %.
Youngseob Kim, Lya Lugon, Alice Maison, Thibaud Sarica, Yelva Roustan, Myrto Valari, Yang Zhang, Michel André, and Karine Sartelet
Geosci. Model Dev., 15, 7371–7396, https://doi.org/10.5194/gmd-15-7371-2022, https://doi.org/10.5194/gmd-15-7371-2022, 2022
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This paper presents the latest version of the street-network model MUNICH, v2.0. The description of MUNICH v1.0, which models gas-phase pollutants in a street network, was published in GMD in 2018. Since then, major modifications have been made to MUNICH. The comprehensive aerosol model SSH-aerosol is now coupled to MUNICH to simulate primary and secondary aerosol concentrations. New parameterisations have also been introduced. Test cases are defined to illustrate the new model functionalities.
Alice Maison, Cédric Flageul, Bertrand Carissimo, Yunyi Wang, Andrée Tuzet, and Karine Sartelet
Atmos. Chem. Phys., 22, 9369–9388, https://doi.org/10.5194/acp-22-9369-2022, https://doi.org/10.5194/acp-22-9369-2022, 2022
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This paper presents a parameterization of the tree crown effect on air flow and pollutant dispersion in a street network model used to simulate air quality at the street level. The new parameterization is built using a finer-scale model (computational fluid dynamics). The tree effect increases with the leaf area index and the crown volume fraction of the trees; the street horizontal velocity is reduced by up to 68 % and the vertical transfer into or out of the street by up to 23 %.
Karine Sartelet, Youngseob Kim, Florian Couvidat, Maik Merkel, Tuukka Petäjä, Jean Sciare, and Alfred Wiedensohler
Atmos. Chem. Phys., 22, 8579–8596, https://doi.org/10.5194/acp-22-8579-2022, https://doi.org/10.5194/acp-22-8579-2022, 2022
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A methodology is defined to estimate number emissions from an inventory providing mass emissions. Number concentrations are simulated over Greater Paris using different nucleation parameterisations (binary, ternary involving sulfuric acid and ammonia, and heteromolecular involving sulfuric acid and extremely low-volatility organics, ELVOCs). The comparisons show that ternary nucleation may not be a dominant process for new particle formation in cities, but they stress the role of ELVOCs.
Boris Vansevenant, Cédric Louis, Corinne Ferronato, Ludovic Fine, Patrick Tassel, Pascal Perret, Evangelia Kostenidou, Brice Temime-Roussel, Barbara D'Anna, Karine Sartelet, Véronique Cerezo, and Yao Liu
Atmos. Meas. Tech., 14, 7627–7655, https://doi.org/10.5194/amt-14-7627-2021, https://doi.org/10.5194/amt-14-7627-2021, 2021
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A new method was developed to correct wall losses of particles on Teflon walls using a new environmental chamber. It was applied to experiments with six diesel vehicles (Euro 3 to 6), tested on a chassis dynamometer. Emissions of particles and precursors were obtained under urban and motorway conditions. The chamber experiments help understand the role of physical processes in diesel particle evolutions in the dark. These results can be applied to situations such as tunnels or winter rush hours.
Lya Lugon, Jérémy Vigneron, Christophe Debert, Olivier Chrétien, and Karine Sartelet
Geosci. Model Dev., 14, 7001–7019, https://doi.org/10.5194/gmd-14-7001-2021, https://doi.org/10.5194/gmd-14-7001-2021, 2021
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The multiscale Street-in-Grid model is used to simulate black carbon (BC) concentrations in streets. To respect street-surface mass balance, particle resuspension is estimated with a new approach based on deposited mass. The contribution of resuspension is low, but non-exhaust emissions from tyre wear may largely contribute to BC concentrations. The impact of the two-way dynamic coupling between scales on BC concentrations varies depending on the street geometry and traffic emission intensity.
Eve-Agnès Fiorentino, Henri Wortham, and Karine Sartelet
Geosci. Model Dev., 14, 2747–2780, https://doi.org/10.5194/gmd-14-2747-2021, https://doi.org/10.5194/gmd-14-2747-2021, 2021
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Indoor air quality (IAQ) is strongly influenced by reactivity with surfaces, which is called heterogeneous reactivity. To date, this reactivity is barely integrated into numerical models due to the strong uncertainties it is subjected to. In this work, an open-source IAQ model, called the H2I model, is developed to consider both gas-phase and heterogeneous reactivity and simulate indoor concentrations of inorganic compounds.
Eve-Agnès Fiorentino, Henri Wortham, and Karine Sartelet
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-192, https://doi.org/10.5194/gmd-2020-192, 2020
Preprint withdrawn
Lya Lugon, Karine Sartelet, Youngseob Kim, Jérémy Vigneron, and Olivier Chrétien
Atmos. Chem. Phys., 20, 7717–7740, https://doi.org/10.5194/acp-20-7717-2020, https://doi.org/10.5194/acp-20-7717-2020, 2020
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This study presents a new version of the multi-scale model Street-in-Grid (SinG) that interconnects regional and local scales in air-quality modeling in urban areas. The new version of SinG performs the finest coupling between transport and chemistry, leading to a numerically stable partitioning between NO and NO2. Multi-scale, local-scale and regional-scale simulations of NO, NO2 and NOx over Paris are compared to observations, and SinG shows good performance for both local and regional scales.
Marwa Majdi, Karine Sartelet, Grazia Maria Lanzafame, Florian Couvidat, Youngseob Kim, Mounir Chrit, and Solene Turquety
Atmos. Chem. Phys., 19, 5543–5569, https://doi.org/10.5194/acp-19-5543-2019, https://doi.org/10.5194/acp-19-5543-2019, 2019
Youngseob Kim, Karine Sartelet, and Florian Couvidat
Atmos. Chem. Phys., 19, 1241–1261, https://doi.org/10.5194/acp-19-1241-2019, https://doi.org/10.5194/acp-19-1241-2019, 2019
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Assumptions (ideality and thermodynamic equilibrium) commonly made in 3-dimensional air quality models were reconsidered to evaluate their impacts on secondary organic aerosol (SOA) formation. Non-ideality (short-, medium- and long-range interactions of organics and inorganics) influences SOA concentrations by about 30 % over Europe. If SOA are highly viscous rather than inviscid, hydrophobic SOA concentrations increase by 6 % but can increase by an order of magnitude for volatile compounds.
Marwa Majdi, Solene Turquety, Karine Sartelet, Carole Legorgeu, Laurent Menut, and Youngseob Kim
Atmos. Chem. Phys., 19, 785–812, https://doi.org/10.5194/acp-19-785-2019, https://doi.org/10.5194/acp-19-785-2019, 2019
Mounir Chrit, Karine Sartelet, Jean Sciare, Marwa Majdi, José Nicolas, Jean-Eudes Petit, and François Dulac
Atmos. Chem. Phys., 18, 18079–18100, https://doi.org/10.5194/acp-18-18079-2018, https://doi.org/10.5194/acp-18-18079-2018, 2018
Mounir Chrit, Karine Sartelet, Jean Sciare, Jorge Pey, José B. Nicolas, Nicolas Marchand, Evelyn Freney, Karine Sellegri, Matthias Beekmann, and François Dulac
Atmos. Chem. Phys., 18, 9631–9659, https://doi.org/10.5194/acp-18-9631-2018, https://doi.org/10.5194/acp-18-9631-2018, 2018
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Fine particulate matter (PM) in the atmosphere is of concern due to its effects on health, climate, ecosystems and biological cycles, and visibility.
These effects are especially important in the Mediterranean region. In this study, the air quality model Polyphemus is used to understand the
sources of inorganic and organic particles in the western Mediterranean and evaluate the uncertainties linked to the model parameters and hypotheses related to condensation/evaporation in the model.
Arineh Cholakian, Matthias Beekmann, Augustin Colette, Isabelle Coll, Guillaume Siour, Jean Sciare, Nicolas Marchand, Florian Couvidat, Jorge Pey, Valerie Gros, Stéphane Sauvage, Vincent Michoud, Karine Sellegri, Aurélie Colomb, Karine Sartelet, Helen Langley DeWitt, Miriam Elser, André S. H. Prévot, Sonke Szidat, and François Dulac
Atmos. Chem. Phys., 18, 7287–7312, https://doi.org/10.5194/acp-18-7287-2018, https://doi.org/10.5194/acp-18-7287-2018, 2018
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In this work, four schemes for the simulation of organic aerosols in the western Mediterranean basin are added to the CHIMERE chemistry–transport model; the resulting simulations are then compared to measurements obtained from ChArMEx. It is concluded that the scheme taking into account the fragmentation and the formation of nonvolatile organic aerosols corresponds better to measurements; the major source of this aerosol in the western Mediterranean is found to be of biogenic origin.
Evelyn Freney, Karine Sellegri, Mounir Chrit, Kouji Adachi, Joel Brito, Antoine Waked, Agnès Borbon, Aurélie Colomb, Régis Dupuy, Jean-Marc Pichon, Laetitia Bouvier, Claire Delon, Corinne Jambert, Pierre Durand, Thierry Bourianne, Cécile Gaimoz, Sylvain Triquet, Anaïs Féron, Matthias Beekmann, François Dulac, and Karine Sartelet
Atmos. Chem. Phys., 18, 7041–7056, https://doi.org/10.5194/acp-18-7041-2018, https://doi.org/10.5194/acp-18-7041-2018, 2018
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The focus of these experiments, within the ChArMEx project, were to better understand the chemical properties of ambient aerosols over the Mediterranean region. A series of airborne measurements were performed aboard the French research aircraft, the ATR42, during the summer period. Aerosol and gas-phase chemical mass spectrometry allowed us to understand the sources and formation of organic aerosols. Numerical models were incorporated into this study to help interpret our observations.
Mounir Chrit, Karine Sartelet, Jean Sciare, Jorge Pey, Nicolas Marchand, Florian Couvidat, Karine Sellegri, and Matthias Beekmann
Atmos. Chem. Phys., 17, 12509–12531, https://doi.org/10.5194/acp-17-12509-2017, https://doi.org/10.5194/acp-17-12509-2017, 2017
Kevin Berland, Clémence Rose, Jorge Pey, Anais Culot, Evelyn Freney, Nikolaos Kalivitis, Giorgios Kouvarakis, José Carlos Cerro, Marc Mallet, Karine Sartelet, Matthias Beckmann, Thierry Bourriane, Greg Roberts, Nicolas Marchand, Nikolaos Mihalopoulos, and Karine Sellegri
Atmos. Chem. Phys., 17, 9567–9583, https://doi.org/10.5194/acp-17-9567-2017, https://doi.org/10.5194/acp-17-9567-2017, 2017
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New particle formation (NPF) from gas-phase precursors is a process that is expected to drive the total number concentration of particles in the atmosphere. Here we use measurements performed simultaneously in Corsica, Crete and Mallorca to show that the spatial extent of the NPF events are several hundreds of kilometers large. Airborne measurements additionally show that nanoparticles in the marine atmosphere can either be of marine origin or from higher altitudes above the continent.
Jovanna Arndt, Jean Sciare, Marc Mallet, Greg C. Roberts, Nicolas Marchand, Karine Sartelet, Karine Sellegri, François Dulac, Robert M. Healy, and John C. Wenger
Atmos. Chem. Phys., 17, 6975–7001, https://doi.org/10.5194/acp-17-6975-2017, https://doi.org/10.5194/acp-17-6975-2017, 2017
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The chemical composition of individual PM2.5 particles was measured at a background site on Corsica in the Mediterranean to determine the contribution of different sources to background aerosol in the region. Most of the particles were from fossil fuel combustion and biomass burning, transported to the site from France, Italy and eastern Europe, and also accumulated other species en route. This work shows that largest impact on air quality in the Mediterranean is from anthropogenic emissions.
C. Di Biagio, L. Doppler, C. Gaimoz, N. Grand, G. Ancellet, J.-C. Raut, M. Beekmann, A. Borbon, K. Sartelet, J.-L. Attié, F. Ravetta, and P. Formenti
Atmos. Chem. Phys., 15, 9611–9630, https://doi.org/10.5194/acp-15-9611-2015, https://doi.org/10.5194/acp-15-9611-2015, 2015
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Observations from this study indicate that continental pollution largely affects the atmospheric composition and structure of the western Mediterranean basin. Pollution plumes reach 3000-4000 m in altitude and present a very complex and highly stratified structure, characterized by fresh and aged layers both in the boundary layer and in the free troposphere. Also we report the observations of high levels of ultrafine particles over the basin, possibly linked to new particle formation events.
S. Zhu, K. N. Sartelet, and C. Seigneur
Geosci. Model Dev., 8, 1595–1612, https://doi.org/10.5194/gmd-8-1595-2015, https://doi.org/10.5194/gmd-8-1595-2015, 2015
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This article presents the Size-Composition Resolved Aerosol Model (SCRAM) for simulating the dynamics of externally mixed atmospheric particles. The model is first validated by comparison with a reference solution and with results of simulations using internally mixed particles. Then, the importance of representing the mixing state when modelling atmospheric aerosol concentrations is investigated in a box model simulation using data representative of air pollution in Greater Paris.
F. Couvidat and K. Sartelet
Geosci. Model Dev., 8, 1111–1138, https://doi.org/10.5194/gmd-8-1111-2015, https://doi.org/10.5194/gmd-8-1111-2015, 2015
Y. Wang, K. N. Sartelet, M. Bocquet, P. Chazette, M. Sicard, G. D'Amico, J. F. Léon, L. Alados-Arboledas, A. Amodeo, P. Augustin, J. Bach, L. Belegante, I. Binietoglou, X. Bush, A. Comerón, H. Delbarre, D. García-Vízcaino, J. L. Guerrero-Rascado, M. Hervo, M. Iarlori, P. Kokkalis, D. Lange, F. Molero, N. Montoux, A. Muñoz, C. Muñoz, D. Nicolae, A. Papayannis, G. Pappalardo, J. Preissler, V. Rizi, F. Rocadenbosch, K. Sellegri, F. Wagner, and F. Dulac
Atmos. Chem. Phys., 14, 12031–12053, https://doi.org/10.5194/acp-14-12031-2014, https://doi.org/10.5194/acp-14-12031-2014, 2014
Y. Wang, K. N. Sartelet, M. Bocquet, and P. Chazette
Atmos. Chem. Phys., 14, 3511–3532, https://doi.org/10.5194/acp-14-3511-2014, https://doi.org/10.5194/acp-14-3511-2014, 2014
Y. Zhang, K. Sartelet, S.-Y. Wu, and C. Seigneur
Atmos. Chem. Phys., 13, 6807–6843, https://doi.org/10.5194/acp-13-6807-2013, https://doi.org/10.5194/acp-13-6807-2013, 2013
Y. Zhang, K. Sartelet, S. Zhu, W. Wang, S.-Y. Wu, X. Zhang, K. Wang, P. Tran, C. Seigneur, and Z.-F. Wang
Atmos. Chem. Phys., 13, 6845–6875, https://doi.org/10.5194/acp-13-6845-2013, https://doi.org/10.5194/acp-13-6845-2013, 2013
A. Waked, C. Seigneur, F. Couvidat, Y. Kim, K. Sartelet, C. Afif, A. Borbon, P. Formenti, and S. Sauvage
Atmos. Chem. Phys., 13, 5873–5886, https://doi.org/10.5194/acp-13-5873-2013, https://doi.org/10.5194/acp-13-5873-2013, 2013
E. Solazzo, R. Bianconi, G. Pirovano, M. D. Moran, R. Vautard, C. Hogrefe, K. W. Appel, V. Matthias, P. Grossi, B. Bessagnet, J. Brandt, C. Chemel, J. H. Christensen, R. Forkel, X. V. Francis, A. B. Hansen, S. McKeen, U. Nopmongcol, M. Prank, K. N. Sartelet, A. Segers, J. D. Silver, G. Yarwood, J. Werhahn, J. Zhang, S. T. Rao, and S. Galmarini
Geosci. Model Dev., 6, 791–818, https://doi.org/10.5194/gmd-6-791-2013, https://doi.org/10.5194/gmd-6-791-2013, 2013
F. Couvidat, Y. Kim, K. Sartelet, C. Seigneur, N. Marchand, and J. Sciare
Atmos. Chem. Phys., 13, 983–996, https://doi.org/10.5194/acp-13-983-2013, https://doi.org/10.5194/acp-13-983-2013, 2013
Y. Wang, K. N. Sartelet, M. Bocquet, and P. Chazette
Atmos. Chem. Phys., 13, 269–283, https://doi.org/10.5194/acp-13-269-2013, https://doi.org/10.5194/acp-13-269-2013, 2013
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The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Similarity-based analysis of atmospheric organic compounds for machine learning applications
Porting the Meso-NH atmospheric model on different GPU architectures for the next generation of supercomputers (version MESONH-v55-OpenACC)
Estimation of aerosol and cloud radiative heating rate in the tropical stratosphere using a radiative kernel method
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
Generalized local fractions – a method for the calculation of sensitivities to emissions from multiple sources for chemically active species, illustrated using the EMEP MSC-W model (rv5.5)
SanDyPALM v1.0: Static and Dynamic Drivers for the PALM-4U Model to Facilitate Realistic Urban Microclimate Simulations
An enhanced emission module for the PALM model system 23.10 with application for PM10 emission from urban domestic heating
Identifying lightning processes in ERA5 soundings with deep learning
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Implementation of a dry deposition module (DEPAC v3.11) in a large eddy simulation code (DALES v4.4)
Accurate and fast prediction of radioactive pollution by Kriging coupled with Auto-Associative Models
Mitigating Hail Overforecasting in the 2-Moment Milbrandt-Yau Microphysics Scheme (v2.25.2_beta_04) in WRF (v4.5.1) by Incorporating the Graupel Spongy Wet Growth Process (MY2_GSWG v1.0)
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Gunho Loren Oh and Philip H. Austin
Geosci. Model Dev., 18, 3921–3940, https://doi.org/10.5194/gmd-18-3921-2025, https://doi.org/10.5194/gmd-18-3921-2025, 2025
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It is difficult to study the behaviour of a cloud field due to internal fluctuations and observational noise. We perform a high-resolution simulation of the boundary-layer cloud field and introduce statistical and numerical techniques, including machine-learning models, to study the evolution of the cloud field, which shows a periodic behaviour. We aim to use the numerical techniques to identify the underlying behaviour within noisy observations.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev., 18, 3819–3855, https://doi.org/10.5194/gmd-18-3819-2025, https://doi.org/10.5194/gmd-18-3819-2025, 2025
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre- and sub-kilometre-scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and an improved representation of clouds and visibility.
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025, https://doi.org/10.5194/gmd-18-3781-2025, 2025
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Aerosol data assimilation has gained popularity as it combines the advantages of modelling and observation. However, few studies have addressed the challenges in the prior vertical structure. Different observations are assimilated to examine the sensitivity of assimilation to vertical structure. Results show that assimilation can optimize the dust field in general. However, if the prior introduces an incorrect structure, the assimilation can significantly deteriorate the integrity of the aerosol profile.
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025, https://doi.org/10.5194/gmd-18-3707-2025, 2025
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This work focuses on the prediction of aerosol concentration values at the ground level, which are a strong indicator of air quality, using artificial neural networks. A study of different variables and their efficiency as inputs for these models is also proposed and reveals that the best results are obtained when using all of them. Comparison between network architectures and information fusion methods allows for the extraction of knowledge on the most efficient methods in the context of this study.
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025, https://doi.org/10.5194/gmd-18-3681-2025, 2025
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Tuning a climate model means adjusting uncertain parameters in the model to best match observations like the global radiation balance and cloud cover. This is usually done by running many simulations of the model with different settings, which can be time-consuming and relies heavily on expert knowledge. To make this process faster and more objective, we developed a machine learning emulator to create a large ensemble and apply a method called history matching to find the best settings.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025, https://doi.org/10.5194/gmd-18-3623-2025, 2025
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This study combines machine learning with concentration-weighted trajectory analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025, https://doi.org/10.5194/gmd-18-3607-2025, 2025
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We developed a deep learning method to estimate CO2 emissions from power plants using satellite images. Trained and validated on simulated data, our model accurately predicts emissions despite challenges like cloud cover. When applied to real OCO3 satellite images, the results closely match reported emissions. This study shows that neural networks trained on simulations can effectively analyse real satellite data, offering a new way to monitor CO2 emissions from space.
Wonbae Bang, Jacob T. Carlin, Kwonil Kim, Alexander V. Ryzhkov, Guosheng Liu, and GyuWon Lee
Geosci. Model Dev., 18, 3559–3581, https://doi.org/10.5194/gmd-18-3559-2025, https://doi.org/10.5194/gmd-18-3559-2025, 2025
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Microphysics model-based diagnosis, such as the spectral bin model (SBM), has recently been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM has a relatively higher accuracy for dry-snow and wet-snow events, whereas it has lower accuracy for rain events. When the microphysics scheme in the SBM was optimized for the corresponding region, the accuracy for rain events improved.
Gabriel Colas, Valéry Masson, François Bouttier, Ludovic Bouilloud, Laura Pavan, and Virve Karsisto
Geosci. Model Dev., 18, 3453–3472, https://doi.org/10.5194/gmd-18-3453-2025, https://doi.org/10.5194/gmd-18-3453-2025, 2025
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In winter, snow- and ice-covered artificial surfaces are important aspects of the urban climate. They may influence the magnitude of the urban heat island effect, but this is still unclear. In this study, we improved the representation of the snow and ice cover in the Town Energy Balance (TEB) urban climate model. Evaluations have shown that the results are promising for using TEB to study the climate of cold cities.
Markus Kunze, Christoph Zülicke, Tarique A. Siddiqui, Claudia C. Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev., 18, 3359–3385, https://doi.org/10.5194/gmd-18-3359-2025, https://doi.org/10.5194/gmd-18-3359-2025, 2025
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with an upper-atmospheric extension with the physics package for numerical weather prediction (UA-ICON(NWP)). We optimized the parameters for the gravity wave parameterizations and achieved realistic modeling of the thermal and dynamic states of the mesopause regions. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025, https://doi.org/10.5194/gmd-18-3311-2025, 2025
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Reducing emissions of methane, a powerful greenhouse gas, is a top policy concern for mitigating anthropogenic climate change. The Integrated Methane Inversion (IMI) is an advanced, cloud-based software that translates satellite observations into actionable emissions data. Here we present IMI version 2.0 with vastly expanded capabilities. These updates enable a wider range of scientific and stakeholder applications from individual basin to global scales with continuous emissions monitoring.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
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The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025, https://doi.org/10.5194/gmd-18-3211-2025, 2025
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This study proposes using a statistical model to freeze errors due to differences in environmental forcing when evaluating the surface turbulent heat fluxes from numerical simulations with observations. The statistical model is first built with observations and then applied to the simulated environment to generate possibly observed fluxes. This novel method provides insight into differently evaluating the numerical formulation of turbulent heat fluxes with a long period of observational data.
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025, https://doi.org/10.5194/gmd-18-3175-2025, 2025
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This paper presents the result of many years of effort of the author, who developed an original mathematical numerical model of heat and moisture exchange processes in soil, vegetation, and snow. The author relied on her 30 years of research experience in atmospheric numerical modelling. The presented model is the fruit of the author's research on physical processes at the surface–atmosphere interface and their numerical approximation and aims at improving numerical weather forecasting and climate simulations.
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025, https://doi.org/10.5194/gmd-18-3065-2025, 2025
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We developed ClimKern, a Python package and radiative kernel repository, to simplify calculating radiative feedbacks and make climate sensitivity studies more reproducible. Testing of ClimKern with sample climate model data reveals that radiative kernel choice may be more important than previously thought, especially in polar regions. Our work highlights the need for kernel sensitivity analyses to be included in future studies.
Matti Niskanen, Aku Seppänen, Henri Oikarinen, Miska Olin, Panu Karjalainen, Santtu Mikkonen, and Kari Lehtinen
Geosci. Model Dev., 18, 2983–3001, https://doi.org/10.5194/gmd-18-2983-2025, https://doi.org/10.5194/gmd-18-2983-2025, 2025
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Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Johann Rasmus Nüß, Nikos Daskalakis, Fabian Günther Piwowarczyk, Angelos Gkouvousis, Oliver Schneising, Michael Buchwitz, Maria Kanakidou, Maarten C. Krol, and Mihalis Vrekoussis
Geosci. Model Dev., 18, 2861–2890, https://doi.org/10.5194/gmd-18-2861-2025, https://doi.org/10.5194/gmd-18-2861-2025, 2025
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We estimate carbon monoxide emissions through inverse modeling, an approach where measurements of tracers in the atmosphere are fed to a model to calculate backwards in time (inverse) where the tracers came from. We introduce measurements from a new satellite instrument and show that, in most places globally, these on their own sufficiently constrain the emissions. This alleviates the need for additional datasets, which could shorten the delay for future carbon monoxide source estimates.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
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This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Hilda Sandström and Patrick Rinke
Geosci. Model Dev., 18, 2701–2724, https://doi.org/10.5194/gmd-18-2701-2025, https://doi.org/10.5194/gmd-18-2701-2025, 2025
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Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
Juan Escobar, Philippe Wautelet, Joris Pianezze, Florian Pantillon, Thibaut Dauhut, Christelle Barthe, and Jean-Pierre Chaboureau
Geosci. Model Dev., 18, 2679–2700, https://doi.org/10.5194/gmd-18-2679-2025, https://doi.org/10.5194/gmd-18-2679-2025, 2025
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The Meso-NH weather research code is adapted for GPUs using OpenACC, leading to significant performance and energy efficiency improvements. Called MESONH-v55-OpenACC, it includes enhanced memory management, communication optimizations and a new solver. On the AMD MI250X Adastra platform, it achieved up to 6× speedup and 2.3× energy efficiency gain compared to CPUs. Storm simulations at 100 m resolution show positive results, positioning the code for future use on exascale supercomputers.
Jie Gao, Yi Huang, Jonathon S. Wright, Ke Li, Tao Geng, and Qiurun Yu
Geosci. Model Dev., 18, 2569–2586, https://doi.org/10.5194/gmd-18-2569-2025, https://doi.org/10.5194/gmd-18-2569-2025, 2025
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The aerosol in the upper troposphere and stratosphere is highly variable, and its radiative effect is poorly understood. To estimate this effect, the radiative kernel is constructed and applied. The results show that the kernels can reproduce aerosol radiative effects and are expected to simulate stratospheric aerosol radiative effects. This approach reduces computational expense, is consistent with radiative model calculations, and can be applied to atmospheric models with speed requirements.
Ji Won Yoon, Seungyeon Lee, Ebony Lee, and Seon Ki Park
Geosci. Model Dev., 18, 2303–2328, https://doi.org/10.5194/gmd-18-2303-2025, https://doi.org/10.5194/gmd-18-2303-2025, 2025
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This study evaluates the Weather Research and Forecasting Model (WRF) coupled with Chemistry (WRF-Chem) to predict a mega Asian dust storm (ADS) over South Korea on 28–29 March 2021. We assessed combinations of five dust emission and four land surface schemes by analyzing meteorological and air quality variables. The best scheme combination reduced the root mean square error (RMSE) for particulate matter 10 (PM10) by up to 29.6 %, demonstrating the highest performance.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
Geosci. Model Dev., 18, 2231–2248, https://doi.org/10.5194/gmd-18-2231-2025, https://doi.org/10.5194/gmd-18-2231-2025, 2025
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The effectiveness of this assimilation system and its sensitivity to the ensemble member size and length of the assimilation window are investigated. This study advances our understanding of the selection of basic parameters in the four-dimensional local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate-matter-polluted environment.
Jens Peter Karolus Wenceslaus Frankemölle, Johan Camps, Pieter De Meutter, and Johan Meyers
Geosci. Model Dev., 18, 1989–2003, https://doi.org/10.5194/gmd-18-1989-2025, https://doi.org/10.5194/gmd-18-1989-2025, 2025
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To detect anomalous radioactivity in the environment, it is paramount that we understand the natural background level. In this work, we propose a statistical model to describe the most likely background level and the associated uncertainty in a network of dose rate detectors. We train, verify, and validate the model using real environmental data. Using the model, we show that we can correctly predict the background level in a subset of the detector network during a known
anomalous event.
Jean-François Grailet, Robin J. Hogan, Nicolas Ghilain, David Bolsée, Xavier Fettweis, and Marilaure Grégoire
Geosci. Model Dev., 18, 1965–1988, https://doi.org/10.5194/gmd-18-1965-2025, https://doi.org/10.5194/gmd-18-1965-2025, 2025
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The MAR (Modèle Régional Atmosphérique) is a regional climate model used for weather forecasting and studying the climate over various regions. This paper presents an update of MAR thanks to which it can precisely decompose solar radiation, in particular in the UV (ultraviolet) and photosynthesis ranges, both being critical to human health and ecosystems. As a first application of this new capability, this paper presents a method for predicting UV indices with MAR.
Yi-Ning Shi, Jun Yang, Wei Han, Lujie Han, Jiajia Mao, Wanlin Kan, and Fuzhong Weng
Geosci. Model Dev., 18, 1947–1964, https://doi.org/10.5194/gmd-18-1947-2025, https://doi.org/10.5194/gmd-18-1947-2025, 2025
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Direct assimilation of observations from ground-based microwave radiometers (GMRs) holds significant potential for improving forecast accuracy. Radiative transfer models (RTMs) play a crucial role in direct data assimilation. In this study, we introduce a new RTM, the Advanced Radiative Transfer Modeling System – Ground-Based (ARMS-gb), designed to simulate brightness temperatures observed by GMRs along with their Jacobians. Several enhancements have been incorporated to achieve higher accuracy.
R. Phani Murali Krishna, Siddharth Kumar, A. Gopinathan Prajeesh, Peter Bechtold, Nils Wedi, Kumar Roy, Malay Ganai, B. Revanth Reddy, Snehlata Tirkey, Tanmoy Goswami, Radhika Kanase, Sahadat Sarkar, Medha Deshpande, and Parthasarathi Mukhopadhyay
Geosci. Model Dev., 18, 1879–1894, https://doi.org/10.5194/gmd-18-1879-2025, https://doi.org/10.5194/gmd-18-1879-2025, 2025
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The High-Resolution Global Forecast Model (HGFM) is an advanced iteration of the operational Global Forecast System (GFS) model. HGFM can produce forecasts at a spatial scale of ~6 km in tropics. It demonstrates improved accuracy in short- to medium-range weather prediction over the Indian region, with notable success in predicting extreme events. Further, the model will be entrusted to operational forecasting agencies after validation and testing.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev., 18, 1851–1878, https://doi.org/10.5194/gmd-18-1851-2025, https://doi.org/10.5194/gmd-18-1851-2025, 2025
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Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rain. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and the model skill is then evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with four open-source models.
Andrew Geiss and Po-Lun Ma
Geosci. Model Dev., 18, 1809–1827, https://doi.org/10.5194/gmd-18-1809-2025, https://doi.org/10.5194/gmd-18-1809-2025, 2025
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Particles in the Earth's atmosphere strongly impact the planet's energy budget, and atmosphere simulations require accurate representation of their interaction with light. This work introduces two approaches to represent light scattering by small particles. The first is a scattering simulator based on Mie theory implemented in Python. The second is a neural network emulator that is more accurate than existing methods and is fast enough to be used in climate and weather simulations.
Andrin Jörimann, Timofei Sukhodolov, Beiping Luo, Gabriel Chiodo, Graham Mann, and Thomas Peter
EGUsphere, https://doi.org/10.5194/egusphere-2025-145, https://doi.org/10.5194/egusphere-2025-145, 2025
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Aerosol particles in the stratosphere affect our climate. Climate models therefore need an accurate description of their properties and evolution. Satellites measure how strongly aerosol particles extinguish light passing through the stratosphere. We describe a method to use such aerosol extinction data to retrieve the number and sizes of the aerosol particles and calculate their optical effects. The resulting data sets for models are validated against ground-based and balloon observations.
Qin Wang, Bo Zeng, Gong Chen, and Yaoting Li
Geosci. Model Dev., 18, 1769–1784, https://doi.org/10.5194/gmd-18-1769-2025, https://doi.org/10.5194/gmd-18-1769-2025, 2025
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This study evaluates the performance of four planetary boundary layer (PBL) schemes in near-surface wind fields over the Sichuan Basin, China. Using 112 sensitivity experiments with the Weather Research and Forecasting (WRF) model and focusing on 28 wind events, it is found that wind direction was less sensitive to the PBL schemes. The quasi-normal scale elimination (QNSE) scheme captured temporal variations best, while the Mellor–Yamada–Janjić (MYJ) scheme had the least error in wind speed.
Tai-Long He, Nikhil Dadheech, Tammy M. Thompson, and Alexander J. Turner
Geosci. Model Dev., 18, 1661–1671, https://doi.org/10.5194/gmd-18-1661-2025, https://doi.org/10.5194/gmd-18-1661-2025, 2025
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It is computationally expensive to infer greenhouse gas (GHG) emissions using atmospheric observations. This is partly due to the detailed model used to represent atmospheric transport. We demonstrate how a machine learning (ML) model can be used to simulate high-resolution atmospheric transport. This type of ML model will help estimate GHG emissions using dense observations, which are becoming increasingly common with the proliferation of urban monitoring networks and geostationary satellites.
Wei Li, Beiming Tang, Patrick C. Campbell, Youhua Tang, Barry Baker, Zachary Moon, Daniel Tong, Jianping Huang, Kai Wang, Ivanka Stajner, and Raffaele Montuoro
Geosci. Model Dev., 18, 1635–1660, https://doi.org/10.5194/gmd-18-1635-2025, https://doi.org/10.5194/gmd-18-1635-2025, 2025
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The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
Yanwei Zhu, Aitor Atencia, Markus Dabernig, and Yong Wang
Geosci. Model Dev., 18, 1545–1559, https://doi.org/10.5194/gmd-18-1545-2025, https://doi.org/10.5194/gmd-18-1545-2025, 2025
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
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
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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.
Astrid Kerkweg, Timo Kirfel, Duong H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
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Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Peter Wind and Willem van Caspel
EGUsphere, https://doi.org/10.5194/egusphere-2024-3571, https://doi.org/10.5194/egusphere-2024-3571, 2025
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This paper presents a numerical method to assess the origin of air pollution. Combined with a numerical air pollution transport and chemistry model, it can follow the contributions from a large number of emission sources. The result is a series of maps that give the relative contributions from for example all European countries at each point.
Julian Vogel, Sebastian Stadler, Ganesh Chockalingam, Afshin Afshari, Johanna Henning, and Matthias Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2025-144, https://doi.org/10.5194/egusphere-2025-144, 2025
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This study presents a toolkit to simplify input data creation for the urban microclimate model PALM-4U. It introduces novel methods to automate the use of open data sources. Our analysis of four test cases created from different geographic data sources shows variations in temperature, humidity, and wind speed, influenced by data quality. Validation indicates that the automated methods yield results comparable to expert-driven approaches, facilitating user-friendly urban climate modeling.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
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As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Leon Geers, Ruud Janssen, Gudrun Thorkelsdottir, Jordi Vilà-Guerau de Arellano, and Martijn Schaap
EGUsphere, https://doi.org/10.5194/egusphere-2025-426, https://doi.org/10.5194/egusphere-2025-426, 2025
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High-resolution data on reactive nitrogen deposition are needed to inform cost-effective policies. Here, we describe the implementation of a dry deposition module into a large eddy simulation code. With this model, we are able to represent the turbulent exchange of tracers at the hectometer resolution. The model calculates the dispersion and deposition of NOx and NH3 in great spatial detail, clearly showing the influence of local land use patterns.
Raphaël Périllat, Sylvain Girard, and Irène Korsakissok
EGUsphere, https://doi.org/10.5194/egusphere-2024-3838, https://doi.org/10.5194/egusphere-2024-3838, 2025
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We developed a method to improve decision-making during nuclear crises by predicting the spread of radiation more efficiently. Existing approaches are often too slow, especially when analyzing complex data like radiation maps. Our method combines techniques to simplify these maps and predict them quickly using statistical tools. This approach could help authorities respond faster and more accurately in emergencies, reducing risks to the population and the environment.
Shaofeng Hua, Gang Chen, Baojun Chen, Mingshan Li, and Xin Xu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3834, https://doi.org/10.5194/egusphere-2024-3834, 2025
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Hail forecasting using numerical models remains a challenge. In this study, we found that the commonly used graupel-to-hail conversion parameterization method led to hail overforecasting in heavy rainfall cases where no hail was observed. By incorporating the spongy wet growth process, we successfully mitigated hail overforecasting. The modified scheme also produced hail in real hail events. This research contributes to a better understanding of hail formation.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3512, https://doi.org/10.5194/egusphere-2024-3512, 2025
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Non-thermal emission from chemical reactions in the Earth's middle und upper atmosphere strongly contributes to the brightness of the night sky below about 2.3 µm. The new Paranal Airglow Line and Continuum Emission model calculates the emission spectrum and its variability with an unprecedented accuracy. Relying on a large spectroscopic data set from astronomical spectrographs and theoretical molecular/atomic data, it is valuable for airglow research and astronomical observatories.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
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Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
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Short summary
Modelling the size distribution and the number concentration is important to represent ultrafine particles. A new analytic formulation is presented to compute coagulation partition coefficients, allowing us to lower the numerical diffusion associated with the resolution of aerosol dynamics. The significance of this effect is assessed in a 0D box model and over greater Paris with a chemistry transport model, using different size resolutions of the particle distribution.
Modelling the size distribution and the number concentration is important to represent ultrafine...
