Articles | Volume 9, issue 2
https://doi.org/10.5194/gmd-9-697-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-9-697-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
18 Feb 2016
Model description paper |
| 18 Feb 2016
ASHEE-1.0: a compressible, equilibrium–Eulerian model for volcanic ash plumes
Scuola Normale Superiore, Pisa, Italy
Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Pisa, Italy
Dipartimento di Matematica, Università degli Studi di Pisa, Pisa, Italy
T. Esposti Ongaro
Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, Pisa, Italy
L. C. Berselli
Dipartimento di Matematica, Università degli Studi di Pisa, Pisa, Italy
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Matteo Trolese, Alessandro Tadini, Laura Pieretti, Damiano Biagini, Spina Cianetti, Simone Colucci, Matteo Cerminara, Claudia D'Oriano, Chiara Montagna, Michele D'Ambrosio, Raffaello Pegna, Giuseppe Re, Francesco Sanseverino, Carlo Giunchi, Carlo Meletti, and Tomaso Esposti Ongaro
EGUsphere, https://doi.org/10.5194/egusphere-2025-3026, https://doi.org/10.5194/egusphere-2025-3026, 2025
This preprint is open for discussion and under review for Geoscience Communication (GC).
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This study describes two hands-on outreach events: an interactive lesson for high-school students during European Researchers’ Night and a tsunami experiment at Lucca Comics & Games. Surveys showed both groups enjoyed the activities, boosted their grasp of geoscience ideas and grew more positive about science. The work emphasizes the effectiveness of quantitative experiment demonstrations and the need to adapt them to the audience, time available and clear educator coordination.
Emmie Malika Bonilauri, Catherine Aaron, Matteo Cerminara, Raphaël Paris, Tomaso Esposti Ongaro, Benedetta Calusi, Domenico Mangione, and Andrew John Lang Harris
Nat. Hazards Earth Syst. Sci., 24, 3789–3813, https://doi.org/10.5194/nhess-24-3789-2024, https://doi.org/10.5194/nhess-24-3789-2024, 2024
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Currently on the island of Stromboli, only 4 min of warning time is available for a locally generated tsunami. We combined tsunami simulations and human exposure to complete a risk analysis. We linked the predicted inundation area and the tsunami warning signals to assess the hazard posed by future tsunamis and to design escape routes to reach safe areas and to optimise evacuation times. Such products can be used by civil protection agencies on Stromboli.
Sara Lenzi, Matteo Cerminara, Mattia de' Michieli Vitturi, Tomaso Esposti Ongaro, and Antonello Provenzale
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-28, https://doi.org/10.5194/gmd-2020-28, 2020
Revised manuscript not accepted
Matteo Trolese, Alessandro Tadini, Laura Pieretti, Damiano Biagini, Spina Cianetti, Simone Colucci, Matteo Cerminara, Claudia D'Oriano, Chiara Montagna, Michele D'Ambrosio, Raffaello Pegna, Giuseppe Re, Francesco Sanseverino, Carlo Giunchi, Carlo Meletti, and Tomaso Esposti Ongaro
EGUsphere, https://doi.org/10.5194/egusphere-2025-3026, https://doi.org/10.5194/egusphere-2025-3026, 2025
This preprint is open for discussion and under review for Geoscience Communication (GC).
Short summary
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This study describes two hands-on outreach events: an interactive lesson for high-school students during European Researchers’ Night and a tsunami experiment at Lucca Comics & Games. Surveys showed both groups enjoyed the activities, boosted their grasp of geoscience ideas and grew more positive about science. The work emphasizes the effectiveness of quantitative experiment demonstrations and the need to adapt them to the audience, time available and clear educator coordination.
Emmie Malika Bonilauri, Catherine Aaron, Matteo Cerminara, Raphaël Paris, Tomaso Esposti Ongaro, Benedetta Calusi, Domenico Mangione, and Andrew John Lang Harris
Nat. Hazards Earth Syst. Sci., 24, 3789–3813, https://doi.org/10.5194/nhess-24-3789-2024, https://doi.org/10.5194/nhess-24-3789-2024, 2024
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Currently on the island of Stromboli, only 4 min of warning time is available for a locally generated tsunami. We combined tsunami simulations and human exposure to complete a risk analysis. We linked the predicted inundation area and the tsunami warning signals to assess the hazard posed by future tsunamis and to design escape routes to reach safe areas and to optimise evacuation times. Such products can be used by civil protection agencies on Stromboli.
Mattia de' Michieli Vitturi, Tomaso Esposti Ongaro, and Samantha Engwell
Geosci. Model Dev., 16, 6309–6336, https://doi.org/10.5194/gmd-16-6309-2023, https://doi.org/10.5194/gmd-16-6309-2023, 2023
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We present version 2 of the numerical code IMEX-SfloW2D. With this version it is possible to simulate a wide range of volcanic mass flows (pyroclastic avalanches, lahars, pyroclastic surges), and here we present its application to transient dilute pyroclastic density currents (PDCs). A simulation of the 1883 Krakatau eruption demonstrates the capability of the numerical model to face a complex natural case involving the propagation of PDCs over the sea surface and across topographic obstacles.
Alessandro Tadini, Andrea Bevilacqua, Augusto Neri, Raffaello Cioni, Giovanni Biagioli, Mattia de'Michieli Vitturi, and Tomaso Esposti Ongaro
Solid Earth, 12, 119–139, https://doi.org/10.5194/se-12-119-2021, https://doi.org/10.5194/se-12-119-2021, 2021
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In this paper we test a simplified numerical model for pyroclastic density currents or PDCs (mixtures of hot gas, lapilli and ash moving across the landscape under the effect of gravity). The aim is quantifying the differences between real and modelled deposits of some PDCs of the 79 CE eruption of Vesuvius, Italy. This step is important because in the paper it is demonstrated that this simplified model is useful for constraining input parameters for more computationally expensive models.
Sara Lenzi, Matteo Cerminara, Mattia de' Michieli Vitturi, Tomaso Esposti Ongaro, and Antonello Provenzale
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-28, https://doi.org/10.5194/gmd-2020-28, 2020
Revised manuscript not accepted
Mattia de' Michieli Vitturi, Tomaso Esposti Ongaro, Giacomo Lari, and Alvaro Aravena
Geosci. Model Dev., 12, 581–595, https://doi.org/10.5194/gmd-12-581-2019, https://doi.org/10.5194/gmd-12-581-2019, 2019
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Pyroclastic avalanches are a type of granular flow generated at active volcanoes by different mechanisms, including the collapse of steep pyroclastic deposits (e.g., scoria and ash cones) and fountaining during moderately explosive eruptions. We present IMEX_SfloW2D, a depth-averaged flow model describing the granular mixture as a single-phase granular fluid. Benchmark cases and preliminary application to the simulation of the 11 February pyroclastic avalanche at Mt. Etna (Italy) are shown.
S. Carcano, L. Bonaventura, T. Esposti Ongaro, and A. Neri
Geosci. Model Dev., 6, 1905–1924, https://doi.org/10.5194/gmd-6-1905-2013, https://doi.org/10.5194/gmd-6-1905-2013, 2013
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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.
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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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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.
Cited articles
Bagheri, G., Bonadonna, C., Manzella, I.,
Pontelandolfo, P., and Haas, P.: Dedicated vertical wind tunnel for the
study of sedimentation of non-spherical particles., Rev. Sci. Instrum., 84,
054501, https://doi.org/10.1063/1.4805019,
2013.
Balachandar, S.: A scaling analysis for point-particle approaches to
turbulent multiphase flows, Int. J. Multiph. Flow, 35, 801–810,
https://doi.org/10.1016/j.ijmultiphaseflow.2009.02.013,
2009.
Balachandar, S. and Eaton, J. K.: Turbulent dispersed multiphase flow, Ann.
Rev. Fluid Mech., 42, 111–133,
https://doi.org/10.1146/annurev.fluid.010908.165243,
2010.
Bardina, J., Ferziger, J. H., and Reynolds, W. C.: Improved Subgrid Scale
Models for Large Eddy Simulation, 1980, American Institute of Aeronautics
and Astronautics, 13th Fluid and Plasma Dynamics Conference, Snowmass, Pap.
No. 80, Colo., 14–16 July, 1980.
Bernardini, M. and Pirozzoli, S.: A general strategy for the optimization of
Runge–Kutta schemes for wave propagation phenomena, J. Comput. Phys., 228,
4182–4199, 2009.
Berselli, L., Iliescu, T., and Layton, W. J.: Mathematics of Large Eddy
Simulation of Turbulent Flows, Springer Science & Business Media,
Springer-Verlag Berlin Heidelberg, Germany, 356 pp., 2005.
Berselli, L. C., Cerminara, M., and Iliescu, T.: Disperse two-phase flows,
with applications to geophysical problems, Pure Appl. Geophys., 172,
181–196,
https://doi.org/10.1007/s00024-014-0889-5,
2015.
Blaisdell, G. A., Mansour, N. N., and Reynolds, W. C.: Numerical Simulation
of Compressible Homogeneous Turbulence, Stanford University, Dept. of Mech.
Eng., Thermosciences Div. Rep. TF-50, 1991.
Boffetta, G., Celani, A., Lillo, F. D., and Musacchio, S.: The Eulerian
description of dilute collisionless suspension, EPL-Europhys. Lett., 78,
14001,
https://doi.org/10.1209/0295-5075/78/14001,
2007.
Bonadonna, C., Macedonio, G., and Sparks, R. S. J.: Numerical modelling of
tephra fallout associated with dome collapses and Vulcanian explosions:
application to hazard assessment on Montserrat, Geol. Soc. London, Mem., 21,
517–537, 2002.
Bürger, R. and Wendland, W. L.: Sedimentation and suspension flows:
historical perspective and some recent developments, J. Eng. Math., 41,
101–116,
https://doi.org/10.1023/A:1011934726111,
2001.
Bursik, M. I.: Effect of wind on the rise height of volcanic plumes,
Geophys. Res. Lett., 28, 3621–3624,
https://doi.org/10.1029/2001GL013393,
2001.
Carcano, S., Bonaventura, L., Esposti Ongaro, T., and Neri, A.: A
semi-implicit, second-order-accurate numerical model for multiphase
underexpanded volcanic jets, Geosci. Model Dev., 6, 1905–1924,
https://doi.org/10.5194/gmd-6-1905-2013, 2013.
Cerminara, M.: The Multiphase Buoyant Plume Solution of the Dusty Gas
Model, in preparation, ArXiV, available at:
http://arxiv.org/abs/1506.01638 (last access: 12 September 2015),
2015.
Cerminara, M.: Multiphase Flows in Volcanology, PhD thesis, Scuola
Normale Superiore, in preparation, 2016.
Cerminara, M., Berselli, L. C., Esposti Ongaro, T., and
Salvetti, M. V.: Direct numerical simulation of a compressible
multiphase flow through the Eulerian approach, in: Direct Large-Eddy
Simul. IX, edited by: Fröhlich, J., Kuerten, H., Geurts, B. J.,
and Armenio, V., ERCOFTAC Series, Springer, Switzerland, 2015.
Cerminara, M., Esposti Ongaro, T., and Neri, A.:
Large eddy simulation of gas–particle kinematic decoupling and turbulent
entrainment in volcanic plumes, J. Volcanol. Geoth. Res., submitted, 2016.
Chacón-Rebollo, T. and Lewandowski, R.: Mathematical and Numerical
Foundations of Turbulence Models, Birkhäuser, New-York, 2013.
Chai, X. and Mahesh, K.: Dynamic-equation model for large-eddy simulation of
compressible flows, J. Fluid Mech., 699, 385–413,
https://doi.org/10.1017/jfm.2012.115, 2012.
Cioni, R., Longo, A., Macedonio, G., Santacroce, R., Sbrana, A.,
Sulpizio, R., and Andronico, D.: Assessing pyroclastic fall hazard through
field data and numerical simulations: example from Vesuvius, J. Geophys.
Res.-Sol. Ea., 108, 1–11,
https://doi.org/10.1029/2001JB000642,
2003.
Clift, R., Grace, J., and Weber, M.: Bubbles, Drops, and Particles,
Academic Press, New York, San Francisco, London, 1978.
Costa A., Folch A., and Macedonio G.: Density-driven transport in the
umbrella region of volcanic clouds: Implications for tephra dispersion
models, Geophys. Res. Lett., 40, 4823–-4827,
https://doi.org/10.1002/grl.50942, 2013.
Costa, A., Suzuki, Y. J., Cerminara, M., Devenish, B. J., Esposti
Ongaro, T., Herzog, M., Van Eaton, A. R., Denby, L., Bursik, M. I., de'
Michieli Vitturi, M., Engwell, S., Neri, A., Barsotti, S., Folch, A.,
Macedonio, G., Girault, F., Carazzo, G., Tait, S., Kaminski, E.,
Mastin, L. G., Woodhouse, M. J., Phillips, J., Hogg, A. J., Degruyter, W.,
and Bonadonna, C.: Overview of the results of the eruption column model
inter-comparison exercise, J. Volcanol. Geoth. Res., submitted, 2015.
Culpo, M.: Current Bottlenecks in the Scalability of OpenFOAM on Massively
Parallel Clusters, Tech. rep., PRACE white papers, available at:
http://www.prace-ri.eu (last access: 14 October 2015), 2011.
Dagna, P.: OpenFOAM on BG/Q Porting and Performance, Tech. rep., CINECA,
available at:
http://www.training.prace-ri.eu/uploads/tx_pracetmo/OpenFOAM_on_BGQ_porting_and_performance.pdf
(last access: 14 October 2015), 2013.
Dartevelle, S., Rose, W. I., Stix, J., Kelfoun, K., and Vallance, J. W.:
Numerical modeling of geophysical granular flows: 2. Computer simulations of
plinian clouds and pyroclastic flows and surges, Geochem. Geophy. Geosy., 5,
Q08004,
https://doi.org/10.1029/2003GC000637,
2004.
Dellino, P., Mele, D., Bonasia, R., Braia, L., La Volpe, L., and
Sulpizio, R.: The analysis of the influence of pumice shape on its terminal
velocity, Geophys. Res. Lett., 32, L21306,
https://doi.org/10.1029/ 2005GL023954,
2005.
de' Michieli Vitturi, M., Neri, A., and Barsotti, S.: PLUME-MoM 1.0:
A new integral model of volcanic plumes based on the method of moments, Geosci. Model Dev., 8, 2447–2463,
https://doi.org/10.5194/gmd-8-2447-2015, 2015.
Di Muro, A., Neri, A., and Rosi, M.: Contemporaneous convective and
collapsing eruptive dynamics: the transitional regime of explosive
eruptions, Geophys. Res. Lett., 31, L10607, https://doi.org/10.1029/2004GL019709, 2004.
Dobran, F., Neri, A., and Macedonio, G.: Numerical simulation of collapsing
volcanic columns, J. Geophys. Res., 98, 4231–4259, 1993.
Ducros, F., Comte, P., and Lesieur, M.: Large-eddy simulation of a spatially
growing boundary layer over an adiabatic flat plate at low Mach number,
Int. J. Heat Fluid Flow, 16, 341–348, 1995.
Elghobashi, S.: Particle-laden turbulent flows: direct simulation and closure
models, Appl. Sci. Res., 48, 301–314, 1991.
Elghobashi, S.: On predicting particle-laden turbulent flows, Appl. Sci.
Res., 52, 309–329, 1994.
Erlebacher, G., Hussaini, M. Y., Speziale, C. G., and Zang, T. A.: Toward the
Large-Eddy Simulation of Compressible Turbulent Flows, ICASE Report 90-76,
Tech. rep., ICASE/NASA Langley Research Center, 1990.
Esposti Ongaro, T., Neri, A., Menconi, G., de' Michieli Vitturi, M.,
Marianelli, P., Cavazzoni, C., Erbacci, G., and Baxter, P. J.: Transient 3D
numerical simulations of column collapse and pyroclastic density current
scenarios at Vesuvius, J. Volcanol. Geotherm. Res., 178, 378–396, 2008.
Fanneløp, T. K. and Webber, D. M.: On buoyant plumes rising from area
sources in a calm environment, J. Fluid Mech., 497, 319–334,
https://doi.org/10.1017/S0022112003006669,
2003.
Feireisl, E.: Dynamics of Viscous Compressible Fluids, vol. 26 of Oxford
Lecture Series in Mathematics and its Applications, Oxford University Press,
Oxford, 212 pp., 2004.
Ferry, J. and Balachandar, S.: A fast Eulerian method for disperse two-phase
flow, Int. J. Multiph. Flow, 27, 1199–1226, https://doi.org/10.1016/S0301-9322(00)00069-0, 2001.
Ferry, J. and Balachandar, S.: Equilibrium expansion for the Eulerian
velocity of small particles, Powder Technol., 125, 131–139,
https://doi.org/10.1016/S0032-5910(01)00499-5,
2002.
Ferry, J. and Balachandar, S.: Equilibrium Eulerian approach for predicting
the thermal field of a dispersion of small particles, Int. J. Heat Mass
Trans., 48, 681–689,
https://doi.org/10.1016/j.ijheatmasstransfer.2004.07.047, 2005.
Ferry, J., Rani, S. L., and Balachandar, S.: A locally implicit improvement
of the equilibrium Eulerian method, Int. J. Multiph. Flow, 29, 869–891,
https://doi.org/10.1016/S0301-9322(03)00064-8,
2003.
Ferziger, J. H. and Perić, M.: Computational Methods for Fluid
Dynamics, Vol. 3, Springer, Berlin, 423 pp., 1996.
Folch, A., Costa, A., and Macedonio, G.: FPLUME-1.0: An integrated volcanic
plume model accounting for ash aggregation, Geosci. Model Dev. Discuss., 8,
8009–8062,
https://doi.org/10.5194/gmdd-8-8009-2015,
2015.
Fureby, C.: On subgrid scale modeling in large eddy simulations of
compressible fluid flow, Phys. Fluids, 8, 1301,
https://doi.org/10.1063/1.868900, 1996.
Ganser, G.: A rational approach to drag prediction of spherical and nonspherical particles, Powd. Tech., 77, 143152, 1993.
Garnier, E., Mossi, M., Sagaut, P., Comte, P., and Deville, M.: On the use
of shock-capturing schemes for large-Eddy simulation, J. Comput. Phys., 153,
273–311,
https://doi.org/10.1006/jcph.1999.6268,
1999.
Garnier, E., Sagaut, P., and Deville, M.: Large Eddy simulation of
shock/homogeneous turbulence interaction, Comput. Fluids, 31, 245–268,
https://doi.org/10.1016/S0045-7930(01)00022-6,
2002.
Garnier, E., Adams, N., and Sagaut, P.: Large Eddy Simulation for
Compressible Flows, https://doi.org/10.1007/978-90-481-2819-8, Springer, the
Netherlands, 276 pp., 2009.
George, W. K., Alpert, R. L., and Tamanini, F.: Turbulence measurements in
an axisymmetric buoyant plume, Int. J. Heat Mass Transf., 20, 1145–1154,
https://doi.org/10.1016/0017-9310(77)90123-5,
1977.
Germano, M., Piomelli, U., Moin, P., and Cabot, W. H.: A dynamic
subgrid-scale eddy viscosity model, Phys. Fluids A-Fluid, 3, 1760–1765,
1991.
Geurts, B. J. and Fröhlich, J.: A framework for predicting accuracy
limitations in large-eddy simulation, Phys. Fluids, 14, L41, https://doi.org/10.1063/1.1480830, 2002.
Gidaspow, D.: Multiphase Flow and Fluidization: Continuum and Kinetic Theory
Descriptions, Academic Press, San Diego, California, 1994.
Glaze, L. S. and Baloga, S. M.: Sensitivity of buoyant plume heights to
ambient atmospheric conditions: Implications for volcanic eruption
columns, J. Geophys. Res., 101, 1529–1540, 1996.
Graf, H.-F., Herzog, M., Oberhuber, J. M., and Textor, C.: Effect of
environmental conditions on volcanic plume rise, J. Geophys. Res., 104,
24309–24320, 1999.
Graham, T.: On the motion of gases, Philos. T. R. Soc., 136, 573–631,
1846.
Greenshields, C. J., Weller, H. G., Gasparini, L., and Reese, J. M.:
Implementation of semi-discrete, non-staggered central schemes in
a colocated, polyhedral, finite volume framework, for high-speed viscous
flows, Int. J. Numer. Methods Fluids, 63, 1–21,
https://doi.org/10.1002/fld.2069, 2010.
Hashimoto, A., Shimbori, T., and Fukui, K.: Tephra fall simulation for the
eruptions at Mt. Shinmoe-dake during 26–27 January 2011 with JMANHM, SOLA,
8, 37–40, 2012.
Honein, A. E. and Moin, P.: Higher entropy conservation and numerical
stability of compressible turbulence simulations, J. Comput. Phys., 201,
531–545,
https://doi.org/10.1016/j.jcp.2004.06.006,
2004.
Ishimine, Y.: Sensitivity of the dynamics of volcanic eruption columns to
their shape, Bull. Volcanol., 68, 516–537,
https://doi.org/10.1007/s00445-005-0027-4,
2006.
Issa, R. I.: Solution of the implicitly discretised fluid flow equations by
operator-splitting, J. Comput. Phys., 62, 40–65,
https://doi.org/10.1016/0021-9991(86)90099-9,
1986.
Jasak, H.: Error Analysis and Estimation for the Finite Volume Method with
Applications to Fluid Flows, PhD thesis, Imperial College London, 394 pp., 1996.
Kaminski, E. and Jaupart, C.: The size distribution of pyroclasts and the
fragmentation sequence in explosive volcanic eruptions, J. Geophys. Res.,
103, 29759,
https://doi.org/10.1029/98JB02795, 1998.
Kaminski, E., Tait, S., and Carazzo, G.: Turbulent entrainment in jets with
arbitrary buoyancy, J. Fluid Mech., 526, 361–376,
https://doi.org/10.1017/S0022112004003209,
2005.
Kay, D. A., Gresho, P. M., Griffiths, D. F., and Silvester, D. J.: Adaptive
time-stepping for incompressible flow part ii: Navier-stokes equations,
SIAM J. Sci. Comput., 32, 111–128, 2010.
Kieffer, S. W.: Factors governing the structure of volcanic jets, Explos.
Volcanism: Inception, Evol., Hazards, National Academy, Washington, 143–157, 1984.
Koyaguchi, T., Suzuki, Y. J., and Kozono, T.: Effects of the crater on
eruption column dynamics, J. Geophys. Res., 115, B07205, https://doi.org/10.1029/2009JB007146, 2010.
Kueppers, U., Perugini, D., and Dingwell, D. B.: “Explosive energy” during
volcanic eruptions from fractal analysis of pyroclasts, Earth Planet. Sci.
Lett., 248, 800–807, 2006.
Kurganov, A., Noelle, S., and Petrova, G.: Semidiscrete central-upwind
schemes for hyperbolic conservation laws and Hamilton–Jacobi equations,
SIAM J. Sci. Comput., 23, 707–740, 2001.
Lesieur, M., Métais, O., and Comte, P.: Large-Eddy Simulations of
Turbulence, Vol. 1, Cambridge University Press, 2005.
Liao, W., Peng, Y., and Luo, L.-S.: Gas-kinetic schemes for direct numerical
simulations of compressible homogeneous turbulence, Phys. Rev. E, 80,
046702,
https://doi.org/10.1103/PhysRevE.80.046702,
2009.
List, E. J.: Turbulent jets and plumes, Annu. Rev. Fluid Mech., 14,
189–212,
https://doi.org/10.1146/annurev.fl.14.010182.001201,
1982.
Lodato, G., Vervisch, L., and Domingo, P.: A compressible wall-adapting
similarity mixed model for large-eddy simulation of the impinging round jet,
Phys. Fluids, 21, 035102,
https://doi.org/10.1063/1.3068761, 2009.
Marble, F.: Dynamics of dusty gases, Annu. Rev. Fluid
Mech., 2, 397–446, https://doi.org/10.1146/annurev.fl.02.010170.002145, 1970.
Maxey, M. R.: The gravitational settling of aerosol particles in homogeneous
turbulence and random flow fields, J. Fluid Mech., 174, 441,
https://doi.org/10.1017/S0022112087000193,
1987.
Moin, P., Squires, K., Cabot, W., and Lee, S.: A dynamic subgrid-scale model
for compressible turbulence and scalar transport, Phys. Fluids A-Fluid, 3,
2746, https://doi.org/10.1063/1.858164, 1991.
Morrissey, M. M.: Burst conditions of explosive volcanic eruptions recorded
on microbarographs, Science, 275, 1290–1293,
https://doi.org/10.1126/science.275.5304.1290,
1997.
Morton, B. R.: Forced plumes, J. Fluid Mech., 5, 151–163, 1959.
Morton, B. R., Taylor, G., and Turner, J. S.: Turbulent gravitational
convection from maintained and instantaneous sources, P. R. Soc. A, 234,
1–23,
https://doi.org/10.1098/rspa.1956.0011,
1956.
Neri, A. and Dobran, F.: Influence of eruption parameters on the thermofluid
dynamics of collapsing volcanic columns, J. Geophys. Res., 99, 11833–11857,
https://doi.org/10.1029/94JB00471, 1994.
Neri, A., Esposti Ongaro, T., Macedonio, G., and Gidaspow, D.:
Multiparticle simulation of collapsing volcanic columns and pyroclastic
flow, J. Geophys. Res., 108, 2202,
https://doi.org/10.1029/2001JB000508,
2003.
Nicoud, F. and Ducros, F.: Subgrid-scale stress modelling based on
the square of the velocity gradient tensor, Flow
Turbul. Combust., 62, 183–200, https://doi.org/10.1023/A:1009995426001, 1999.
Oberhuber, J. M., Herzog, M., Graf, H.-F., and Schwanke, K.: Volcanic plume
simulation on large scales, J. Volcanol. Geoth. Res., 87, 29–53,
https://doi.org/10.1016/S0377-0273(98)00099-7,
1998.
Ogden, D. E., Glatzmaier, G. A., and Wohletz, K. H.: Effects of vent
overpressure on buoyant eruption columns: implications for plume stability,
Earth Planet Sc. Lett., 268, 283–292, 2008a.
Ogden, D. E., Wohletz, K. H., Glatzmaier, G. A., and Brodsky, E. E.:
Numerical simulations of volcanic jets: importance of vent overpressure, J.
Geophys. Res., 113, B02204,
https://doi.org/10.1029/2007JB005133,
2008b.
Orescanin, M. M., Austin, J. M., and Kieffer, S. W.: Unsteady high-pressure
flow experiments with applications to explosive volcanic eruptions, J.
Geophys. Res., 115, B06206,
https://doi.org/10.1029/2009JB006985, 2010.
Patankar, S. V.: Numerical Heat Transfer and Fluid Flow, Hemisphere
Publishing Corporation, New York, USA, 205 pp., 1980.
Pelanti, M. and LeVeque, R. J.: High-resolution finite volume methods for
dusty gas jets and plumes, SIAM J. Sci. Comput., 28, 1335–1360,
https://doi.org/10.1137/050635018, 2006.
Pfeiffer T., Costa A., Macedonio G.: A model for the numerical simulation of
tephra fall deposits, J. Volcanol. Geotherm. Res., 140, 273–294,
https://doi.org/10.1016/j.jvolgeores.2004.09.001,
2005.
Pirozzoli, S. and Grasso, F.: Direct numerical simulations of isotropic
compressible turbulence: influence of compressibility on dynamics and
structures, Phys. Fluids, 16, 4386,
https://doi.org/10.1063/1.1804553, 2004.
Piscaglia, F., Montorfano, A., and Onorati, A.: Towards the LES simulation
of IC engines with parallel topologically changing meshes, SAE Int. J.
Engines, 6, 926–940,
https://doi.org/10.4271/2013-01-1096,
2013.
Plourde, F., Pham, M. V., Kim, S. D., and Balachandar, S.: Direct numerical
simulations of a rapidly expanding thermal plume: structure and entrainment
interaction, J. Fluid Mech., 604, 99–123,
https://doi.org/10.1017/S0022112008001006,
2008.
Pope, S. B.: Turbulent Flows, Cambridge University Press, 771 pp., 2000.
Prandtl, L.: The Essentials of Fluid Dynamics, https://doi.org/10.1007/978-1-4419-1564-1, Blackie & Son
Limited, New York, 1963.
Rani, S. L. and Balachandar, S.: Evaluation of the equilibrium Eulerian
approach for the evolution of particle concentration in isotropic
turbulence, Int. J. Multiph. Flow, 29, 1793–1816,
https://doi.org/10.1016/j.ijmultiphaseflow.2003.09.005,
2003.
Ricou, F. P. and Spalding, D. B.: Measurements of entrainment by
axisymmetrical turbulent jets, J. Fluid Mech., 11, 21–32, 1961.
Sagaut, P.: Large Eddy Simulation for Incompressible Flows: an
Introduction, Springer Science & Business Media, Germany, 556 pp., 2006.
Scase, M. M.: Evolution of volcanic eruption columns, J. Geophys. Res.,
114, F04003, https://doi.org/10.1029/2009JF001300, 2009.
Shabbir, A. and George, W. K.: Experiments on a round turbulent buoyant
plume, J. Fluid Mech., 275, 1–32, 1994.
Shotorban, B. and Balachandar, S.: A Eulerian model for large-eddy
simulation of concentration of particles with small Stokes numbers, Phys.
Fluids, 19, 118107,
https://doi.org/10.1063/1.2804956, 2007.
Sod, G.: A survey of several of nonlinear finite difference methods
hyperbolic conservation laws, J. Comput. Phys., 27, 1–31, 1978.
Sparks, R. S. J.: The dynamics of bubble formation and growth in magmas:
a review and analysis, J. Volcanol. Geoth. Res., 3, 1–37, 1978.
Sparks, R. S. J., Bursik, M. I., Carey, S. N., Gilbert, J. S., Glaze, L. S.,
Sigurdsson, H., and Woods, A. W.: Volcanic Plumes, John Wiley, Chichester, 590 pp., 1997.
Suzuki, Y. J.: A numerical study of turbulent mixing in eruption clouds using
a three-dimensional fluid dynamics model, J. Geophys. Res., 110, B08201,
https://doi.org/10.1029/2004JB003460, 2005.
Suzuki, Y. J. and Koyaguchi, T.: Numerical determination of the efficiency
of
entrainment in volcanic eruption columns, Geophys. Res. Lett., 37,
L05302,
https://doi.org/10.1029/2009GL042159, 2010.
Suzuki, Y. J. and Koyaguchi, T.: 3D numerical simulation of volcanic
eruption clouds during the 2011 Shinmoe-dake eruptions, Earth Planets Space,
65, 581–589,
https://doi.org/10.5047/eps.2013.03.009,
2013.
Suzuki, Y. J., Costa, A., Cerminara, M., Esposti Ongaro, T., Herzog, M.,
Van Eaton, A., Denby, L. C.: Inter-comparison of three-dimensional models of
volcanic plumes, submitted to J. Volcanol. Geoth. Res., accepted, 2016.
Valentine, G. A. and Wohletz, K. H.: Numerical models of Plinian eruption
columns and pyroclastic flows, J. Geophys. Res., 94, 1867–1887, 1989.
Van Eaton, A. R., Mastin, L. G., Herzog, M., Schwaiger, H. F., Schneider,
D. J., Wallace, K. L., and Clarke, A. B.: Hail formation triggers rapid ash
aggregation in volcanic plumes, Nat. Commun., 6, 7860,
https://doi.org/10.1038/ncomms8860, 2015.
Van Leer, B.: Towards the ultimate conservative difference scheme, J.
Comput. Phys., 135, 229–248, 1997.
Veitch, G. and Woods, A. W.: Particle recycling in volcanic plumes, B.
Volcanol., 64, 31–39, 2002.
Vreman, A. W. A.: Direct and Large-Eddy Simulation of the Compressible
Turbulent Mixing Layer, PhD thesis, University of Twente, 1995.
Vuorinen, V., Keskinen, J. P., Duwig, C., and Boersma, B. J.: On the
implementation of low-dissipative Runge–Kutta projection methods for time
dependent flows using OpenFOAM, Comput. Fluids, 93, 153–163,
https://doi.org/10.1016/j.compfluid.2014.01.026,
2014.
Wang, L.-P. and Maxey, M. R.: Settling velocity and concentration
distribution of heavy particles in homogeneous isotropic turbulence, J.
Fluid Mech., 256, 27,
https://doi.org/10.1017/S0022112093002708,
1993.
Wilson, L.: Explosive volcanic eruptions – III – Plinian eruption
columns, Geophys. J. Roy. Astr. S., 45, 543–556, 1976.
Wilson, L.: Relationships between pressure, volatile content and ejecta
velocity in three types of volcanic explosion, J. Volcanol. Geoth. Res., 8,
297–313, 1980.
Wilson, L., Sparks, R. S. J., and Walker, G. P. L.: Explosive volcanic
eruptions – IV. The control of magma properties and conduit geometry on
eruption column behaviour, Geophys. J. Roy. Astr. S., 63, 117–148, 1980.
Woodhouse, M. J., Phillips, J. C., and Hogg, A. J.: Unsteady Turbulent
Buoyant Plumes, ArXiV, available at:
http://arxiv.org/abs/1507.06571 (last access: 23 July 2015), 2015.
Woods, A. W.: The fluid dynamics and thermodynamics of eruption columns,
Bull. Volcanol., 50, 169–193, 1988.
Woods, A. W.: Moist convection and the injection of volcanic ash into the
atmosphere, J. Geophys. Res., 98, 17617–17627, 1993.
Woods, A. W.: Turbulent plumes in nature, Annu. Rev. Fluid Mech., 42,
391–412,
https://doi.org/10.1146/annurev-fluid-121108-145430,
2010.
Woods, A. W. and Bower, S. M.: The decompression of volcanic jets in
a crater during explosive volcanic eruptions, Earth Planet. Sci. Lett., 131,
189–205, 1995.
Woods, A. W. and Bursik, M. I.: Particle fallout, thermal disequilibrium and
volcanic plumes, B. Volcanol., 53, 559–570, 1991.
Yoshizawa, A.: Statistical theory for compressible turbulent shear flows,
with the application to subgrid modeling, Phys. Fluids, 29, 2152,
https://doi.org/10.1063/1.865552, 1986.
Yoshizawa, A.: Bridging between eddy-viscosity-type and second-order
turbulence models through a two-scale turbulence theory, Phys. Rev. E, 48,
273–281,
https://doi.org/10.1103/PhysRevE.48.273,
1993.
Yüceil, K. B. and Ötügen, M. V.: Scaling parameters for
underexpanded supersonic jets, Phys. Fluids, 14, 4206, https://doi.org/10.1063/1.1513796, 2002.
Zhou, X., Luo, K. H., and Williams, J. J.: Large-eddy simulation of
a turbulent forced plume, Eur. J. Mech. B-Fluid., 20, 233–254,
https://doi.org/10.1016/S0997-7546(00)01117-1,
2001.
Short summary
A new model for gas–particles compressible turbulent dynamics is developed. It is implemented in a fluid dynamic code based on the OpenFOAM libraries. The solver is tested against well known benchmarks, in particular: single and multiphase isotropic turbulence, plume turbulent dynamics and shock tube experiments. These comparisons validate the capability of the solver to capture the desired physics. A volcanic plume is analyzed, focusing on non-equilibrium ash dynamics and mean plume properties.
A new model for gas–particles compressible turbulent dynamics is developed. It is implemented in...
