Articles | Volume 16, issue 12
https://doi.org/10.5194/gmd-16-3459-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3459-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
27 Jun 2023
Development and technical paper |
| 27 Jun 2023
| 27 Jun 2023
Reconstructing tephra fall deposits via ensemble-based data assimilation techniques
Geosciences Barcelona (GEO3BCN-CSIC), Barcelona, Spain
Barcelona Supercomputing Center, Barcelona, Spain
Antonio Costa
Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Bologna, Bologna, Italy
Giovanni Macedonio
Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Vesuviano, Naples, Italy
Arnau Folch
Geosciences Barcelona (GEO3BCN-CSIC), Barcelona, Spain
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We present a new implementation of an ensemble-based data assimilation method to improve forecasting of volcanic aerosols. This system can be efficiently integrated into operational workflows by exploiting high-performance computing resources. We found a dramatic improvement of forecast quality when satellite retrievals are continuously assimilated. Management of volcanic risk and reduction of aviation impacts can strongly benefit from this research.
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This work addresses a quantitative hazard assessment on the possible impact on air traffic of a future ash-forming eruption on the island of Jan Mayen. Through high-performance computing resources, we numerically simulate the transport of ash clouds and ash concentration at different flight levels over an area covering Iceland and the UK using the FALL3D model. This approach allows us to derive a set of probability maps explaining the extent and persisting concentration conditions of ash clouds.
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Leonardo A. Mingari, Estela A. Collini, Arnau Folch, Walter Báez, Emilce Bustos, María Soledad Osores, Florencia Reckziegel, Peter Alexander, and José G. Viramonte
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In this paper, we provide the first comprehensive description of
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played by the orography and the condition that triggered the
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A. Folch, L. Mingari, M. S. Osores, and E. Collini
Nat. Hazards Earth Syst. Sci., 14, 119–133, https://doi.org/10.5194/nhess-14-119-2014, https://doi.org/10.5194/nhess-14-119-2014, 2014
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We present a numerical model for lahars generated by the mobilization of tephra deposits from a reference size eruption at Somma–Vesuvius. The paper presents the model (pyhsics and numerics) and a sensitivity analysis of the processes modelled, numerical schemes, and grid resolution. This work provides the basis for application to hazard quantification for lahars in the Vesuvius area. To this end, we rely on results of the two companion papers (Part 1 on field data, Part 3 on hazard maps).
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We study the distribution of two historical pyroclastic fall–flow and lahar deposits from the sub-Plinian Vesuvius eruptions of 472 CE Pollena and 1631. The motivation comes directly from the widely distributed impact that both the eruptions and lahar phenomena had on the Campanian territory, not only around the volcano but also down the nearby Apennine valleys. Data on about 500 stratigraphic sections and modeling allowed us to evaluate the physical and dynamical impact of these phenomena.
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Silvia Massaro, Manuel Stocchi, Beatriz Martínez Montesinos, Laura Sandri, Jacopo Selva, Roberto Sulpizio, Biagio Giaccio, Massimiliano Moscatelli, Edoardo Peronace, Marco Nocentini, Roberto Isaia, Manuel Titos Luzón, Pierfrancesco Dellino, Giuseppe Naso, and Antonio Costa
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A new methodology to calculate a probabilistic long-term tephra fallout hazard assessment in southern Italy from the Neapolitan volcanoes is provided. By means of thousands of numerical simulations we quantify the mean annual frequency with which the tephra load at the ground exceeds critical thresholds in 50 years. The output hazard maps account for changes in eruptive regimes of each volcano and are also comparable with those of other natural disasters in which more sources are integrated.
Andrea Bevilacqua, Alvaro Aravena, Willy Aspinall, Antonio Costa, Sue Mahony, Augusto Neri, Stephen Sparks, and Brittain Hill
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This paper presents FALL3D-8.0, the latest version release of an open-source code with a track record of 15+ years and a growing number of users in the volcanological and atmospheric communities. The code, originally conceived for atmospheric dispersal and deposition of tephra particles, has been extended to model other types of particles, aerosols and radionuclides. This paper details new model applications and validation of FALL3D-8.0 using satellite, ground-deposit load and radionuclide data.
Silvia Massaro, Roberto Sulpizio, Gianluca Norini, Gianluca Groppelli, Antonio Costa, Lucia Capra, Giacomo Lo Zupone, Michele Porfido, and Andrea Gabrieli
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In this work we provide a 2D finite-element modelling of the stress field conditions around the Fuego de Colima volcano (Mexico) in order to test the response of the commercial Linear Static Analysis software to increasingly different geological constraints. Results suggest that an appropriate set of geological and geophysical data improves the mesh generation procedures and the degree of accuracy of numerical outputs, aimed at more reliable physics-based representations of the natural system.
Arnau Folch, Leonardo Mingari, Natalia Gutierrez, Mauricio Hanzich, Giovanni Macedonio, and Antonio Costa
Geosci. Model Dev., 13, 1431–1458, https://doi.org/10.5194/gmd-13-1431-2020, https://doi.org/10.5194/gmd-13-1431-2020, 2020
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Soledad Osores, Juan Ruiz, Arnau Folch, and Estela Collini
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Volcanic ash dispersal forecasts are routinely used to avoid aircraft encounters with volcanic ash. However, the accuracy of these forecasts depends on the knowledge of key factors that are usually difficult to observe directly. In this work we apply an inverse methodology to improve ash concentration forecasts. Results are encouraging, showing that accurate estimations of ash emissions can be performed using the proposed approach, leading to an improvement in ash concentration forecasts.
Silvia Massaro, Antonio Costa, Roberto Sulpizio, Diego Coppola, and Lucia Capra
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The Fuego de Colima volcano (Mexico) shows a complex eruptive history, with periods of rapid and slow lava dome growth punctuated by explosive activity. Here we reconstructed the 1998–2018 average discharge rate by means of satellite thermal data and the literature. Using spectral and wavelet analysis, we found a multi-term cyclic behavior that is in good agreement with numerical modeling, accounting for a variable magmatic feeding system composed of a single or double magma chamber system.
Matthieu Poret, Stefano Corradini, Luca Merucci, Antonio Costa, Daniele Andronico, Mario Montopoli, Gianfranco Vulpiani, and Valentin Freret-Lorgeril
Atmos. Chem. Phys., 18, 4695–4714, https://doi.org/10.5194/acp-18-4695-2018, https://doi.org/10.5194/acp-18-4695-2018, 2018
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Alejandro Marti and Arnau Folch
Atmos. Chem. Phys., 18, 4019–4038, https://doi.org/10.5194/acp-18-4019-2018, https://doi.org/10.5194/acp-18-4019-2018, 2018
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Arnau Folch, Jordi Barcons, Tomofumi Kozono, and Antonio Costa
Nat. Hazards Earth Syst. Sci., 17, 861–879, https://doi.org/10.5194/nhess-17-861-2017, https://doi.org/10.5194/nhess-17-861-2017, 2017
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Atmospheric dispersal of a gas denser than air can threat the environment and surrounding communities. In complex terrains, microscale winds and local orographic features can have a strong influence on the gas cloud behavior, potentially leading to inaccurate model results if not captured by coarser-scale simulations. We introduce a methodology for microscale wind field characterization and validate it using, as a test case, the CO2 gas dispersal from 1986 Lake Nyos eruption.
Leonardo A. Mingari, Estela A. Collini, Arnau Folch, Walter Báez, Emilce Bustos, María Soledad Osores, Florencia Reckziegel, Peter Alexander, and José G. Viramonte
Atmos. Chem. Phys., 17, 6759–6778, https://doi.org/10.5194/acp-17-6759-2017, https://doi.org/10.5194/acp-17-6759-2017, 2017
Short summary
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In this paper, we provide the first comprehensive description of
a dust episode occurred in South America in June 2015 through
observations and numerical simulations. We have investigated
the spatiotemporal distribution of aerosols and the emission
process over complex terrain to gain insight into the key role
played by the orography and the condition that triggered the
long-range transport episode.
Alejandro Marti, Arnau Folch, Oriol Jorba, and Zavisa Janjic
Atmos. Chem. Phys., 17, 4005–4030, https://doi.org/10.5194/acp-17-4005-2017, https://doi.org/10.5194/acp-17-4005-2017, 2017
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We describe and evaluate NMMB-MONARCH-ASH, a novel online multi-scale meteorological and transport model developed at the BSC-CNS capable of forecasting the dispersal and deposition of volcanic ash. The forecast skills of the model have been validated and they improve on those from traditional operational offline (decoupled) models. The results support the use of online coupled models to aid civil aviation and emergency management during a crisis such as the 2010 eruption of Eyjafjallajökull.
A. Folch, A. Costa, and G. Macedonio
Geosci. Model Dev., 9, 431–450, https://doi.org/10.5194/gmd-9-431-2016, https://doi.org/10.5194/gmd-9-431-2016, 2016
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We present FPLUME-1.0, a steady-state 1-D cross-section-averaged eruption column model based on the buoyant plume theory (BPT). The model accounts for plume bending by wind, entrainment of ambient moisture, effects of water phase changes, particle fallout and re-entrainment, a new parameterization for the air entrainment coefficients and a model for wet aggregation of ash particles in presence of liquid water or ice.
R. Tonini, L. Sandri, A. Costa, and J. Selva
Nat. Hazards Earth Syst. Sci., 15, 409–415, https://doi.org/10.5194/nhess-15-409-2015, https://doi.org/10.5194/nhess-15-409-2015, 2015
S. Biass, C. Scaini, C. Bonadonna, A. Folch, K. Smith, and A. Höskuldsson
Nat. Hazards Earth Syst. Sci., 14, 2265–2287, https://doi.org/10.5194/nhess-14-2265-2014, https://doi.org/10.5194/nhess-14-2265-2014, 2014
C. Scaini, S. Biass, A. Galderisi, C. Bonadonna, A. Folch, K. Smith, and A. Höskuldsson
Nat. Hazards Earth Syst. Sci., 14, 2289–2312, https://doi.org/10.5194/nhess-14-2289-2014, https://doi.org/10.5194/nhess-14-2289-2014, 2014
A. Folch, L. Mingari, M. S. Osores, and E. Collini
Nat. Hazards Earth Syst. Sci., 14, 119–133, https://doi.org/10.5194/nhess-14-119-2014, https://doi.org/10.5194/nhess-14-119-2014, 2014
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We propose a Hermite–Birkhoff radial basis function (HRBF) formulation, AdaHRBF, with an adaptive gradient magnitude for continuous 3D stratigraphic potential field (SPF) modeling of multiple stratigraphic interfaces. In the linear system of HRBF interpolants constrained by the scattered on-contact attribute points and off-contact attitude points of a set of strata in 3D space, we add a novel optimization term to iteratively obtain the true gradient magnitude.
Mohammad Moulaeifard, Simon Bernard, and Florian Wellmann
Geosci. Model Dev., 16, 3565–3579, https://doi.org/10.5194/gmd-16-3565-2023, https://doi.org/10.5194/gmd-16-3565-2023, 2023
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In this work, we propose a flexible framework to generate and interact with geological models using explicit surface representations. The essence of the work lies in the determination of the flexible control mesh, topologically similar to the main geological structure, watertight and controllable with few control points, to manage the geological structures. We exploited the subdivision surface method in our work, which is commonly used in the animation and gaming industry.
Hui Gao, Xinming Wu, Jinyu Zhang, Xiaoming Sun, and Zhengfa Bi
Geosci. Model Dev., 16, 2495–2513, https://doi.org/10.5194/gmd-16-2495-2023, https://doi.org/10.5194/gmd-16-2495-2023, 2023
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We propose a workflow to automatically generate synthetic seismic data and corresponding stratigraphic labels (e.g., clinoform facies, relative geologic time, and synchronous horizons) by geological and geophysical forward modeling. Trained with only synthetic datasets, our network works well to accurately and efficiently predict clinoform facies in 2D and 3D field seismic data. Such a workflow can be easily extended for other geological and geophysical scenarios in the future.
Malte Jörn Ziebarth and Sebastian von Specht
EGUsphere, https://doi.org/10.5194/egusphere-2023-222, https://doi.org/10.5194/egusphere-2023-222, 2023
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Thermal energy from the active Earth’s interior constantly dissipates through the Earth’s surface. This heat flow is not spatially uniform and its exact pattern is hard to predict since it depends on crustal and mantle properties, both varying across scales. Our new model “REHEATFUNQ” addresses this difficulty by treating the fluctuations of heat flow within a region statistically. REHEATFUNQ estimates the regional distribution of heat flow and quantifies known structural signals therein.
Ibsen Chivata Cardenas, Terje Aven, and Roger Flage
Geosci. Model Dev., 16, 1601–1615, https://doi.org/10.5194/gmd-16-1601-2023, https://doi.org/10.5194/gmd-16-1601-2023, 2023
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We discuss challenges in uncertainty quantification for geohazard assessments. The challenges arise from limited data and the one-off nature of geohazard features. The challenges include the credibility of predictions, input uncertainty, and assumptions’ impact. Considerations to increase credibility of the quantification are provided. Crucial tasks in the quantification are the exhaustive scrutiny of the background knowledge coupled with the assessment of deviations of assumptions made.
Zhengfa Bi, Xinming Wu, Zhaoliang Li, Dekuan Chang, and Xueshan Yong
Geosci. Model Dev., 15, 6841–6861, https://doi.org/10.5194/gmd-15-6841-2022, https://doi.org/10.5194/gmd-15-6841-2022, 2022
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We present an implicit modeling method based on deep learning to produce a geologically valid and structurally compatible model from unevenly sampled structural data. Trained with automatically generated synthetic data with realistic features, our network can efficiently model geological structures without the need to solve large systems of mathematical equations, opening new opportunities for further leveraging deep learning to improve modeling capacity in many Earth science applications.
D. Rhodri Davies, Stephan C. Kramer, Sia Ghelichkhan, and Angus Gibson
Geosci. Model Dev., 15, 5127–5166, https://doi.org/10.5194/gmd-15-5127-2022, https://doi.org/10.5194/gmd-15-5127-2022, 2022
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Firedrake is a state-of-the-art system that automatically generates highly optimised code for simulating finite-element (FE) problems in geophysical fluid dynamics. It creates a separation of concerns between employing the FE method and implementing it. Here, we demonstrate the applicability and benefits of Firedrake for simulating geodynamical flows, with a focus on the slow creeping motion of Earth's mantle over geological timescales, which is ultimately the engine driving our dynamic Earth.
Federico Brogi, Simone Colucci, Jacopo Matrone, Chiara Paola Montagna, Mattia De' Michieli Vitturi, and Paolo Papale
Geosci. Model Dev., 15, 3773–3796, https://doi.org/10.5194/gmd-15-3773-2022, https://doi.org/10.5194/gmd-15-3773-2022, 2022
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Computer simulations play a fundamental role in understanding volcanic phenomena. The growing complexity of these simulations requires the development of flexible computational tools that can easily switch between sub-models and solution techniques as well as optimizations. MagmaFOAM is a newly developed library that allows for maximum flexibility for solving multiphase volcanic flows and promotes collaborative work for in-house and community model development, testing, and comparison.
Grace A. Nield, Matt A. King, Rebekka Steffen, and Bas Blank
Geosci. Model Dev., 15, 2489–2503, https://doi.org/10.5194/gmd-15-2489-2022, https://doi.org/10.5194/gmd-15-2489-2022, 2022
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We present a finite-element model of post-seismic solid Earth deformation built in the software package Abaqus for the purpose of calculating post-seismic deformation in the far field of major earthquakes. The model is benchmarked against an existing open-source post-seismic model demonstrating good agreement. The advantage over existing models is the potential for simple modification to include 3-D Earth structure, non-linear rheologies and alternative or multiple sources of stress change.
Alessandro Lechmann, David Mair, Akitaka Ariga, Tomoko Ariga, Antonio Ereditato, Ryuichi Nishiyama, Ciro Pistillo, Paola Scampoli, Mykhailo Vladymyrov, and Fritz Schlunegger
Geosci. Model Dev., 15, 2441–2473, https://doi.org/10.5194/gmd-15-2441-2022, https://doi.org/10.5194/gmd-15-2441-2022, 2022
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Muon tomography is a technology that is used often in geoscientific research. The know-how of data analysis is, however, still possessed by physicists who developed this technology. This article aims at providing geoscientists with the necessary tools to perform their own analyses. We hope that a lower threshold to enter the field of muon tomography will allow more geoscientists to engage with muon tomography. SMAUG is set up in a modular way to allow for its own modules to work in between.
Zuzanna M. Swirad and Adam P. Young
Geosci. Model Dev., 15, 1499–1512, https://doi.org/10.5194/gmd-15-1499-2022, https://doi.org/10.5194/gmd-15-1499-2022, 2022
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Cliff base and top lines that delimit coastal cliff faces are usually manually digitized based on maps, aerial photographs, terrain models, etc. However, manual mapping is time consuming and depends on the mapper's decisions and skills. To increase the objectivity and efficiency of cliff mapping, we developed CliffDelineaTool, an algorithm that identifies cliff base and top positions along cross-shore transects using elevation and slope characteristics.
Holly Kyeore Han, Natalya Gomez, and Jeannette Xiu Wen Wan
Geosci. Model Dev., 15, 1355–1373, https://doi.org/10.5194/gmd-15-1355-2022, https://doi.org/10.5194/gmd-15-1355-2022, 2022
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Interactions between ice sheets, sea level and the solid Earth occur over a range of timescales from years to tens of thousands of years. This requires coupled ice-sheet–sea-level models to exchange information frequently, leading to a quadratic increase in computation time with the number of model timesteps. We present a new sea-level model algorithm that allows coupled models to improve the computational feasibility and precisely capture short-term interactions within longer simulations.
Jérémie Giraud, Vitaliy Ogarko, Roland Martin, Mark Jessell, and Mark Lindsay
Geosci. Model Dev., 14, 6681–6709, https://doi.org/10.5194/gmd-14-6681-2021, https://doi.org/10.5194/gmd-14-6681-2021, 2021
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We review different techniques to model the Earth's subsurface from geophysical data (gravity field anomaly, magnetic field anomaly) using geological models and measurements of the rocks' properties. We show examples of application using idealised examples reproducing realistic features and provide theoretical details of the open-source algorithm we use.
Eric A. de Kemp
Geosci. Model Dev., 14, 6661–6680, https://doi.org/10.5194/gmd-14-6661-2021, https://doi.org/10.5194/gmd-14-6661-2021, 2021
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This is a proof of concept and review paper of spatial agents, with initial research focusing on geomodelling. The results may be of interest to others working on complex regional geological modelling with sparse data. Structural agent-based swarming behaviour is key to advancing this field. The study provides groundwork for research in structural geology 3D modelling with spatial agents. This work was done with NetLogo, a free agent modelling platform used mostly for teaching complex systems.
José M. Bastías Espejo, Andy Wilkins, Gabriel C. Rau, and Philipp Blum
Geosci. Model Dev., 14, 6257–6272, https://doi.org/10.5194/gmd-14-6257-2021, https://doi.org/10.5194/gmd-14-6257-2021, 2021
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The hydraulic and mechanical properties of the subsurface are inherently heterogeneous. RHEA is a simulator that can perform couple hydro-geomechanical processes in heterogeneous porous media with steep gradients. RHEA is able to fully integrate spatial heterogeneity, allowing allocation of distributed hydraulic and geomechanical properties at mesh element level. RHEA is a valuable tool that can simulate problems considering realistic heterogeneity inherent to geologic formations.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, Guillaume Caumon, Mark Jessell, and Robin Armit
Geosci. Model Dev., 14, 6197–6213, https://doi.org/10.5194/gmd-14-6197-2021, https://doi.org/10.5194/gmd-14-6197-2021, 2021
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Fault discontinuities in rock packages represent the plane where two blocks of rock have moved. They are challenging to incorporate into geological models because the geometry of the faulted rock units are defined by not only the location of the discontinuity but also the kinematics of the fault. In this paper, we outline a structural geology framework for incorporating faults into geological models by directly incorporating kinematics into the mathematical framework of the model.
Florence Colleoni, Laura De Santis, Enrico Pochini, Edy Forlin, Riccardo Geletti, Giuseppe Brancatelli, Magdala Tesauro, Martina Busetti, and Carla Braitenberg
Geosci. Model Dev., 14, 5285–5305, https://doi.org/10.5194/gmd-14-5285-2021, https://doi.org/10.5194/gmd-14-5285-2021, 2021
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PALEOSTRIP has been developed in the framework of past Antarctic ice sheet reconstructions for periods when bathymetry around Antarctica differed substantially from today. It has been designed for users with no knowledge of numerical modelling and allows users to switch on and off the processes involved in backtracking and backstripping. Applications are broad, and it can be used to restore any continental margin bathymetry or sediment thickness and to perform basin analysis.
Lachlan Grose, Laurent Ailleres, Gautier Laurent, and Mark Jessell
Geosci. Model Dev., 14, 3915–3937, https://doi.org/10.5194/gmd-14-3915-2021, https://doi.org/10.5194/gmd-14-3915-2021, 2021
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LoopStructural is an open-source 3D geological modelling library with a model design allowing for multiple different algorithms to be used for comparison for the same geology. Geological structures are modelled using structural geology concepts and techniques, allowing for complex structures such as overprinted folds and faults to be modelled. In the paper, we demonstrate automatically generating a 3-D model from map2loop-processed geological survey data of the Flinders Ranges, South Australia.
Zhenjiao Jiang, Dirk Mallants, Lei Gao, Tim Munday, Gregoire Mariethoz, and Luk Peeters
Geosci. Model Dev., 14, 3421–3435, https://doi.org/10.5194/gmd-14-3421-2021, https://doi.org/10.5194/gmd-14-3421-2021, 2021
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Fast and reliable tools are required to extract hidden information from big geophysical and remote sensing data. A deep-learning model in 3D image construction from 2D image(s) is here developed for paleovalley mapping from globally available digital elevation data. The outstanding performance for 3D subsurface imaging gives confidence that this generic novel tool will make better use of existing geophysical and remote sensing data for improved management of limited earth resources.
Stephan C. Kramer, D. Rhodri Davies, and Cian R. Wilson
Geosci. Model Dev., 14, 1899–1919, https://doi.org/10.5194/gmd-14-1899-2021, https://doi.org/10.5194/gmd-14-1899-2021, 2021
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Computational models of Earth's mantle require rigorous verification and validation. Analytical solutions of the underlying Stokes equations provide a method to verify that these equations are accurately solved for. However, their derivation in spherical and cylindrical shell domains with physically relevant boundary conditions is involved. This paper provides a number of solutions. They are provided in a Python package (Assess) and their use is demonstrated in a convergence study with Fluidity.
Bastian van den Bout, Theo van Asch, Wei Hu, Chenxiao X. Tang, Olga Mavrouli, Victor G. Jetten, and Cees J. van Westen
Geosci. Model Dev., 14, 1841–1864, https://doi.org/10.5194/gmd-14-1841-2021, https://doi.org/10.5194/gmd-14-1841-2021, 2021
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Landslides, debris flows and other types of dense gravity-driven flows threaten livelihoods around the globe. Understanding the mechanics of these flows can be crucial for predicting their behaviour and reducing disaster risk. Numerical models assume that the solids and fluids of the flow are unstructured. The newly presented model captures the internal structure during movement. This important step can lead to more accurate predictions of landslide movement.
Andrzej Górszczyk and Stéphane Operto
Geosci. Model Dev., 14, 1773–1799, https://doi.org/10.5194/gmd-14-1773-2021, https://doi.org/10.5194/gmd-14-1773-2021, 2021
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We present the 3D multi-parameter synthetic geomodel of the subduction zone, as well as the workflow designed to implement all of its components. The model contains different geological structures of various scales and complexities. It is intended to serve as a tool for the geophysical community to validate imaging approaches, design acquisition techniques, estimate uncertainties, benchmark computing approaches, etc.
Mattia de' Michieli Vitturi and Federica Pardini
Geosci. Model Dev., 14, 1345–1377, https://doi.org/10.5194/gmd-14-1345-2021, https://doi.org/10.5194/gmd-14-1345-2021, 2021
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Here, we present PLUME-MoM-TSM, a volcanic plume model that allows us to quantify the formation of aggregates during the rise of the plume, model the phase change of water, and include the possibility to simulate the initial spreading of the tephra umbrella cloud intruding from the volcanic column into the atmosphere. The model is first applied to the 2015 Calbuco eruption (Chile) and provides an analytical relationship between the upwind spreading and some characteristic of the volcanic column.
Zhikui Guo, Lars Rüpke, and Chunhui Tao
Geosci. Model Dev., 13, 6547–6565, https://doi.org/10.5194/gmd-13-6547-2020, https://doi.org/10.5194/gmd-13-6547-2020, 2020
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We present the 3-D hydro-thermo-transport model HydrothermalFoam v1.0, which we designed to provide the marine geosciences community with an easy-to-use and state-of-the-art tool for simulating mass and energy transport in submarine hydrothermal systems. HydrothermalFoam is based on the popular open-source platform OpenFOAM, comes with a number of tutorials, and is published under the GNU General Public License v3.0.
Marisol Monterrubio-Velasco, F. Ramón Zúñiga, Quetzalcoatl Rodríguez-Pérez, Otilio Rojas, Armando Aguilar-Meléndez, and Josep de la Puente
Geosci. Model Dev., 13, 6361–6381, https://doi.org/10.5194/gmd-13-6361-2020, https://doi.org/10.5194/gmd-13-6361-2020, 2020
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The Mexican subduction zone along the Pacific coast is one of the most active seismic zones in the world, where every year larger-magnitude earthquakes shake huge inland cities such as Mexico City. In this work, we use TREMOL (sThochastic Rupture Earthquake ModeL) to simulate the seismicity observed in this zone. Our numerical results reinforce the hypothesis that in some subduction regions single asperities are responsible for producing the observed seismicity.
Thomas Zwinger, Grace A. Nield, Juha Ruokolainen, and Matt A. King
Geosci. Model Dev., 13, 1155–1164, https://doi.org/10.5194/gmd-13-1155-2020, https://doi.org/10.5194/gmd-13-1155-2020, 2020
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We present a newly developed flat-earth model, Elmer/Earth, for viscoelastic treatment of solid earth deformation under ice loads. Unlike many previous approaches with proprietary software, this model is based on the open-source FEM code Elmer, with the advantage for scientists to apply and alter the model without license constraints. The new-generation full-stress ice-sheet model Elmer/Ice shares the same code base, enabling future coupled ice-sheet–glacial-isostatic-adjustment simulations.
Swarup Chauhan, Kathleen Sell, Wolfram Rühaak, Thorsten Wille, and Ingo Sass
Geosci. Model Dev., 13, 315–334, https://doi.org/10.5194/gmd-13-315-2020, https://doi.org/10.5194/gmd-13-315-2020, 2020
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We present CobWeb 1.0, a graphical user interface for analysing tomographic images of geomaterials. CobWeb offers different machine learning techniques for accurate multiphase image segmentation and visualizing material specific parameters such as pore size distribution, relative porosity and volume fraction. We demonstrate a novel approach of dual filtration and dual segmentation to eliminate edge enhancement artefact in synchrotron-tomographic datasets and provide the computational code.
Loïc Huder, Nicolas Gillet, and Franck Thollard
Geosci. Model Dev., 12, 3795–3803, https://doi.org/10.5194/gmd-12-3795-2019, https://doi.org/10.5194/gmd-12-3795-2019, 2019
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The pygeodyn package is a geomagnetic data assimilation tool written in Python. It gives access to the Earth's core flow dynamics, controlled by geomagnetic observations, by means of a reduced numerical model anchored to geodynamo simulation statistics. It aims to provide the community with a user-friendly and tunable data assimilation algorithm. It can be used for education, geomagnetic model production or tests in conjunction with webgeodyn, a set of visualization tools for geomagnetic models.
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.
Yihao Wu, Zhicai Luo, Bo Zhong, and Chuang Xu
Geosci. Model Dev., 11, 4797–4815, https://doi.org/10.5194/gmd-11-4797-2018, https://doi.org/10.5194/gmd-11-4797-2018, 2018
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A multilayer approach is parameterized for model development, and the multiple layers are located at different depths beneath the Earth’s surface. This method may be beneficial for gravity/manget field modeling, which may outperform the traditional single-layer approach.
Andres Payo, Bismarck Jigena Antelo, Martin Hurst, Monica Palaseanu-Lovejoy, Chris Williams, Gareth Jenkins, Kathryn Lee, David Favis-Mortlock, Andrew Barkwith, and Michael A. Ellis
Geosci. Model Dev., 11, 4317–4337, https://doi.org/10.5194/gmd-11-4317-2018, https://doi.org/10.5194/gmd-11-4317-2018, 2018
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We describe a new algorithm that automatically delineates the cliff top and toe of a cliffed coastline from a digital elevation model (DEM). The algorithm builds upon existing methods but is specifically designed to resolve very irregular planform coastlines with many bays and capes, such as parts of the coastline of Great Britain.
Hugo Cruz-Jiménez, Guotu Li, Paul Martin Mai, Ibrahim Hoteit, and Omar M. Knio
Geosci. Model Dev., 11, 3071–3088, https://doi.org/10.5194/gmd-11-3071-2018, https://doi.org/10.5194/gmd-11-3071-2018, 2018
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One of the most important challenges seismologists and earthquake engineers face is reliably estimating ground motion in an area prone to large damaging earthquakes. This study aimed at better understanding the relationship between characteristics of geological faults (e.g., hypocenter location, rupture size/location, etc.) and resulting ground motion, via statistical analysis of a rupture simulation model. This study provides important insight on ground-motion responses to geological faults.
Fabio Crameri
Geosci. Model Dev., 11, 2541–2562, https://doi.org/10.5194/gmd-11-2541-2018, https://doi.org/10.5194/gmd-11-2541-2018, 2018
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Firstly, this study acts as a compilation of key geodynamic diagnostics and describes how to automatise them for a more efficient scientific procedure. Secondly, it outlines today's key pitfalls of scientific visualisation and provides means to circumvent them with, for example, a novel set of fully scientific colour maps. Thirdly, it introduces StagLab 3.0, a software that applies such fully automated diagnostics and state-of-the-art visualisation in the blink of an eye.
Michael Bock, Olaf Conrad, Andreas Günther, Ernst Gehrt, Rainer Baritz, and Jürgen Böhner
Geosci. Model Dev., 11, 1641–1652, https://doi.org/10.5194/gmd-11-1641-2018, https://doi.org/10.5194/gmd-11-1641-2018, 2018
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We introduce the Soil and
Landscape Evolution Model (SaLEM) for the prediction of soil parent material evolution following a lithologically differentiated approach. The GIS tool is working within the software framework SAGA GIS. Weathering, erosion and transport functions are calibrated using extrinsic and intrinsic parameter data. First results indicate that our approach shows evidence for the spatiotemporal prediction of soil parental material properties.
Karthik Iyer, Henrik Svensen, and Daniel W. Schmid
Geosci. Model Dev., 11, 43–60, https://doi.org/10.5194/gmd-11-43-2018, https://doi.org/10.5194/gmd-11-43-2018, 2018
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Igneous intrusions in sedimentary basins have a profound effect on the thermal structure of the hosting sedimentary rocks. In this paper, we present a user-friendly 1-D FEM-based tool, SILLi, that calculates the thermal effects of sill intrusions on the enclosing sedimentary stratigraphy. The motivation is to make a standardized numerical toolkit openly available that can be widely used by scientists with different backgrounds to test the effects of magmatic bodies in a wide variety of settings.
Charles M. Shobe, Gregory E. Tucker, and Katherine R. Barnhart
Geosci. Model Dev., 10, 4577–4604, https://doi.org/10.5194/gmd-10-4577-2017, https://doi.org/10.5194/gmd-10-4577-2017, 2017
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Rivers control the movement of sediment and nutrients across Earth's surface. Understanding how rivers change through time is important for mitigating natural hazards and predicting Earth's response to climate change. We develop a new computer model for predicting how rivers cut through sediment and rock. Our model is designed to be joined with models of flooding, landslides, vegetation change, and other factors to provide a comprehensive toolbox for predicting changes to the landscape.
Diego Takahashi and Vanderlei C. Oliveira Jr.
Geosci. Model Dev., 10, 3591–3608, https://doi.org/10.5194/gmd-10-3591-2017, https://doi.org/10.5194/gmd-10-3591-2017, 2017
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Ellipsoids are the only bodies for which the self-demagnetization can be treated analytically. This property is useful for modelling compact orebodies having high susceptibility. We present a review of the magnetic modelling of ellipsoids, propose a way of determining the isotropic susceptibility above which the self-demagnetization must be considered, and discuss the ambiguity between confocal ellipsoids, as well as provide a set of routines to model the magnetic field produced by ellipsoids.
Hein J. van Heck, J. Huw Davies, Tim Elliott, and Don Porcelli
Geosci. Model Dev., 9, 1399–1411, https://doi.org/10.5194/gmd-9-1399-2016, https://doi.org/10.5194/gmd-9-1399-2016, 2016
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Currently, extensive geochemical databases of surface observations exist, but satisfying explanations of underlying mantle processes are lacking. We have implemented a new way to track both bulk compositions and concentrations of trace elements in a mantle convection code. In our model, chemical fractionation happens at evolving melting zones. We compare our results to a semi-analytical theory relating observed arrays of correlated Pb isotope compositions to melting age distributions.
J. Florian Wellmann, Sam T. Thiele, Mark D. Lindsay, and Mark W. Jessell
Geosci. Model Dev., 9, 1019–1035, https://doi.org/10.5194/gmd-9-1019-2016, https://doi.org/10.5194/gmd-9-1019-2016, 2016
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We often obtain knowledge about the subsurface in the form of structural geological models, as a basis for subsurface usage or resource extraction. Here, we provide a modelling code to construct such models on the basis of significant deformational events in geological history, encapsulated in kinematic equations. Our methods simplify complex dynamic processes, but enable us to evaluate how events interact, and finally how certain we are about predictions of structures in the subsurface.
A. D. Wickert
Geosci. Model Dev., 9, 997–1017, https://doi.org/10.5194/gmd-9-997-2016, https://doi.org/10.5194/gmd-9-997-2016, 2016
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Earth's lithosphere bends beneath surface loads, such as ice, sediments, and mountain belts. The pattern of this bending, or flexural isostatic response, is a function of both the loads and the spatially variable strength of the lithosphere. gFlex is an easy-to-use program to calculate flexural isostastic response, and may be used to better understand how ice sheets, glaciers, large lakes, sedimentary basins, volcanoes, and other surface loads interact with the solid Earth.
A. Folch, A. Costa, and G. Macedonio
Geosci. Model Dev., 9, 431–450, https://doi.org/10.5194/gmd-9-431-2016, https://doi.org/10.5194/gmd-9-431-2016, 2016
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We present FPLUME-1.0, a steady-state 1-D cross-section-averaged eruption column model based on the buoyant plume theory (BPT). The model accounts for plume bending by wind, entrainment of ambient moisture, effects of water phase changes, particle fallout and re-entrainment, a new parameterization for the air entrainment coefficients and a model for wet aggregation of ash particles in presence of liquid water or ice.
M. Dumon and E. Van Ranst
Geosci. Model Dev., 9, 41–57, https://doi.org/10.5194/gmd-9-41-2016, https://doi.org/10.5194/gmd-9-41-2016, 2016
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This paper presents a FOSS model called PyXRD used to improve the quantification of complex mixed-layer phyllosilicate assemblages using X-ray diffraction. The novelty of this model is the ab initio incorporation of the multi-specimen method, making it possible to share phases and their parameters across multiple specimens. We present results from a comparison of PyXRD with Sybilla v2.2.2 and a number of theoretical experiments illustrating the use of the multi-specimen set-up.
M. Mergili, J. Krenn, and H.-J. Chu
Geosci. Model Dev., 8, 4027–4043, https://doi.org/10.5194/gmd-8-4027-2015, https://doi.org/10.5194/gmd-8-4027-2015, 2015
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r.randomwalk is a flexible and multi-functional open-source GIS tool for simulating the propagation of mass movements. Mass points are routed from given release pixels through a digital elevation model until a defined break criterion is reached. In contrast to existing tools, r.randomwalk includes functionalities to account for parameter uncertainties, and it offers built-in functions for validation and visualization. We show the key functionalities of r.randomwalk for three test areas.
V. Naipal, C. Reick, J. Pongratz, and K. Van Oost
Geosci. Model Dev., 8, 2893–2913, https://doi.org/10.5194/gmd-8-2893-2015, https://doi.org/10.5194/gmd-8-2893-2015, 2015
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We adjusted the topographical and rainfall erosivity factors that are the triggers of erosion in the Revised Universal Soil Loss Equation (RUSLE) model to make the model better applicable at coarse resolution on a global scale. The adjusted RUSLE model compares much better to current high resolution estimates of soil erosion in the USA and Europe. It therefore provides a basis for estimating past and future global impacts of soil erosion on climate with the use of Earth system models.
Cited articles
Amezcua, J. and Leeuwen, P. J. V.:
Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach, Tellus A, 66, 23493, https://doi.org/10.3402/tellusa.v66.23493, 2014. a
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A Local Least Squares Framework for Ensemble Filtering, Mon. Weather Rev., 131, 634–642, https://doi.org/10.1175/1520-0493(2003)131<0634:ALLSFF>2.0.CO;2, 2003. a, b
Beckett, F. M., Witham, C. S., Leadbetter, S. J., Crocker, R., Webster, H. N., Hort, M. C., Jones, A. R., Devenish, B. J., and Thomson, D. J.:
Atmospheric Dispersion Modelling at the London VAAC: A Review of Developments since the 2010 Eyjafjallajökull Volcano Ash Cloud, Atmosphere, 11, 352, https://doi.org/10.3390/atmos11040352, 2020. a
Bonadonna, C. and Costa, A.:
Plume height, volume, and classification of volcanic explosive eruptions based on the Weibull function, B. Volcanol., 75, 1–19, https://doi.org/10.1007/s00445-013-0742-1, 2013. a
Bonadonna, C., Ernst, G., and Sparks, R.:
Thickness variations and volume estimates of tephra fall deposits: the importance of particle Reynolds number, J. Volcanol. Geoth. Res., 81, 173–187, https://doi.org/10.1016/S0377-0273(98)00007-9, 1998. a
Bonadonna, C., Folch, A., Loughlin, S., and Puempel, H.:
Future developments in modelling and monitoring of volcanic ash clouds: outcomes from the first IAVCEI-WMO workshop on Ash Dispersal Forecast and Civil Aviation, B. Volcanol., 74, 1–10, 2012. a
Bonadonna, C., Biass, S., and Costa, A.:
Physical characterization of explosive volcanic eruptions based on tephra deposits: Propagation of uncertainties and sensitivity analysis, J. Volcanol. Geoth. Res., 296, 80–100, https://doi.org/10.1016/j.jvolgeores.2015.03.009, 2015. a
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.:
Data assimilation in the geosciences: An overview of methods, issues, and perspectives, WIREs Clim. Change, 9, e535, https://doi.org/10.1002/wcc.535, 2018. a, b
Clarkson, R. J., Majewicz, E. J., and Mack, P.:
A re-evaluation of the 2010 quantitative understanding of the effects volcanic ash has on gas turbine engines, P. I. Mech. Eng. G-J. Aer., 230, 2274–2291, https://doi.org/10.1177/0954410015623372, 2016. a
Constantinescu, R., White, J. T., Connor, C. B., Hopulele-Gligor, A., Charbonnier, S., Thouret, J.-C., Lindsay, J. M., and Bertin, D.:
Uncertainty Quantification of Eruption Source Parameters Estimated From Tephra Fall Deposits, Geophys. Res. Lett., 49, e2021GL097425, https://doi.org/10.1029/2021GL097425, 2022. a
Cornell, W., Carey, S., and Sigurdsson, H.:
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Costa, A., Folch, A., and Macedonio, G.:
A model for wet aggregation of ash particles in volcanic plumes and clouds: I. Theoretical formulation, J. Geophys. Res.-Sol. Ea., 115, B09201, https://doi.org/10.1029/2009JB007175, 2010. a
Costa, A., Pioli, L., and Bonadonna, C.:
Assessing tephra total grain-size distribution: Insights from field data analysis, Earth Planet. Sc. Lett., 443, 90–107, https://doi.org/10.1016/j.epsl.2016.02.040, 2016. a
Dacre, H. F. and Harvey, N. J.:
Characterizing the Atmospheric Conditions Leading to Large Error Growth in Volcanic Ash Cloud Forecasts, J. Appl. Meteorol. Clim., 57, 1011–1019, https://doi.org/10.1175/JAMC-D-17-0298.1, 2018. a
Dare, R. A., Smith, D. H., and Naughton, M. J.:
Ensemble Prediction of the Dispersion of Volcanic Ash from the 13 February 2014 Eruption of Kelut, Indonesia, J. Appl. Meteorol. Clim., 55, 61–78, https://doi.org/10.1175/JAMC-D-15-0079.1, 2016. a
Degruyter, W. and Bonadonna, C.:
Improving on mass flow rate estimates of volcanic eruptions, Geophys. Res. Lett., 39, L16308, https://doi.org/10.1029/2012GL052566, 2012. a, b
Denlinger, R. P., Pavolonis, M., and Sieglaff, J.:
A robust method to forecast volcanic ash clouds, J. Geophys. Res.-Atmos., 117, D13208, https://doi.org/10.1029/2012JD017732, 2012. a
Dominguez, L., Bonadonna, C., Forte, P., Jarvis, P. A., Cioni, R., Mingari, L., Bran, D., and Panebianco, J. E.:
Aeolian remobilisation of the 2011-Cordón Caulle Tephra-Fallout Deposit: example of an important process in the life cycle of Volcanic Ash, Front. Earth Sci., 7, 343, https://doi.org/10.3389/feart.2019.00343, 2020. a
Durant, A. J., Rose, W. I., Sarna-Wojcicki, A. M., Carey, S., and Volentik, A. C. M.:
Hydrometeor-enhanced tephra sedimentation: Constraints from the 18 May 1980 eruption of Mount St. Helens, J. Geophys. Res.-Sol. Ea., 114, B03204, https://doi.org/10.1029/2008JB005756, 2009. a
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Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res.-Oceans, 99, 10143–10162, https://doi.org/10.1029/94JC00572, 1994. a, b
Folch, A. and Mingari, L.: Data Assimilation of Volcanic Clouds: Recent Advances and Implications on Operational Forecasts, in: Applications of Data Assimilation and Inverse Problems in the Earth Sciences (Special Publications of the International Union of Geodesy and Geophysics, edited by: Ismail-Zadeh, A., Castelli, F., Jones, D., and Sanchez, S., 144–156, Cambridge University Press, Cambridge, https://doi.org/10.1017/9781009180412.010, 2023. a, b, c
Folch, A., Costa, A., Durant, A., and Macedonio, G.:
A model for wet aggregation of ash particles in volcanic plumes and clouds: II. Model application, J. Geophys. Res.-Sol. Ea., 115, B09202, https://doi.org/10.1029/2009JB007176, 2010. a
Folch, A., Mingari, L., Osores, M. S., and Collini, E.:
Modeling volcanic ash resuspension – application to the 14–18 October 2011 outbreak episode in central Patagonia, Argentina, Nat. Hazards Earth Syst. Sci., 14, 119–133, https://doi.org/10.5194/nhess-14-119-2014, 2014. a
Folch, A., Mingari, L., Gutierrez, N., Hanzich, M., Macedonio, G., and Costa, A.:
FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 1: Model physics and numerics, Geosci. Model Dev., 13, 1431–1458, https://doi.org/10.5194/gmd-13-1431-2020, 2020. a, b
Folch, A., Costa, A., Macedonio, G. and Mingari, L.: FALL3D (8.2.0), Zenodo [code], https://doi.org/10.5281/zenodo.7267808, 2022a. a
Folch, A., Mingari, L., and Prata, A. T.:
Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1, Front. Earth Sci., 9, 741841, https://doi.org/10.3389/feart.2021.741841, 2022b. a, b
Folch, A., Abril, C., Afanasiev, M., Amati, G., Bader, M., Badia, R. M., Bayraktar, H. B., Barsotti, S., Basili, R., Bernardi, F., Boehm, C., Brizuela, B., Brogi, F., Cabrera, E., Casarotti, E., Castro, M. J., Cerminara, M., Cirella, A., Cheptsov, A., Conejero, J., Costa, A., de la Asunción, M., de la Puente, J., Djuric, M., Dorozhinskii, R., Espinosa, G., Esposti-Ongaro, T., Farnós, J., Favretto-Cristini, N., Fichtner, A., Fournier, A., Gabriel, A.-A., Gallard, J.-M., Gibbons, S. J., Glimsdal, S., González-Vida, J. M., Gracia, J., Gregorio, R., Gutierrez, N., Halldorsson, B., Hamitou, O., Houzeaux, G., Jaure, S., Kessar, M., Krenz, L.,Krischer, L., Laforet, S., Lanucara, P., Li, B., Lorenzino, M. C., Lorito, S., Løvholt, F., Macedonio, G., Macías, J., Marín, G., Martínez Montesinos, B., Mingari, L., Moguilny, G., Montellier, V., Monterrubio-Velasco, M., Moulard, G. E., Nagaso, M., Nazaria, M., Niethammer, C., Pardini, F., Pienkowska, M., Pizzimenti, L., Poiata, N., Rannabauer, L., Rojas, O., Rodriguez, J. E., Romano, F., Rudyy, O., Ruggiero, V., Samfass, P., Sánchez-Linares, C., Sanchez, S., Sandri, L., Scala, A., Schaeffer, N., Schuchart, J., Selva, J., Sergeant, A., Stallone, A., Taroni, M., Thrastarson, S., Titos, M., Tonelllo, N., Tonini, R., Ulrich, T., Vilotte, J.-P., Vöge, M., Volpe, M., Wirp, S. A., and Wössner, U.:
The EU Center of Excellence for Exascale in Solid Earth (ChEESE): Implementation, results, and roadmap for the second phase, Future Gener. Comp. Sy., 146, 47–61, https://doi.org/10.1016/j.future.2023.04.006, 2023. a
Fu, G., Heemink, A., Lu, S., Segers, A., Weber, K., and Lin, H.-X.:
Model-based aviation advice on distal volcanic ash clouds by assimilating aircraft in situ measurements, Atmos. Chem. Phys., 16, 9189–9200, https://doi.org/10.5194/acp-16-9189-2016, 2016. a
Fu, G., Prata, F., Lin, H. X., Heemink, A., Segers, A., and Lu, S.:
Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption, Atmos. Chem. Phys., 17, 1187–1205, https://doi.org/10.5194/acp-17-1187-2017, 2017. a
Harvey, N. J., Dacre, H. F., Webster, H. N., Taylor, I. A., Khanal, S., Grainger, R. G., and Cooke, M. C.:
The impact of ensemble meteorology on inverse modeling estimates of volcano emissions and ash dispersion forecasts: Grímsvötn 2011, Atmosphere, 11, 1022, https://doi.org/10.3390/atmos11101022, 2020. a
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Computation of probabilistic hazard maps and source parameter estimation for volcanic ash transport and dispersion, J. Comput. Phys., 271, 39–59, https://doi.org/10.1016/j.jcp.2013.11.032, 2014. a
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Reconstructing the plinian and co-ignimbrite sources of large volcanic eruptions: a novel approach for the Campanian Ignimbrite, Sci. Rep., 6, 1–11, https://doi.org/10.1038/srep21220, 2016. a, b
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On the feasibility and usefulness of high-performance computing in probabilistic volcanic hazard assessment: An application to tephra hazard from Campi Flegrei, Front. Earth Sci., 10, 941789, https://doi.org/10.3389/feart.2022.941789, 2022. a
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Mingari, L., Costa, A., Macedonio, G., and Folch, A.: gmd-2022-246 (2.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7259531, 2022a. a
Mingari, L., Folch, A., Prata, A. T., Pardini, F., Macedonio, G., and Costa, A.:
Data assimilation of volcanic aerosol observations using FALL3D+PDAF, Atmos. Chem. Phys., 22, 1773–1792, https://doi.org/10.5194/acp-22-1773-2022, 2022b. a, b, c
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FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 2: Model validation, Geosci. Model Dev., 14, 409–436, https://doi.org/10.5194/gmd-14-409-2021, 2021. a, b
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A Computationally Efficient Ensemble Filtering Scheme for Quantitative Volcanic Ash Forecasts, J. Geophys. Res.-Atmos., 126, e2020JD033094, https://doi.org/10.1029/2020JD033094, 2021a. a
Zidikheri, M. J. and Lucas, C.:
Improving Ensemble Volcanic Ash Forecasts by Direct Insertion of Satellite Data and Ensemble Filtering, Atmosphere, 12, 1215, https://doi.org/10.3390/atmos12091215, 2021b. a
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Short summary
Two novel techniques for ensemble-based data assimilation, suitable for semi-positive-definite variables with highly skewed uncertainty distributions such as tephra deposit mass loading, are applied to reconstruct the tephra fallout deposit resulting from the 2015 Calbuco eruption in Chile. The deposit spatial distribution and the ashfall volume according to the analyses are in good agreement with estimations based on field measurements and isopach maps reported in previous studies.
Two novel techniques for ensemble-based data assimilation, suitable for semi-positive-definite...
