Articles | Volume 12, issue 3
https://doi.org/10.5194/gmd-12-979-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-979-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
15 Mar 2019
Model description paper |
| 15 Mar 2019
| 15 Mar 2019
Development of a dynamic dust source map for NMME-DREAM v1.0 model based on MODIS Normalized Difference Vegetation Index (NDVI) over the Arabian Peninsula
Stavros Solomos
Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing (IAASARS), National Observatory of Athens, Athens, Greece
Department of Geography and Urban Planning, National Space Science and Technology Center, United Arab Emirates University, United Arab Emirates
Abdelgadir Abuelgasim
CORRESPONDING AUTHOR
Department of Geography and Urban Planning, National Space Science and Technology Center, United Arab Emirates University, United Arab Emirates
Christos Spyrou
Department of Geography, Harokopio University of Athens (HUA), El. Venizelou Str. 70, 17671 Athens, Greece
Ioannis Binietoglou
National Institute of R&D for Optoelectronics, 409 Atomiştilor Str., Magurele 77125, Romania
Slobodan Nickovic
Republic Hydrometeorological Service of Serbia, 11000 Belgrade, Serbia
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Akriti Masoom, Ilias Fountoulakis, Stelios Kazadzis, Ioannis-Panagiotis Raptis, Anna Kampouri, Basil E. Psiloglou, Dimitra Kouklaki, Kyriakoula Papachristopoulou, Eleni Marinou, Stavros Solomos, Anna Gialitaki, Dimitra Founda, Vasileios Salamalikis, Dimitris Kaskaoutis, Natalia Kouremeti, Nikolaos Mihalopoulos, Vassilis Amiridis, Andreas Kazantzidis, Alexandros Papayannis, Christos S. Zerefos, and Kostas Eleftheratos
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Dust orientation in the Earth's atmosphere has been an ongoing investigation in recent years, and its potential proof will be a paradigm shift for dust remote sensing. We have designed and developed a polarization lidar that provides direct measurements of dust orientation, as well as more detailed information of the particle microphysics. We provide a description of its design as well as its first measurements.
Abderrazak Bannari and Abdelgader Abuelgasim
SOIL Discuss., https://doi.org/10.5194/soil-2021-55, https://doi.org/10.5194/soil-2021-55, 2021
Manuscript not accepted for further review
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The study aims to analyze the ability of vegetation indices (VI’s) to map soil salt contents compared to the potential of evaporite mineral indices (EMI). The method used is based on simulated and satellite data acquired over two study areas: Kuwait-State and Omongwa salt-pan in Namibia. The obtained results demonstrated that it is impossible for VI’s to discriminate or to predict soil salinity. While, the EMI performed very well for the salt-affected soil classes mapping.
Anna Gialitaki, Alexandra Tsekeri, Vassilis Amiridis, Romain Ceolato, Lucas Paulien, Anna Kampouri, Antonis Gkikas, Stavros Solomos, Eleni Marinou, Moritz Haarig, Holger Baars, Albert Ansmann, Tatyana Lapyonok, Anton Lopatin, Oleg Dubovik, Silke Groß, Martin Wirth, Maria Tsichla, Ioanna Tsikoudi, and Dimitris Balis
Atmos. Chem. Phys., 20, 14005–14021, https://doi.org/10.5194/acp-20-14005-2020, https://doi.org/10.5194/acp-20-14005-2020, 2020
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Stratospheric smoke particles are found to significantly depolarize incident light, while this effect is also accompanied by a strong spectral dependence. We utilize scattering simulations to show that this behaviour can be attributed to the near-spherical shape of the particles. We also examine whether an extension of the current AERONET scattering model to include the near-spherical shapes could be of benefit to the AERONET retrieval for stratospheric smoke associated with enhanced PLDR.
Nikolaos Papagiannopoulos, Giuseppe D'Amico, Anna Gialitaki, Nicolae Ajtai, Lucas Alados-Arboledas, Aldo Amodeo, Vassilis Amiridis, Holger Baars, Dimitris Balis, Ioannis Binietoglou, Adolfo Comerón, Davide Dionisi, Alfredo Falconieri, Patrick Fréville, Anna Kampouri, Ina Mattis, Zoran Mijić, Francisco Molero, Alex Papayannis, Gelsomina Pappalardo, Alejandro Rodríguez-Gómez, Stavros Solomos, and Lucia Mona
Atmos. Chem. Phys., 20, 10775–10789, https://doi.org/10.5194/acp-20-10775-2020, https://doi.org/10.5194/acp-20-10775-2020, 2020
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Volcanic and desert dust particles affect human activities in manifold ways; consequently, mitigation tools are important. Their early detection and the issuance of early warnings are key elements in the initiation of operational response procedures. A methodology for the early warning of these hazards using European Aerosol Research Lidar Network (EARLINET) data is presented. The tailored product is investigated during a volcanic eruption and mineral dust advected in the eastern Mediterranean.
Emmanouil Proestakis, Vassilis Amiridis, Eleni Marinou, Ioannis Binietoglou, Albert Ansmann, Ulla Wandinger, Julian Hofer, John Yorks, Edward Nowottnick, Abduvosit Makhmudov, Alexandros Papayannis, Aleksander Pietruczuk, Anna Gialitaki, Arnoud Apituley, Artur Szkop, Constantino Muñoz Porcar, Daniele Bortoli, Davide Dionisi, Dietrich Althausen, Dimitra Mamali, Dimitris Balis, Doina Nicolae, Eleni Tetoni, Gian Luigi Liberti, Holger Baars, Ina Mattis, Iwona Sylwia Stachlewska, Kalliopi Artemis Voudouri, Lucia Mona, Maria Mylonaki, Maria Rita Perrone, Maria João Costa, Michael Sicard, Nikolaos Papagiannopoulos, Nikolaos Siomos, Pasquale Burlizzi, Rebecca Pauly, Ronny Engelmann, Sabur Abdullaev, and Gelsomina Pappalardo
Atmos. Chem. Phys., 19, 11743–11764, https://doi.org/10.5194/acp-19-11743-2019, https://doi.org/10.5194/acp-19-11743-2019, 2019
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To increase accuracy and validate satellite-based products, comparison with ground-based reference observations is required. To do this, we present evaluation activity of EARLINET for the qualitative and quantitative assessment of NASA's CATS lidar operating aboard the International Space Station (ISS) while identified discrepancies are discussed. Better understanding CATS performance and limitations provides a valuable basis for scientific studies implementing the satellite-based lidar system.
Eleni Marinou, Matthias Tesche, Athanasios Nenes, Albert Ansmann, Jann Schrod, Dimitra Mamali, Alexandra Tsekeri, Michael Pikridas, Holger Baars, Ronny Engelmann, Kalliopi-Artemis Voudouri, Stavros Solomos, Jean Sciare, Silke Groß, Florian Ewald, and Vassilis Amiridis
Atmos. Chem. Phys., 19, 11315–11342, https://doi.org/10.5194/acp-19-11315-2019, https://doi.org/10.5194/acp-19-11315-2019, 2019
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We assess the feasibility of ground-based and spaceborne lidars to retrieve profiles of cloud-relevant aerosol concentrations and ice-nucleating particles. The retrieved profiles are in good agreement with airborne in situ measurements. Our methodology will be applied to satellite observations in the future so as to provide a global 3D product of cloud-relevant properties.
Nikolaos Papagiannopoulos, Lucia Mona, Aldo Amodeo, Giuseppe D'Amico, Pilar Gumà Claramunt, Gelsomina Pappalardo, Lucas Alados-Arboledas, Juan Luís Guerrero-Rascado, Vassilis Amiridis, Panagiotis Kokkalis, Arnoud Apituley, Holger Baars, Anja Schwarz, Ulla Wandinger, Ioannis Binietoglou, Doina Nicolae, Daniele Bortoli, Adolfo Comerón, Alejandro Rodríguez-Gómez, Michaël Sicard, Alex Papayannis, and Matthias Wiegner
Atmos. Chem. Phys., 18, 15879–15901, https://doi.org/10.5194/acp-18-15879-2018, https://doi.org/10.5194/acp-18-15879-2018, 2018
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A stand-alone automatic method for typing observations of the European Aerosol Research Lidar Network (EARLINET) is presented. The method compares the observations to model distributions that were constructed using EARLINET pre-classified data. The algorithm’s versatility and adaptability makes it suitable for network-wide typing studies.
Doina Nicolae, Jeni Vasilescu, Camelia Talianu, Ioannis Binietoglou, Victor Nicolae, Simona Andrei, and Bogdan Antonescu
Atmos. Chem. Phys., 18, 14511–14537, https://doi.org/10.5194/acp-18-14511-2018, https://doi.org/10.5194/acp-18-14511-2018, 2018
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A new aerosol typing algorithm based on artificial neural networks (ANNs) has been developed. The algorithm is providing the most probable aerosol type based on EARLINET LIDAR profiles. The ANNs used by the algorithm were trained using synthetic data, for which a new aerosol model has been developed. Blind tests on EARLINET data samples showed the capability of the algorithm to retrieve the aerosol type from a large variety of data, with different quality and physical content.
Antonis Gkikas, Vincenzo Obiso, Carlos Pérez García-Pando, Oriol Jorba, Nikos Hatzianastassiou, Lluis Vendrell, Sara Basart, Stavros Solomos, Santiago Gassó, and José Maria Baldasano
Atmos. Chem. Phys., 18, 8757–8787, https://doi.org/10.5194/acp-18-8757-2018, https://doi.org/10.5194/acp-18-8757-2018, 2018
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The present study investigates the direct radiative effects (DREs), induced during 20 intense Mediterranean desert dust outbreaks, based on regional short-term numerical simulations of the NMMB-MONARCH model: more specifically, (i) the DREs and their associated impacts on temperature and surface sensible and latent heat fluxes, (ii) the feedbacks on dust AOD and dust emissions, and (iii) the possible improvements in short-term forecasts (up to 84 h) of temperature and radiation.
Dimitra Mamali, Eleni Marinou, Jean Sciare, Michael Pikridas, Panagiotis Kokkalis, Michael Kottas, Ioannis Binietoglou, Alexandra Tsekeri, Christos Keleshis, Ronny Engelmann, Holger Baars, Albert Ansmann, Vassilis Amiridis, Herman Russchenberg, and George Biskos
Atmos. Meas. Tech., 11, 2897–2910, https://doi.org/10.5194/amt-11-2897-2018, https://doi.org/10.5194/amt-11-2897-2018, 2018
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The paper's scope is to evaluate the performance of in situ atmospheric aerosol instrumentation on board unmanned aerial vehicles (UAVs) and the performance of algorithms used to calculate the aerosol mass from remote sensing instruments by comparing the two independent techniques to each other. Our results indicate that UAV-based aerosol measurements (using specific in situ and remote sensing instrumentation) can provide reliable ways to determine the aerosol mass throughout the atmosphere.
Lev D. Labzovskii, Alexandros Papayannis, Ioannis Binietoglou, Robert F. Banks, Jose M. Baldasano, Florica Toanca, Chris G. Tzanis, and John Christodoulakis
Ann. Geophys., 36, 213–229, https://doi.org/10.5194/angeo-36-213-2018, https://doi.org/10.5194/angeo-36-213-2018, 2018
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This study aims to evaluate synergetic methods for relative humidity vertical profiling based on lidar–radiometer and lidar–simulation combinations. We demonstrate the effectiveness of combined lidar-based methods for relative humidity profiling in comparison with radiometer observations or WRF simulations and assess temperature-related uncertainties in resulting relative humidity profiles. All results are acquired during the HygrA-CD campaign in Athens (Greece) in 2014.
Emmanouil Proestakis, Vassilis Amiridis, Eleni Marinou, Aristeidis K. Georgoulias, Stavros Solomos, Stelios Kazadzis, Julien Chimot, Huizheng Che, Georgia Alexandri, Ioannis Binietoglou, Vasiliki Daskalopoulou, Konstantinos A. Kourtidis, Gerrit de Leeuw, and Ronald J. van der A
Atmos. Chem. Phys., 18, 1337–1362, https://doi.org/10.5194/acp-18-1337-2018, https://doi.org/10.5194/acp-18-1337-2018, 2018
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We provide a 3-D climatology of desert dust aerosols over South and East Asia, based on 9 years of CALIPSO observations and an EARLINET methodology. The results provide the horizontal, vertical and seasonal distribution of dust aerosols over SE Asia along with the change in dust transport pathways. The dataset is unique for its potential applications, including evaluation and assimilation activities in atmospheric simulations and the estimation of the climatic impact of dust aerosols.
Alexandra Tsekeri, Anton Lopatin, Vassilis Amiridis, Eleni Marinou, Julia Igloffstein, Nikolaos Siomos, Stavros Solomos, Panagiotis Kokkalis, Ronny Engelmann, Holger Baars, Myrto Gratsea, Panagiotis I. Raptis, Ioannis Binietoglou, Nikolaos Mihalopoulos, Nikolaos Kalivitis, Giorgos Kouvarakis, Nikolaos Bartsotas, George Kallos, Sara Basart, Dirk Schuettemeyer, Ulla Wandinger, Albert Ansmann, Anatoli P. Chaikovsky, and Oleg Dubovik
Atmos. Meas. Tech., 10, 4995–5016, https://doi.org/10.5194/amt-10-4995-2017, https://doi.org/10.5194/amt-10-4995-2017, 2017
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The Generalized Aerosol Retrieval from Radiometer and Lidar Combined data algorithm (GARRLiC) and the LIdar-Radiometer Inversion Code (LIRIC) provide the opportunity to study the aerosol vertical distribution by combining ground-based lidar and sun-photometric measurements. Here, we utilize the capabilities of both algorithms for the characterization of Saharan dust and marine particles, along with their mixtures, in the south-eastern Mediterranean.
Panagiotis G. Kosmopoulos, Stelios Kazadzis, Michael Taylor, Eleni Athanasopoulou, Orestis Speyer, Panagiotis I. Raptis, Eleni Marinou, Emmanouil Proestakis, Stavros Solomos, Evangelos Gerasopoulos, Vassilis Amiridis, Alkiviadis Bais, and Charalabos Kontoes
Atmos. Meas. Tech., 10, 2435–2453, https://doi.org/10.5194/amt-10-2435-2017, https://doi.org/10.5194/amt-10-2435-2017, 2017
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We study the impact of dust on solar energy using remote sensing data in conjunction with synergistic modelling and forecasting techniques. Under high aerosol loads, we found great solar energy losses of the order of 80 and 50% for concentrated solar power and photovoltaic installations, respectively. The 1-day forecast presented an overall accuracy within 10% in direct comparison to the real conditions under high energy potential, optimising the efficient energy planning and policies.
Eleni Marinou, Vassilis Amiridis, Ioannis Binietoglou, Athanasios Tsikerdekis, Stavros Solomos, Emannouil Proestakis, Dimitra Konsta, Nikolaos Papagiannopoulos, Alexandra Tsekeri, Georgia Vlastou, Prodromos Zanis, Dimitrios Balis, Ulla Wandinger, and Albert Ansmann
Atmos. Chem. Phys., 17, 5893–5919, https://doi.org/10.5194/acp-17-5893-2017, https://doi.org/10.5194/acp-17-5893-2017, 2017
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We provide a 3D multiyear analysis on the evolution of Saharan dust over Europe, using a dust product retrieved from the CALIPSO satellite and using EARLINET methods. The results reveal for the first time the 9-year 3D seasonal patterns of dust over its transport paths from the Sahara towards the Mediterranean. The dataset is unique with respect to its potential applications, including the evaluation of dust models and the estimation of ice nuclei concentration profiles from space.
Jann Schrod, Daniel Weber, Jaqueline Drücke, Christos Keleshis, Michael Pikridas, Martin Ebert, Bojan Cvetković, Slobodan Nickovic, Eleni Marinou, Holger Baars, Albert Ansmann, Mihalis Vrekoussis, Nikos Mihalopoulos, Jean Sciare, Joachim Curtius, and Heinz G. Bingemer
Atmos. Chem. Phys., 17, 4817–4835, https://doi.org/10.5194/acp-17-4817-2017, https://doi.org/10.5194/acp-17-4817-2017, 2017
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In this paper we present data of ice-nucleating particles (INPs) from a 1-month campaign in the Eastern Mediterranean using unmanned aircraft systems (UASs, drones) and offline sampling with subsequent laboratory analysis. To our knowledge, this is the first time INPs were measured onboard a UAS. We find that INP concentrations were 1 magnitude higher aloft than at the ground, highlighting that surface-based measurement of INP may only be of limited significance for the situation at cloud level.
Stavros Solomos, Albert Ansmann, Rodanthi-Elisavet Mamouri, Ioannis Binietoglou, Platon Patlakas, Eleni Marinou, and Vassilis Amiridis
Atmos. Chem. Phys., 17, 4063–4079, https://doi.org/10.5194/acp-17-4063-2017, https://doi.org/10.5194/acp-17-4063-2017, 2017
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An extreme dust storm affected Middle East and the Eastern Mediterranean in September 2015. This event was produced by a combination of meteorological and land-use properties. Analysis with remote sensing observations and modeling simulations reveals (i) transport of warm moist air from the Red and Arabian seas, (ii) formation of a thermal low over Syria, (iii) convective outflows and haboob formation (i.e. propagating dust walls), and (iv) changes in land-use and dust erodibility due to war.
Christos S. Zerefos, Kostas Eleftheratos, John Kapsomenakis, Stavros Solomos, Antje Inness, Dimitris Balis, Alberto Redondas, Henk Eskes, Marc Allaart, Vassilis Amiridis, Arne Dahlback, Veerle De Bock, Henri Diémoz, Ronny Engelmann, Paul Eriksen, Vitali Fioletov, Julian Gröbner, Anu Heikkilä, Irina Petropavlovskikh, Janusz Jarosławski, Weine Josefsson, Tomi Karppinen, Ulf Köhler, Charoula Meleti, Christos Repapis, John Rimmer, Vladimir Savinykh, Vadim Shirotov, Anna Maria Siani, Andrew R. D. Smedley, Martin Stanek, and René Stübi
Atmos. Chem. Phys., 17, 551–574, https://doi.org/10.5194/acp-17-551-2017, https://doi.org/10.5194/acp-17-551-2017, 2017
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The paper makes a convincing case that the Brewer network is capable of detecting enhanced SO2 columns, as observed, e.g., after volcanic eruptions. For this reason, large volcanic eruptions of the past decade have been used to detect and forecast SO2 plumes of volcanic origin using the Brewer and other ground-based networks, aided by satellite, trajectory analysis calculations and modelling.
Alexandra Tsekeri, Vassilis Amiridis, Franco Marenco, Athanasios Nenes, Eleni Marinou, Stavros Solomos, Phil Rosenberg, Jamie Trembath, Graeme J. Nott, James Allan, Michael Le Breton, Asan Bacak, Hugh Coe, Carl Percival, and Nikolaos Mihalopoulos
Atmos. Meas. Tech., 10, 83–107, https://doi.org/10.5194/amt-10-83-2017, https://doi.org/10.5194/amt-10-83-2017, 2017
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The In situ/Remote sensing aerosol Retrieval Algorithm (IRRA) provides vertical profiles of aerosol optical, microphysical and hygroscopic properties from airborne in situ and remote sensing measurements. The algorithm is highly advantageous for aerosol characterization in humid conditions, employing the ISORROPIA II model for acquiring the particle hygroscopic growth. IRRA can find valuable applications in aerosol–cloud interaction schemes and in validation of active space-borne sensors.
Rodanthi-Elisavet Mamouri, Albert Ansmann, Argyro Nisantzi, Stavros Solomos, George Kallos, and Diofantos G. Hadjimitsis
Atmos. Chem. Phys., 16, 13711–13724, https://doi.org/10.5194/acp-16-13711-2016, https://doi.org/10.5194/acp-16-13711-2016, 2016
Slobodan Nickovic, Bojan Cvetkovic, Fabio Madonna, Marco Rosoldi, Goran Pejanovic, Slavko Petkovic, and Jugoslav Nikolic
Atmos. Chem. Phys., 16, 11367–11378, https://doi.org/10.5194/acp-16-11367-2016, https://doi.org/10.5194/acp-16-11367-2016, 2016
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Specific physical and mineralogical properties of desert dust particles cause extremely efficient production of ice crystals in clouds, thus influencing climate and weather even in regions far from dust sources. This study describes a methodology for predicting conditions of cold cloud formation due to dust. This approach required development of an integrated dust-atmospheric modelling system, designed to improve operational forecasts of weather in general, and cloud/precipitation in particular.
María José Granados-Muñoz, Francisco Navas-Guzmán, Juan Luis Guerrero-Rascado, Juan Antonio Bravo-Aranda, Ioannis Binietoglou, Sergio Nepomuceno Pereira, Sara Basart, José María Baldasano, Livio Belegante, Anatoli Chaikovsky, Adolfo Comerón, Giuseppe D'Amico, Oleg Dubovik, Luka Ilic, Panos Kokkalis, Constantino Muñoz-Porcar, Slobodan Nickovic, Doina Nicolae, Francisco José Olmo, Alexander Papayannis, Gelsomina Pappalardo, Alejandro Rodríguez, Kerstin Schepanski, Michaël Sicard, Ana Vukovic, Ulla Wandinger, François Dulac, and Lucas Alados-Arboledas
Atmos. Chem. Phys., 16, 7043–7066, https://doi.org/10.5194/acp-16-7043-2016, https://doi.org/10.5194/acp-16-7043-2016, 2016
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This study provides a detailed overview of the Mediterranean region regarding aerosol microphysical properties during the ChArMEx/EMEP campaign in July 2012. An in-depth analysis of the horizontal, vertical, and temporal dimensions is performed using LIRIC, proving the algorithm's ability in automated retrieval of microphysical property profiles within a network. A validation of four dust models is included, obtaining fair good agreement, especially for the vertical distribution of the aerosol.
N. Huneeus, S. Basart, S. Fiedler, J.-J. Morcrette, A. Benedetti, J. Mulcahy, E. Terradellas, C. Pérez García-Pando, G. Pejanovic, S. Nickovic, P. Arsenovic, M. Schulz, E. Cuevas, J. M. Baldasano, J. Pey, S. Remy, and B. Cvetkovic
Atmos. Chem. Phys., 16, 4967–4986, https://doi.org/10.5194/acp-16-4967-2016, https://doi.org/10.5194/acp-16-4967-2016, 2016
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Five dust models are evaluated regarding their performance in predicting an intense Saharan dust outbreak affecting western and northern Europe (NE). Models predict the onset and evolution of the event for all analysed lead times. On average, differences among the models are larger than differences in lead times for each model. The models tend to underestimate the long-range transport towards NE. This is partly due to difficulties in simulating the vertical dust distribution and horizontal wind.
Nikolaos Papagiannopoulos, Lucia Mona, Lucas Alados-Arboledas, Vassilis Amiridis, Holger Baars, Ioannis Binietoglou, Daniele Bortoli, Giuseppe D'Amico, Aldo Giunta, Juan Luis Guerrero-Rascado, Anja Schwarz, Sergio Pereira, Nicola Spinelli, Ulla Wandinger, Xuan Wang, and Gelsomina Pappalardo
Atmos. Chem. Phys., 16, 2341–2357, https://doi.org/10.5194/acp-16-2341-2016, https://doi.org/10.5194/acp-16-2341-2016, 2016
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Satellite-derived products must undergo data evaluation with reference data sets in order to identify any possible reasons of discrepancy or to assess their representativity. In that direction, data coming from CALIPSO satellite were compared with observations from the ground. We identified a CALIPSO underestimation that could be linked to an assumption in the satellites' algorithms. The proposed correction improves the performance and could enhance aerosol modeling.
G. D'Amico, A. Amodeo, H. Baars, I. Binietoglou, V. Freudenthaler, I. Mattis, U. Wandinger, and G. Pappalardo
Atmos. Meas. Tech., 8, 4891–4916, https://doi.org/10.5194/amt-8-4891-2015, https://doi.org/10.5194/amt-8-4891-2015, 2015
M. Sicard, G. D'Amico, A. Comerón, L. Mona, L. Alados-Arboledas, A. Amodeo, H. Baars, J. M. Baldasano, L. Belegante, I. Binietoglou, J. A. Bravo-Aranda, A. J. Fernández, P. Fréville, D. García-Vizcaíno, A. Giunta, M. J. Granados-Muñoz, J. L. Guerrero-Rascado, D. Hadjimitsis, A. Haefele, M. Hervo, M. Iarlori, P. Kokkalis, D. Lange, R. E. Mamouri, I. Mattis, F. Molero, N. Montoux, A. Muñoz, C. Muñoz Porcar, F. Navas-Guzmán, D. Nicolae, A. Nisantzi, N. Papagiannopoulos, A. Papayannis, S. Pereira, J. Preißler, M. Pujadas, V. Rizi, F. Rocadenbosch, K. Sellegri, V. Simeonov, G. Tsaknakis, F. Wagner, and G. Pappalardo
Atmos. Meas. Tech., 8, 4587–4613, https://doi.org/10.5194/amt-8-4587-2015, https://doi.org/10.5194/amt-8-4587-2015, 2015
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In the framework of the ACTRIS summer 2012 measurement campaign (8 June–17 July 2012), EARLINET organized and performed a controlled exercise of feasibility to demonstrate its potential to perform operational, coordinated measurements and deliver products in near-real time. The paper describes the measurement protocol and discusses the delivery of real-time and near-real-time lidar-derived products.
I. Binietoglou, S. Basart, L. Alados-Arboledas, V. Amiridis, A. Argyrouli, H. Baars, J. M. Baldasano, D. Balis, L. Belegante, J. A. Bravo-Aranda, P. Burlizzi, V. Carrasco, A. Chaikovsky, A. Comerón, G. D'Amico, M. Filioglou, M. J. Granados-Muñoz, J. L. Guerrero-Rascado, L. Ilic, P. Kokkalis, A. Maurizi, L. Mona, F. Monti, C. Muñoz-Porcar, D. Nicolae, A. Papayannis, G. Pappalardo, G. Pejanovic, S. N. Pereira, M. R. Perrone, A. Pietruczuk, M. Posyniak, F. Rocadenbosch, A. Rodríguez-Gómez, M. Sicard, N. Siomos, A. Szkop, E. Terradellas, A. Tsekeri, A. Vukovic, U. Wandinger, and J. Wagner
Atmos. Meas. Tech., 8, 3577–3600, https://doi.org/10.5194/amt-8-3577-2015, https://doi.org/10.5194/amt-8-3577-2015, 2015
V. Amiridis, E. Marinou, A. Tsekeri, U. Wandinger, A. Schwarz, E. Giannakaki, R. Mamouri, P. Kokkalis, I. Binietoglou, S. Solomos, T. Herekakis, S. Kazadzis, E. Gerasopoulos, E. Proestakis, M. Kottas, D. Balis, A. Papayannis, C. Kontoes, K. Kourtidis, N. Papagiannopoulos, L. Mona, G. Pappalardo, O. Le Rille, and A. Ansmann
Atmos. Chem. Phys., 15, 7127–7153, https://doi.org/10.5194/acp-15-7127-2015, https://doi.org/10.5194/acp-15-7127-2015, 2015
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LIVAS is a 3-D multi-wavelength global aerosol and cloud optical database optimized for future space-based lidar end-to-end simulations of realistic atmospheric scenarios as well as retrieval algorithm testing activities. The global database is based on CALIPSO observations at 532nm, while for the conversion at 355nm EARLINET data are utilized.
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
A. Vukovic, M. Vujadinovic, G. Pejanovic, J. Andric, M. R. Kumjian, V. Djurdjevic, M. Dacic, A. K. Prasad, H. M. El-Askary, B. C. Paris, S. Petkovic, S. Nickovic, and W. A. Sprigg
Atmos. Chem. Phys., 14, 3211–3230, https://doi.org/10.5194/acp-14-3211-2014, https://doi.org/10.5194/acp-14-3211-2014, 2014
A. Baklanov, K. Schlünzen, P. Suppan, J. Baldasano, D. Brunner, S. Aksoyoglu, G. Carmichael, J. Douros, J. Flemming, R. Forkel, S. Galmarini, M. Gauss, G. Grell, M. Hirtl, S. Joffre, O. Jorba, E. Kaas, M. Kaasik, G. Kallos, X. Kong, U. Korsholm, A. Kurganskiy, J. Kushta, U. Lohmann, A. Mahura, A. Manders-Groot, A. Maurizi, N. Moussiopoulos, S. T. Rao, N. Savage, C. Seigneur, R. S. Sokhi, E. Solazzo, S. Solomos, B. Sørensen, G. Tsegas, E. Vignati, B. Vogel, and Y. Zhang
Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014, https://doi.org/10.5194/acp-14-317-2014, 2014
S. Nickovic, A. Vukovic, and M. Vujadinovic
Atmos. Chem. Phys., 13, 9169–9181, https://doi.org/10.5194/acp-13-9169-2013, https://doi.org/10.5194/acp-13-9169-2013, 2013
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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
Juan Zhao, Jianping Guo, and Xiaohui Zheng
Geosci. Model Dev., 18, 4075–4101, https://doi.org/10.5194/gmd-18-4075-2025, https://doi.org/10.5194/gmd-18-4075-2025, 2025
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A series of observing system simulation experiments are conducted to assess the impact of multiple radar wind profiler (RWP) networks on convective-scale numerical weather prediction. Results from three southwest-type heavy rainfall cases in the Beijing–Tianjin–Hebei region suggest the added forecast skill of ridge and foothill networks associated with the Taihang Mountains over the existing RWP network. This research provides valuable guidance for designing optimal RWP networks in the region.
Matthias Kohl, Christoph Brühl, Jennifer Schallock, Holger Tost, Patrick Jöckel, Adrian Jost, Steffen Beirle, Michael Höpfner, and Andrea Pozzer
Geosci. Model Dev., 18, 3985–4007, https://doi.org/10.5194/gmd-18-3985-2025, https://doi.org/10.5194/gmd-18-3985-2025, 2025
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SO2 from explosive volcanic eruptions reaching the stratosphere can oxidize and form sulfur aerosols, potentially persisting for several years. We developed a new submodel, Explosive Volcanic ERuptions (EVER), that seamlessly includes stratospheric volcanic SO2 emissions in global numerical simulations based on a novel standard historical model setup, successfully evaluated with satellite observations. Sensitivity studies on the Nabro eruption in 2011 evaluate different emission methods.
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.
Oscar Jacquot and Karine Sartelet
Geosci. Model Dev., 18, 3965–3984, https://doi.org/10.5194/gmd-18-3965-2025, https://doi.org/10.5194/gmd-18-3965-2025, 2025
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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.
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.
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Short summary
In this research we developed a time-dependent dust source map for NMME-DREAM v1.0 model based on the MODIS Normalized Digital Vegetation Index (NDVI). Areas with NDVI < 0.1 are classified as active dust sources. The new modeling system is tested for the analysis of dust particle dispersion over SW Asia using a mesoscale model grid increment of 0.1° × 0.1° km for a period of 1 year. Simulated AOD increased compared to the static dust source approach and there was an increase in dust loads.
In this research we developed a time-dependent dust source map for NMME-DREAM v1.0 model based...
