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
Related authors
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
Short summary
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
Short summary
Short summary
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
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Related subject area
Atmospheric sciences
Optimized dynamic mode decomposition for reconstruction and forecasting of atmospheric chemistry data
Interpolating turbulent heat fluxes missing from a prairie observation on the Tibetan Plateau using artificial intelligence models
Carbon dioxide plume dispersion simulated at the hectometer scale using DALES: model formulation and observational evaluation
Low-level jets in the North and Baltic seas: mesoscale model sensitivity and climatology using WRF V4.2.1
SynRad v1.0: a radar forward operator to simulate synthetic weather radar observations from volcanic ash clouds
Chempath 1.0: an open-source pathway analysis program for photochemical models
PALACE v1.0: Paranal Airglow Line And Continuum Emission model
Atmospheric moisture tracking with WAM2layers v3
A new set of indicators for model evaluation complementing FAIRMODE's modelling quality objective (MQO)
Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region
New submodel for emissions from Explosive Volcanic ERuptions (EVER v1.1) within the Modular Earth Submodel System (MESSy, version 2.55.1)
Quantifying the oscillatory evolution of simulated boundary-layer cloud fields using Gaussian process regression
Numerical investigations on the modelling of ultrafine particles in SSH-aerosol-v1.3a: size resolution and redistribution
The third Met Office Unified Model–JULES Regional Atmosphere and Land Configuration, RAL3
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Diagnosis of winter precipitation types using the spectral bin model (version 1DSBM-19M): comparison of five methods using ICE-POP 2018 field experiment data
Improving winter condition simulations in SURFEX-TEB v9.0 with a multi-layer snow model and ice
UA-ICON with the NWP physics package (version ua-icon-2.1): mean state and variability of the middle atmosphere
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit
The Multi-Compartment Hg Modeling and Analysis Project (MCHgMAP): mercury modeling to support international environmental policy
Similarity-based analysis of atmospheric organic compounds for machine learning applications
The Atmospheric Potential Oxygen forward Model Intercomparison Project (APO-MIP1): Evaluating simulated atmospheric transport of air-sea gas exchange tracers and APO flux products
Porting the Meso-NH atmospheric model on different GPU architectures for the next generation of supercomputers (version MESONH-v55-OpenACC)
Estimation of aerosol and cloud radiative heating rate in the tropical stratosphere using a radiative kernel method
Development of a High-Resolution Coupled SHiELD-MOM6 Model. Part I – Model Overview, Coupling Technique, and Validation in a Regional Setup
Evaluation of dust emission and land surface schemes in predicting a mega Asian dust storm over South Korea using WRF-Chem
Sensitivity studies of a four-dimensional local ensemble transform Kalman filter coupled with WRF-Chem version 3.9.1 for improving particulate matter simulation accuracy
A Bayesian method for predicting background radiation at environmental monitoring stations in local-scale networks
Inclusion of the ECMWF ecRad radiation scheme (v1.5.0) in the MAR (v3.14), regional evaluation for Belgium, and assessment of surface shortwave spectral fluxes at Uccle
Development of a fast radiative transfer model for ground-based microwave radiometers (ARMS-gb v1.0): validation and comparison to RTTOV-gb
Indian Institute of Tropical Meteorology (IITM) High-Resolution Global Forecast Model version 1: an attempt to resolve monsoon prediction deadlock
Cell-tracking-based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
NeuralMie (v1.0): an aerosol optics emulator
A REtrieval Method for optical and physical Aerosol Properties in the stratosphere (REMAPv1)
Simulation performance of planetary boundary layer schemes in WRF v4.3.1 for near-surface wind over the western Sichuan Basin: a single-site assessment
FootNet v1.0: development of a machine learning emulator of atmospheric transport
Updates and evaluation of NOAA's online-coupled air quality model version 7 (AQMv7) within the Unified Forecast System
Quantifying the analysis uncertainty for nowcasting application
Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01
The MESSy DWARF (based on MESSy v2.55.2)
Meghana Velagar, Christoph Keller, and J. Nathan Kutz
Geosci. Model Dev., 18, 4667–4684, https://doi.org/10.5194/gmd-18-4667-2025, https://doi.org/10.5194/gmd-18-4667-2025, 2025
Short summary
Short summary
We develop the data-driven method of dynamic mode decomposition for producing a robust and stable surrogate reduced-order model of atmospheric chemistry dynamics. The model is computationally efficient, provides interpretable patterns of activity, and produces uncertainty quantification metrics. It is ideal for the forecasting of atmospheric chemistry in a computationally tractable manner.
Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu
Geosci. Model Dev., 18, 4625–4641, https://doi.org/10.5194/gmd-18-4625-2025, https://doi.org/10.5194/gmd-18-4625-2025, 2025
Short summary
Short summary
This study evaluates various machine learning and statistical methods for interpolating turbulent heat flux data over the Tibetan Plateau. The Transformer model showed the best performance, leading to the development of the Transformer_CNN model, which combines global and local attention mechanisms. Results show that Transformer_CNN outperforms the other models and was successfully applied to interpolate heat flux data from 2007 to 2016.
Arseniy Karagodin-Doyennel, Fredrik Jansson, Bart J. H. van Stratum, Hugo Denier van der Gon, Jordi Vilà-Guerau de Arellano, and Sander Houweling
Geosci. Model Dev., 18, 4571–4599, https://doi.org/10.5194/gmd-18-4571-2025, https://doi.org/10.5194/gmd-18-4571-2025, 2025
Short summary
Short summary
We introduce a new simulation platform based on the Dutch Atmospheric Large-Eddy Simulation (DALES) to simulate carbon dioxide (CO2) emissions and their dispersion in turbulent environments at a hectometer resolution. This model incorporates both anthropogenic emission inventories and online ecosystem fluxes. Simulation results for the main urban area in the Netherlands demonstrate the strong potential of DALES to improve CO2 emission modeling and to support mitigation strategies.
Bjarke T. E. Olsen, Andrea N. Hahmann, Nicolas G. Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
Geosci. Model Dev., 18, 4499–4533, https://doi.org/10.5194/gmd-18-4499-2025, https://doi.org/10.5194/gmd-18-4499-2025, 2025
Short summary
Short summary
Low-level jets (LLJs) are strong winds in the lower atmosphere that are important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Vishnu Nair, Anujah Mohanathan, Michael Herzog, David G. Macfarlane, and Duncan A. Robertson
Geosci. Model Dev., 18, 4417–4432, https://doi.org/10.5194/gmd-18-4417-2025, https://doi.org/10.5194/gmd-18-4417-2025, 2025
Short summary
Short summary
A numerical model that simulates the measurement processes behind the ground-based radars used to detect volcanic ash clouds is introduced. Using weather radars to detect volcanic clouds is not ideal, as fine ash particles are smaller than raindrops and remain undetected. We evaluate the performance of weather radars to study ash clouds and to identify optimal frequencies that balance the trade-off between a higher return signal and the higher path attenuation that comes at these higher frequencies.
Daniel Garduno Ruiz, Colin Goldblatt, and Anne-Sofie Ahm
Geosci. Model Dev., 18, 4433–4454, https://doi.org/10.5194/gmd-18-4433-2025, https://doi.org/10.5194/gmd-18-4433-2025, 2025
Short summary
Short summary
Photochemical models describe how the composition of the atmosphere changes due to chemical reactions, transport, and other processes. These models are useful for studying the composition of the Earth's and other planets' atmospheres. Understanding the results of these models can be difficult. Here, we build on previous work to develop open-source code that can identify the reaction chains (pathways) that produce the results of these models, facilitating the understanding of these results.
Stefan Noll, Carsten Schmidt, Patrick Hannawald, Wolfgang Kausch, and Stefan Kimeswenger
Geosci. Model Dev., 18, 4353–4398, https://doi.org/10.5194/gmd-18-4353-2025, https://doi.org/10.5194/gmd-18-4353-2025, 2025
Short summary
Short summary
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, this model is valuable for airglow research and astronomical observatories.
Peter Kalverla, Imme Benedict, Chris Weijenborg, and Ruud J. van der Ent
Geosci. Model Dev., 18, 4335–4352, https://doi.org/10.5194/gmd-18-4335-2025, https://doi.org/10.5194/gmd-18-4335-2025, 2025
Short summary
Short summary
We introduce a new version of WAM2layers (Water Accounting Model – 2 layers), a computer program that tracks how the weather brings water from one place to another. It uses data from weather and climate models, whose resolution is steadily increasing. Processing the latest data had become a challenge, and the updates presented here ensure that WAM2layers runs smoothly again. We also made it easier to use the program and to understand its source code. This makes it more transparent, reliable, and easier to maintain.
Alexander de Meij, Cornelis Cuvelier, Philippe Thunis, and Enrico Pisoni
Geosci. Model Dev., 18, 4231–4245, https://doi.org/10.5194/gmd-18-4231-2025, https://doi.org/10.5194/gmd-18-4231-2025, 2025
Short summary
Short summary
We assess relevance and utility indicators by evaluating nine Copernicus Atmospheric Monitoring Service models in calculated air pollutant values. For NO2, the results highlight difficulties at traffic stations. For PM2.5 and PM10 the bias and winter–summer gradients reveal issues. O3 evaluation shows that seasonal gradients are useful. Overall, the indicators reveal model limitations, yet there is a need to reconsider the strictness of some indicators for certain pollutants.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
Modelling the size distribution and the number concentration is important to represent ultrafine particles. A new analytic formulation is presented to compute coagulation partition coefficients, allowing us to lower the numerical diffusion associated with the resolution of aerosol dynamics. The significance of this effect is assessed in a 0D box model and over greater Paris with a chemistry transport model, using different size resolutions of the particle distribution.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Yuming Jin, Britton B. Stephens, Matthew C. Long, Naveen Chandra, Frédéric Chevallier, Joram J. D. Hooghiem, Ingrid T. Luijkx, Shamil Maksyutov, Eric J. Morgan, Yosuke Niwa, Prabir K. Patra, Christian Rödenbeck, and Jesse Vance
EGUsphere, https://doi.org/10.5194/egusphere-2025-1736, https://doi.org/10.5194/egusphere-2025-1736, 2025
Short summary
Short summary
We carry out a comprehensive atmospheric transport model (ATM) intercomparison project. This project aims to evaluate errors in ATMs and three air-sea O2 exchange products by comparing model simulations with observations collected from surface stations, ships, and aircraft. We also present a model evaluation framework to independently quantify transport-related and flux-related biases that contribute to model-observation discrepancies in atmospheric tracer distributions.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Joseph Mouallem, Kun Gao, Brandon G. Reichl, Lauren Chilutti, Lucas Harris, Rusty Benson, Niki Zadeh, Jing Chen, Jan-Huey Chen, and Cheng Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1690, https://doi.org/10.5194/egusphere-2025-1690, 2025
Short summary
Short summary
We introduce a new high-resolution model that couple the atmosphere and ocean to better simulate extreme weather events. It combines GFDL’s advanced atmospheric and ocean models with a powerful coupling system that allows robust and efficient two-way interactions. Simulations show the model accurately captures hurricane behavior and its impact on the ocean. It also runs efficiently on supercomputers. This model is a key step toward improving extreme weather forecast.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a new implementation of the ensemble mode, building upon the initial developments.
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
Short summary
Short summary
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.
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...
