Articles | Volume 16, issue 12
https://doi.org/10.5194/gmd-16-3479-2023
© 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-16-3479-2023
© 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
Leveraging Google's Tensor Processing Units for tsunami-risk mitigation planning in the Pacific Northwest and beyond
Ian Madden
Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
Simone Marras
CORRESPONDING AUTHOR
Department of Mechanical Engineering & Center for Applied Mathematics and Statistics, New Jersey Institute of Technology, Newark, NJ, USA
Jenny Suckale
Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
Department of Geophysics, Doerr School of Sustainability, Stanford University, USA
Related authors
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Andrew O. Hoffman, Paul T. Summers, Jenny Suckale, Knut Christianson, Ginny Catania, and Howard Conway
EGUsphere, https://doi.org/10.5194/egusphere-2025-1239, https://doi.org/10.5194/egusphere-2025-1239, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
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In Antarctica, fast-flowing ice streams drive most ice loss. Radar data from Conway Ice Ridge reveal that the van der Veen and Mercer Ice Streams were wider ~3000 years ago and narrowed progressively. Numerical modeling demonstrates that small thickness changes can rapidly alter shear-margin locations. These findings offer crucial insights into Late Holocene Ice Sheet readvance.
Akshay Sridhar, Yassine Tissaoui, Simone Marras, Zhaoyi Shen, Charles Kawczynski, Simon Byrne, Kiran Pamnany, Maciej Waruszewski, Thomas H. Gibson, Jeremy E. Kozdon, Valentin Churavy, Lucas C. Wilcox, Francis X. Giraldo, and Tapio Schneider
Geosci. Model Dev., 15, 6259–6284, https://doi.org/10.5194/gmd-15-6259-2022, https://doi.org/10.5194/gmd-15-6259-2022, 2022
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ClimateMachine is a new open-source Julia-language atmospheric modeling code. We describe its limited-area configuration and the model equations, and we demonstrate applicability through benchmark problems, including atmospheric flow in the shallow cumulus regime. We show that the discontinuous Galerkin numerics and model equations allow global conservation of key variables (up to sources and sinks). We assess CPU strong scaling and GPU weak scaling to show its suitability for large simulations.
Ludovic Räss, Aleksandar Licul, Frédéric Herman, Yury Y. Podladchikov, and Jenny Suckale
Geosci. Model Dev., 13, 955–976, https://doi.org/10.5194/gmd-13-955-2020, https://doi.org/10.5194/gmd-13-955-2020, 2020
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Accurate predictions of future sea level rise require numerical models that predict rapidly deforming ice. Localised ice deformation can be captured numerically only with high temporal and spatial resolution. This paper’s goal is to propose a parallel FastICE solver for modelling ice deformation. Our model is particularly useful for improving our process-based understanding of localised ice deformation. Our solver reaches a parallel efficiency of 99 % on GPU-based supercomputers.
Related subject area
Numerical methods
CLAQC v1.0 – Country Level Air Quality Calculator: an empirical modeling approach
Hydro-geomorphological modelling of leaky wooden dam efficacy from reach to catchment scale with CAESAR-Lisflood 1.9j
Enhancing single precision with quasi-double precision: achieving double-precision accuracy in the Model for Prediction Across Scales – Atmosphere (MPAS-A) version 8.2.1
Advances in land surface forecasting: a comparison of LSTM, gradient boosting, and feed-forward neural networks as prognostic state emulators in a case study with ecLand
Subgrid corrections for the linear inertial equations of a compound flood model – a case study using SFINCS 2.1.1 Dollerup release
Introducing Iterative Model Calibration (IMC) v1.0: a generalizable framework for numerical model calibration with a CAESAR-Lisflood case study
Development of a high-order global dynamical core using the discontinuous Galerkin method for an atmospheric large-eddy simulation (LES) and proposal of test cases: SCALE-DG v0.8.0
Optimized step size control within the Rosenbrock solvers for stiff chemical ODE systems in KPP version 2.2.3_rs4
A joint reconstruction and model selection approach for large-scale linear inverse modeling (msHyBR v2)
Assimilation of snow water equivalent from AMSR2 and IMS satellite data utilizing the local ensemble transform Kalman filter
The Paleochrono-1.1 probabilistic model to derive a common age model for several paleoclimatic sites using absolute and relative dating constraints
asQ: parallel-in-time finite element simulations using ParaDiag for geoscientific models and beyond
Explicit stochastic advection algorithms for the regional-scale particle-resolved atmospheric aerosol model WRF-PartMC (v1.0)
Potential based Thermodynamics with Consistent Conservative Cascade Transport for Implicit Large Eddy Simulation: PTerodaC3TILES version 1.0
The Measurement Error Proxy System Model: MEPSM v0.2
Numerical stabilization methods for level-set-based ice front migration
Modelling chemical advection during magma ascent
Consistent point data assimilation in Firedrake and Icepack
A computationally efficient parameterization of aerosol, cloud and precipitation pH for application at global and regional scale (EQSAM4Clim-v12)
Assessing the benefits of approximately exact step sizes for Picard and Newton solver in simulating ice flow (FEniCS-full-Stokes v.1.3.2)
Assessing effects of climate and technology uncertainties in large natural resource allocation problems
VISIR-2: ship weather routing in Python
Incremental analysis update (IAU) in the Model for Prediction Across Scales coupled with the Joint Effort for Data assimilation Integration (MPAS–JEDI 2.0.0)
Decision-making strategies implemented in SolFinder 1.0 to identify eco-efficient aircraft trajectories: application study in AirTraf 3.0
Developing meshing workflows in Gmsh v4.11 for the geologic uncertainty assessment of high-temperature aquifer thermal energy storage
Development and preliminary validation of a land surface image assimilation system based on the Common Land Model
NorSand4AI: a comprehensive triaxial test simulation database for NorSand constitutive model materials
ParticleDA.jl v.1.0: a distributed particle-filtering data assimilation package
HETerogeneous vectorized or Parallel (HETPv1.0): an updated inorganic heterogeneous chemistry solver for the metastable-state NH4+–Na+–Ca2+–K+–Mg2+–SO42−–NO3−–Cl−–H2O system based on ISORROPIA II
Three-dimensional geological modelling of igneous intrusions in LoopStructural v1.5.10
Estimating volcanic ash emissions using retrieved satellite ash columns and inverse ash transport modeling using VolcanicAshInversion v1.2.1, within the operational eEMEP (emergency European Monitoring and Evaluation Programme) volcanic plume forecasting system (version rv4_17)
Accounting for uncertainties in forecasting tropical-cyclone-induced compound flooding
An automatic mesh generator for coupled 1D–2D hydrodynamic models
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 1: Dust budget analyses and the impacts of a revised coupling scheme
Numerical coupling of aerosol emissions, dry removal, and turbulent mixing in the E3SM Atmosphere Model version 1 (EAMv1) – Part 2: A semi-discrete error analysis framework for assessing coupling schemes
jsmetrics v0.2.0: a Python package for metrics and algorithms used to identify or characterise atmospheric jet streams
P3D-BRNS v1.0.0: a three-dimensional, multiphase, multicomponent, pore-scale reactive transport modelling package for simulating biogeochemical processes in subsurface environments
MinVoellmy v1: a lightweight model for simulating rapid mass movements based on a modified Voellmy rheology
Scalable Feature Extraction and Tracking (SCAFET): a general framework for feature extraction from large climate data sets
Sweep interpolation: a cost-effective semi-Lagrangian scheme in the Global Environmental Multiscale model
CHONK 1.0: landscape evolution framework: cellular automata meets graph theory
Perspectives of physics-based machine learning strategies for geoscientific applications governed by partial differential equations
Calibration of absorbing boundary layers for geoacoustic wave modeling in pseudo-spectral time-domain methods
GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling
Parameterization and tuning of the Bay of Biscay Atlantis model v1
A comparison of Eulerian and Lagrangian methods for vertical particle transport in the water column
AutoQS v1: automatic parametrization of QuickSampling based on training images analysis
Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions
A dynamical core based on a discontinuous Galerkin method for higher-order finite-element sea ice modeling
GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation
Stefania Renna, Francesco Granella, Lara Aleluia Reis, and Paulina Schulz-Antipa
Geosci. Model Dev., 18, 2373–2408, https://doi.org/10.5194/gmd-18-2373-2025, https://doi.org/10.5194/gmd-18-2373-2025, 2025
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The Country Level Air Quality Calculator (CLAQC) is a new fast modeling tool that predicts globally country-level monthly and annual concentrations of two major air pollutants, fine particulate matter (PM2.5) and tropospheric ozone (O3). It was designed to inform national and regional climate and pollution mitigation policies. It is easy to use and computationally efficient, allowing for the simulation of a large number of emission scenarios for policy assessments and optimization frameworks.
Joshua M. Wolstenholme, Christopher J. Skinner, David Milan, Robert E. Thomas, and Daniel R. Parsons
Geosci. Model Dev., 18, 1395–1411, https://doi.org/10.5194/gmd-18-1395-2025, https://doi.org/10.5194/gmd-18-1395-2025, 2025
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Leaky wooden dams are a type of natural flood management intervention that aims to reduce flood risk downstream by temporarily holding back water during a storm event and releasing it afterwards. These structures alter the river hydrology, and therefore the geomorphology, yet often this is excluded from numerical models. Here we show that by not simulating geomorphology, we are currently underestimating the efficacy of these structures to reduce the flood peak and store water.
Jiayi Lai, Lanning Wang, Qizhong Wu, Yizhou Yang, and Fang Wang
Geosci. Model Dev., 18, 1089–1102, https://doi.org/10.5194/gmd-18-1089-2025, https://doi.org/10.5194/gmd-18-1089-2025, 2025
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High-performance computing limitations often hinder numerical model development. Traditional models use double precision for accuracy, which is computationally expensive. Lower precision reduces costs but can introduce errors. The quasi-double-precision (QDP) algorithm helps mitigate these errors. This study applies the QDP algorithm to the Model for Prediction Across Scales – Atmosphere, showing reduced errors and computational time, making it an efficient solution for large-scale simulations.
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington, Margarita Choulga, Souhail Boussetta, Maria Kalweit, Joschka Bödecker, Carsten F. Dormann, Florian Pappenberger, and Gianpaolo Balsamo
Geosci. Model Dev., 18, 921–937, https://doi.org/10.5194/gmd-18-921-2025, https://doi.org/10.5194/gmd-18-921-2025, 2025
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We compared spatiotemporal forecasts of three machine learning models that learned water and energy
states on the land surface from a physical model scheme. The forecasting models were developed with reanalysis data and simulations on a European scale and transferred to the globe. We found that all approaches deliver highly accurate approximations of the physical dynamic at long time horizons, implying their usefulness to advance land surface forecasting with synthetic data.
states on the land surface from a physical model scheme. The forecasting models were developed with reanalysis data and simulations on a European scale and transferred to the globe. We found that all approaches deliver highly accurate approximations of the physical dynamic at long time horizons, implying their usefulness to advance land surface forecasting with synthetic data.
Maarten van Ormondt, Tim Leijnse, Roel de Goede, Kees Nederhoff, and Ap van Dongeren
Geosci. Model Dev., 18, 843–861, https://doi.org/10.5194/gmd-18-843-2025, https://doi.org/10.5194/gmd-18-843-2025, 2025
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Accurate flood risk assessments are crucial for storm protection. To achieve efficiency, computational costs must be minimized. This paper introduces a novel subgrid approach for linear inertial equations (LIEs) with bed level and friction variations, implemented in the Super-Fast INundation of CoastS (SFINCS) model. Pre-processed lookup tables enhance simulation precision with lower costs. Validations show significant accuracy improvement even at coarser resolutions.
Chayan Banerjee, Kien Nguyen, Clinton Fookes, Gregory Hancock, and Thomas Coulthard
Geosci. Model Dev., 18, 803–818, https://doi.org/10.5194/gmd-18-803-2025, https://doi.org/10.5194/gmd-18-803-2025, 2025
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In geosciences, the reliance on numerical models necessitates the precise calibration of their parameters to effectively translate information from observed to unobserved settings. We introduce a generalizable framework for calibrating numerical models, with a case study of the geomorphological model CAESAR-Lisflood. This approach efficiently identifies the optimal set of parameters for a given numerical model, enabling retrospective and prospective analyses at various temporal resolutions.
Yuta Kawai and Hirofumi Tomita
Geosci. Model Dev., 18, 725–762, https://doi.org/10.5194/gmd-18-725-2025, https://doi.org/10.5194/gmd-18-725-2025, 2025
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When considering future high-resolution atmospheric simulations with O(10–100 m) grid spacing, such as global large-eddy simulations, numerical errors with the conventional low-order dynamical cores can contaminate the effects of turbulent parameterization. To achieve high-order discretization, we developed a global atmospheric dynamical core using the discontinuous Galerkin method (DGM). By conducting several validation tests, we discussed the impact of a high-order DGM on atmospheric flows.
Raphael Dreger, Timo Kirfel, Andrea Pozzer, Simon Rosanka, Rolf Sander, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-166, https://doi.org/10.5194/gmd-2024-166, 2025
Revised manuscript accepted for GMD
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Model simulations are essentials for understanding the interactions between atmospheric composition and weather. However, models including chemistry are very slow. Hence, any computation speedup of such models is important for advancing the understanding of interactions within the Earth System. In this study we analysed and optimized the time stepping for chemistry calculations. Our results show that atmospheric chemistry models could be run notably faster without any loss in the accuracy.
Malena Sabaté Landman, Julianne Chung, Jiahua Jiang, Scot M. Miller, and Arvind K. Saibaba
Geosci. Model Dev., 17, 8853–8872, https://doi.org/10.5194/gmd-17-8853-2024, https://doi.org/10.5194/gmd-17-8853-2024, 2024
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Making an informed decision about what prior information to incorporate or discard in an inverse model is important yet very challenging, as it is often not straightforward to distinguish between informative and non-informative variables. In this study, we develop a new approach for incorporating prior information in an inverse model using predictor variables, while simultaneously selecting the relevant predictor variables for the estimation of the unknown quantity of interest.
Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee
Geosci. Model Dev., 17, 8799–8816, https://doi.org/10.5194/gmd-17-8799-2024, https://doi.org/10.5194/gmd-17-8799-2024, 2024
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We developed an advanced snow water equivalent (SWE) data assimilation framework using satellite data based on a land surface model. The results of this study highlight the beneficial impact of data assimilation by effectively combining land surface model and satellite-derived data according to their relative uncertainty, thereby controlling not only transitional regions but also the regions with heavy snow accumulation that are difficult to detect by satellite.
Frédéric Parrenin, Marie Bouchet, Christo Buizert, Emilie Capron, Ellen Corrick, Russell Drysdale, Kenji Kawamura, Amaëlle Landais, Robert Mulvaney, Ikumi Oyabu, and Sune Olander Rasmussen
Geosci. Model Dev., 17, 8735–8750, https://doi.org/10.5194/gmd-17-8735-2024, https://doi.org/10.5194/gmd-17-8735-2024, 2024
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The Paleochrono-1.1 probabilistic dating model allows users to derive a common and optimized chronology for several paleoclimatic sites from various archives (ice cores, speleothems, marine cores, lake cores, etc.). It combines prior sedimentation scenarios with chronological information such as dated horizons, dated intervals, stratigraphic links and (for ice cores) Δdepth observations. Paleochrono-1.1 is available under an open-source license.
Joshua Hope-Collins, Abdalaziz Hamdan, Werner Bauer, Lawrence Mitchell, and Colin Cotter
EGUsphere, https://doi.org/https://doi.org/10.48550/arXiv.2409.18792, https://doi.org/https://doi.org/10.48550/arXiv.2409.18792, 2024
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Effectively using modern supercomputers requires massively parallel algorithms. Time-parallel algorithms calculate the system state (e.g. the atmosphere) at multiple times simultaneously and have exciting potential, but are tricky to implement and still require development. We have developed software to simplify implementing and testing the ParaDiag algorithm on supercomputers. We show that for some atmospheric problems it can enable faster or more accurate solutions than traditional techniques.
Jeffrey H. Curtis, Nicole Riemer, and Matthew West
Geosci. Model Dev., 17, 8399–8420, https://doi.org/10.5194/gmd-17-8399-2024, https://doi.org/10.5194/gmd-17-8399-2024, 2024
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This paper introduces a numerical method for simulating particle-based aerosol transport in atmospheric models. We detail the various numerical properties of the advection order method and demonstrate its implementation in a 3D weather prediction model (WRF) for the first time. Particle-based techniques improve the accuracy of aerosol size and composition predictions, which are key for aerosol–cloud and aerosol–radiation interactions.
John Thuburn
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-153, https://doi.org/10.5194/gmd-2024-153, 2024
Revised manuscript accepted for GMD
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A new computational fluid dynamics code for simulating the atmospheric boundary layer and convection is presented. Moist thermodynamics is formulated via thermodynamic potentials, avoiding inconsistencies that can be introduced with conventional approaches. Numerical methods typical of weather and climate models are used, with no explicit subgrid scheme. Results highlight some advantages (e.g., large time steps) and disadvantages (e.g., weak vertical fluxes near the surface) of this approach.
Matt J. Fischer
Geosci. Model Dev., 17, 6745–6760, https://doi.org/10.5194/gmd-17-6745-2024, https://doi.org/10.5194/gmd-17-6745-2024, 2024
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In paleoclimate research, proxy system models (PSMs) model chemical or biological systems which receive environmental input and output (e.g., geochemical signals). The environmental inputs are rarely noiseless, which causes problems when calibrating multi-input PSMs. Here a PSM is developed which includes generalized noise in both model inputs and outputs and prior information. A quasi-Bayesian method enhances the stability of the solution of the Measurement Error Proxy System Model.
Gong Cheng, Mathieu Morlighem, and G. Hilmar Gudmundsson
Geosci. Model Dev., 17, 6227–6247, https://doi.org/10.5194/gmd-17-6227-2024, https://doi.org/10.5194/gmd-17-6227-2024, 2024
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We conducted a comprehensive analysis of the stabilization and reinitialization techniques currently employed in ISSM and Úa for solving level-set equations, specifically those related to the dynamic representation of moving ice fronts within numerical ice sheet models. Our results demonstrate that the streamline upwind Petrov–Galerkin (SUPG) method outperforms the other approaches. We found that excessively frequent reinitialization can lead to exceptionally high errors in simulations.
Hugo Dominguez, Nicolas Riel, and Pierre Lanari
Geosci. Model Dev., 17, 6105–6122, https://doi.org/10.5194/gmd-17-6105-2024, https://doi.org/10.5194/gmd-17-6105-2024, 2024
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Predicting the behaviour of magmatic systems is important for understanding Earth's matter and heat transport. Numerical modelling is a technique that can predict complex systems at different scales of space and time by solving equations using various techniques. This study tests four algorithms to find the best way to transport the melt composition. The "weighted essentially non-oscillatory" algorithm emerges as the best choice, minimising errors and preserving system mass well.
Reuben W. Nixon-Hill, Daniel Shapero, Colin J. Cotter, and David A. Ham
Geosci. Model Dev., 17, 5369–5386, https://doi.org/10.5194/gmd-17-5369-2024, https://doi.org/10.5194/gmd-17-5369-2024, 2024
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Scientists often use models to study complex processes, like the movement of ice sheets, and compare them to measurements for estimating quantities that are hard to measure. We highlight an approach that ensures accurate results from point data sources (e.g. height measurements) by evaluating the numerical solution at true point locations. This method improves accuracy, aids communication between scientists, and is well-suited for integration with specialised software that automates processes.
Swen Metzger, Samuel Rémy, Jason E. Williams, Vincent Huijnen, and Johannes Flemming
Geosci. Model Dev., 17, 5009–5021, https://doi.org/10.5194/gmd-17-5009-2024, https://doi.org/10.5194/gmd-17-5009-2024, 2024
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EQSAM4Clim has recently been revised to provide an accurate and efficient method for calculating the acidity of atmospheric particles. It is based on an analytical concept that is sufficiently fast and free of numerical noise, which makes it attractive for air quality forecasting. Version 12 allows the calculation of aerosol composition based on the gas–liquid–solid and the reduced gas–liquid partitioning with the associated water uptake for both cases, including the acidity of the aerosols.
Niko Schmidt, Angelika Humbert, and Thomas Slawig
Geosci. Model Dev., 17, 4943–4959, https://doi.org/10.5194/gmd-17-4943-2024, https://doi.org/10.5194/gmd-17-4943-2024, 2024
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Future sea-level rise is of big significance for coastal regions. The melting and acceleration of glaciers plays a major role in sea-level change. Computer simulation of glaciers costs a lot of computational resources. In this publication, we test a new way of simulating glaciers. This approach produces the same results but has the advantage that it needs much less computation time. As simulations can be obtained with fewer computation resources, higher resolution and physics become affordable.
Jevgenijs Steinbuks, Yongyang Cai, Jonas Jaegermeyr, and Thomas W. Hertel
Geosci. Model Dev., 17, 4791–4819, https://doi.org/10.5194/gmd-17-4791-2024, https://doi.org/10.5194/gmd-17-4791-2024, 2024
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This paper applies a cutting-edge numerical method, SCEQ, to show how uncertain climate change and technological progress affect the future utilization of the world's scarce land resources. The paper's key insight is to illustrate how much global cropland will expand when future crop yields are unknown. The study finds the range of outcomes for land use change to be smaller when using this novel method compared to existing deterministic models.
Gianandrea Mannarini, Mario Leonardo Salinas, Lorenzo Carelli, Nicola Petacco, and Josip Orović
Geosci. Model Dev., 17, 4355–4382, https://doi.org/10.5194/gmd-17-4355-2024, https://doi.org/10.5194/gmd-17-4355-2024, 2024
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Ship weather routing has the potential to reduce CO2 emissions, but it currently lacks open and verifiable research. The Python-refactored VISIR-2 model considers currents, waves, and wind to optimise routes. The model was validated, and its computational performance is quasi-linear. For a ferry sailing in the Mediterranean Sea, VISIR-2 yields the largest percentage emission savings for upwind navigation. Given the vessel performance curve, the model is generalisable across various vessel types.
Soyoung Ha, Jonathan J. Guerrette, Ivette Hernández Baños, William C. Skamarock, and Michael G. Duda
Geosci. Model Dev., 17, 4199–4211, https://doi.org/10.5194/gmd-17-4199-2024, https://doi.org/10.5194/gmd-17-4199-2024, 2024
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To mitigate the imbalances in the initial conditions, this study introduces our recent implementation of the incremental analysis update (IAU) in the Model for Prediction Across Scales – Atmospheric (MPAS-A) component coupled with the Joint Effort for Data assimilation Integration (JEDI) through the cycling system. A month-long cycling run demonstrates the successful implementation of the IAU capability in the MPAS–JEDI cycling system.
Federica Castino, Feijia Yin, Volker Grewe, Hiroshi Yamashita, Sigrun Matthes, Simone Dietmüller, Sabine Baumann, Manuel Soler, Abolfazl Simorgh, Maximilian Mendiguchia Meuser, Florian Linke, and Benjamin Lührs
Geosci. Model Dev., 17, 4031–4052, https://doi.org/10.5194/gmd-17-4031-2024, https://doi.org/10.5194/gmd-17-4031-2024, 2024
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We introduce SolFinder 1.0, a decision-making tool to select trade-offs between different objective functions for optimal aircraft trajectories, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel and uses weather conditions simulated by the EMAC atmospheric model. This paper focuses on the ability of SolFinder to identify eco-efficient trajectories, reducing a flight's climate impact at limited cost penalties.
Ali Dashti, Jens C. Grimmer, Christophe Geuzaine, Florian Bauer, and Thomas Kohl
Geosci. Model Dev., 17, 3467–3485, https://doi.org/10.5194/gmd-17-3467-2024, https://doi.org/10.5194/gmd-17-3467-2024, 2024
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This study developed new meshing workflows to enable the automatic generation of meshes that follow geological models. The workflow allows for importing several geological models as input for Gmsh and later exporting the same number of high-quality meshes. This way, geological uncertainty is directly included in the numerical simulations. This study evaluates the impact of the geological uncertainty on thermohydraulic performance of two reservoirs for high-temperature heat storage applications.
Wangbin Shen, Zhaohui Lin, Zhengkun Qin, and Juan Li
Geosci. Model Dev., 17, 3447–3465, https://doi.org/10.5194/gmd-17-3447-2024, https://doi.org/10.5194/gmd-17-3447-2024, 2024
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In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The findings demonstrate that the assimilation of surface soil moisture observation images effectively and reasonably enhances the spatial structure of soil moisture analysis field.
Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante
Geosci. Model Dev., 17, 3175–3197, https://doi.org/10.5194/gmd-17-3175-2024, https://doi.org/10.5194/gmd-17-3175-2024, 2024
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The paper addresses synthetic dataset challenges in nonlinear constitutive modeling of soils, providing two datasets: one with 2000 soil types, 40 test conditions each (160 000 triaxial tests), and a second with 2048 soil types, 42 test conditions each (172 032 triaxial tests). Each dataset is a 4000×10 matrix applicable for multivariate forecasting and geotechnical simulations. In addition, a new Python code is introduced, empowering researchers to leverage Python packages for NorSand analyses.
Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, and Serge Guillas
Geosci. Model Dev., 17, 2427–2445, https://doi.org/10.5194/gmd-17-2427-2024, https://doi.org/10.5194/gmd-17-2427-2024, 2024
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Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across a wide range of settings. Progress for researchers is currently limited by a lack of tools to run these models effectively and efficiently. A key challenge is the optimal use of high-performance computing environments. The work presented here focuses on a developed open-source software platform which aims to improve this usage, with an emphasis placed on flexibility, efficiency, and scalability.
Stefan J. Miller, Paul A. Makar, and Colin J. Lee
Geosci. Model Dev., 17, 2197–2219, https://doi.org/10.5194/gmd-17-2197-2024, https://doi.org/10.5194/gmd-17-2197-2024, 2024
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This work outlines a new solver written in Fortran to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the forward algorithms of ISORROPIA II. The new code includes numerical improvements that decrease the computational speed (compared to ISORROPIA II) while improving the accuracy of the partitioning solution.
Fernanda Alvarado-Neves, Laurent Ailleres, Lachlan Grose, Alexander R. Cruden, and Robin Armit
Geosci. Model Dev., 17, 1975–1993, https://doi.org/10.5194/gmd-17-1975-2024, https://doi.org/10.5194/gmd-17-1975-2024, 2024
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Previous work has demonstrated that adding geological knowledge to modelling methods creates more accurate and reliable models. Following this reasoning, we added constraints from magma emplacement mechanisms into existing modelling frameworks to improve the 3D characterisation of igneous intrusions. We tested the method on synthetic and real-world case studies, and the results show that our method can reproduce intrusion morphologies with no manual processing and using realistic datasets.
André R. Brodtkorb, Anna Benedictow, Heiko Klein, Arve Kylling, Agnes Nyiri, Alvaro Valdebenito, Espen Sollum, and Nina Kristiansen
Geosci. Model Dev., 17, 1957–1974, https://doi.org/10.5194/gmd-17-1957-2024, https://doi.org/10.5194/gmd-17-1957-2024, 2024
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It is vital to know the extent and concentration of volcanic ash in the atmosphere during a volcanic eruption. Whilst satellite imagery may give an estimate of the ash right now (assuming no cloud coverage), we also need to know where it will be in the coming hours. This paper presents a method for estimating parameters for a volcanic eruption based on satellite observations of ash in the atmosphere. The software package is open source and applicable to similar inversion scenarios.
Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, José Antonio Álvarez Antolínez, Tim Leijnse, and Dano Roelvink
Geosci. Model Dev., 17, 1789–1811, https://doi.org/10.5194/gmd-17-1789-2024, https://doi.org/10.5194/gmd-17-1789-2024, 2024
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Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.
Younghun Kang and Ethan J. Kubatko
Geosci. Model Dev., 17, 1603–1625, https://doi.org/10.5194/gmd-17-1603-2024, https://doi.org/10.5194/gmd-17-1603-2024, 2024
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Models used to simulate the flow of coastal and riverine waters, including flooding, require a geometric representation (or mesh) of geographic features that exhibit a range of disparate spatial scales from large, open waters to small, narrow channels. Representing these features in an accurate way without excessive computational overhead presents a challenge. Here, we develop an automatic mesh generation tool to help address this challenge. Our results demonstrate the efficacy of our approach.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
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Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
Christopher J. Vogl, Hui Wan, Carol S. Woodward, and Quan M. Bui
Geosci. Model Dev., 17, 1409–1428, https://doi.org/10.5194/gmd-17-1409-2024, https://doi.org/10.5194/gmd-17-1409-2024, 2024
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Generally speaking, accurate climate simulation requires an accurate evolution of the underlying mathematical equations on large computers. The equations are typically formulated and evolved in process groups. Process coupling refers to how the evolution of each group is combined with that of other groups to evolve the full set of equations for the whole atmosphere. This work presents a mathematical framework to evaluate methods without the need to first implement the methods.
Tom Keel, Chris Brierley, and Tamsin Edwards
Geosci. Model Dev., 17, 1229–1247, https://doi.org/10.5194/gmd-17-1229-2024, https://doi.org/10.5194/gmd-17-1229-2024, 2024
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Jet streams are an important control on surface weather as their speed and shape can modify the properties of weather systems. Establishing trends in the operation of jet streams may provide some indication of the future of weather in a warming world. Despite this, it has not been easy to establish trends, as many methods have been used to characterise them in data. We introduce a tool containing various implementations of jet stream statistics and algorithms that works in a standardised manner.
Amir Golparvar, Matthias Kästner, and Martin Thullner
Geosci. Model Dev., 17, 881–898, https://doi.org/10.5194/gmd-17-881-2024, https://doi.org/10.5194/gmd-17-881-2024, 2024
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Coupled reaction transport modelling is an established and beneficial method for studying natural and synthetic porous material, with applications ranging from industrial processes to natural decompositions in terrestrial environments. Up to now, a framework that explicitly considers the porous structure (e.g. from µ-CT images) for modelling the transport of reactive species is missing. We presented a model that overcomes this limitation and represents a novel numerical simulation toolbox.
Stefan Hergarten
Geosci. Model Dev., 17, 781–794, https://doi.org/10.5194/gmd-17-781-2024, https://doi.org/10.5194/gmd-17-781-2024, 2024
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The Voellmy rheology has been widely used for simulating snow and rock avalanches. Recently, a modified version of this rheology was proposed, which turned out to be able to predict the observed long runout of large rock avalanches theoretically. The software MinVoellmy presented here is the first numerical implementation of the modified rheology. It consists of MATLAB and Python classes, where simplicity and parsimony were the design goals.
Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu
Geosci. Model Dev., 17, 301–320, https://doi.org/10.5194/gmd-17-301-2024, https://doi.org/10.5194/gmd-17-301-2024, 2024
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This study introduces a new computational framework called Scalable Feature Extraction and Tracking (SCAFET), designed to extract and track features in climate data. SCAFET stands out by using innovative shape-based metrics to identify features without relying on preconceived assumptions about the climate model or mean state. This approach allows more accurate comparisons between different models and scenarios.
Mohammad Mortezazadeh, Jean-François Cossette, Ashu Dastoor, Jean de Grandpré, Irena Ivanova, and Abdessamad Qaddouri
Geosci. Model Dev., 17, 335–346, https://doi.org/10.5194/gmd-17-335-2024, https://doi.org/10.5194/gmd-17-335-2024, 2024
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The interpolation process is the most computationally expensive step of the semi-Lagrangian (SL) approach. In this paper we implement a new interpolation scheme into the semi-Lagrangian approach which has the same computational cost as a third-order polynomial scheme but with the accuracy of a fourth-order interpolation scheme. This improvement is achieved by using two third-order backward and forward polynomial interpolation schemes in two consecutive time steps.
Boris Gailleton, Luca C. Malatesta, Guillaume Cordonnier, and Jean Braun
Geosci. Model Dev., 17, 71–90, https://doi.org/10.5194/gmd-17-71-2024, https://doi.org/10.5194/gmd-17-71-2024, 2024
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This contribution presents a new method to numerically explore the evolution of mountain ranges and surrounding areas. The method helps in monitoring with details on the timing and travel path of material eroded from the mountain ranges. It is particularly well suited to studies juxtaposing different domains – lakes or multiple rock types, for example – and enables the combination of different processes.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
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In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Carlos Spa, Otilio Rojas, and Josep de la Puente
Geosci. Model Dev., 16, 7237–7252, https://doi.org/10.5194/gmd-16-7237-2023, https://doi.org/10.5194/gmd-16-7237-2023, 2023
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This paper develops a calibration methodology of all absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. The main contributions of the paper are (a) an implementation and quantitative comparison of all absorbing techniques available for PSTD methods through a simple and robust numerical experiment, and (b) a validation of these absorbing techniques in several 3-D seismic scenarios with gradual heterogeneity complexities.
Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard
Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023, https://doi.org/10.5194/gmd-16-6987-2023, 2023
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Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Ane Lopez de Gamiz-Zearra, Cecilie Hansen, Xavier Corrales, Iñaki Quincoces, Izaskun Preciado, and Eider Andonegi
EGUsphere, https://doi.org/10.5194/egusphere-2023-1368, https://doi.org/10.5194/egusphere-2023-1368, 2023
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This paper describes the development of the first calibrated end-to-end Atlantis model for the Bay of Biscay that will help us improve the comprehension of the spatial functioning of the Bay of Biscay ecosystem and help establishing management measures of human activities. Our results highlighted the importance of lower trophic levels to the pelagic system and demonstrate the importance of having accurate and precise data for biological processes.
Tor Nordam, Ruben Kristiansen, Raymond Nepstad, Erik van Sebille, and Andy M. Booth
Geosci. Model Dev., 16, 5339–5363, https://doi.org/10.5194/gmd-16-5339-2023, https://doi.org/10.5194/gmd-16-5339-2023, 2023
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We describe and compare two common methods, Eulerian and Lagrangian models, used to simulate the vertical transport of material in the ocean. They both solve the same transport problems but use different approaches for representing the underlying equations on the computer. The main focus of our study is on the numerical accuracy of the two approaches. Our results should be useful for other researchers creating or using these types of transport models.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 16, 5265–5279, https://doi.org/10.5194/gmd-16-5265-2023, https://doi.org/10.5194/gmd-16-5265-2023, 2023
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Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.
Siting Li, Ping Wang, Hong Wang, Yue Peng, Zhaodong Liu, Wenjie Zhang, Hongli Liu, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 16, 4171–4191, https://doi.org/10.5194/gmd-16-4171-2023, https://doi.org/10.5194/gmd-16-4171-2023, 2023
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Optimizing the initial state of atmospheric chemistry model input is one of the most essential methods to improve forecast accuracy. Considering the large computational load of the model, we introduce an ensemble optimal interpolation scheme (EnOI) for operational use and efficient updating of the initial fields of chemical components. The results suggest that EnOI provides a practical and cost-effective technique for improving the accuracy of chemical weather numerical forecasts.
Thomas Richter, Véronique Dansereau, Christian Lessig, and Piotr Minakowski
Geosci. Model Dev., 16, 3907–3926, https://doi.org/10.5194/gmd-16-3907-2023, https://doi.org/10.5194/gmd-16-3907-2023, 2023
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Sea ice covers not only the pole regions but affects the weather and climate globally. For example, its white surface reflects more sunlight than land. The oceans around the poles are therefore kept cool, which affects the circulation in the oceans worldwide. Simulating the behavior and changes in sea ice on a computer is, however, very difficult. We propose a new computer simulation that better models how cracks in the ice change over time and show this by comparing to other simulations.
Emma J. MacKie, Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs, and Allan Zhang
Geosci. Model Dev., 16, 3765–3783, https://doi.org/10.5194/gmd-16-3765-2023, https://doi.org/10.5194/gmd-16-3765-2023, 2023
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Earth scientists often have to fill in spatial gaps in measurements. This gap-filling or interpolation can be accomplished with geostatistical methods, where the statistical relationships between measurements are used to inform how these gaps should be filled. Despite the broad utility of these methods, there are few freely available geostatistical software applications. We present GStatSim, a Python package for performing different geostatistical interpolation methods.
Cited articles
Abdolali, A. and Kirby, J. T.:
Role of compressibility on tsunami propagation, J. Geophys. Res.-Oceans, 122, 9780–9794, 2017. a
Abdolali, A., Kadri, U., and Kirby, J. T.:
Effect of water compressibility, sea-floor elasticity, and field gravitational potential on tsunami phase speed, Sci. Rep.-UK, 9, 1–8, 2019. a
Aida, I.:
Numercal experiments for the tsunami propagation of the 1964 Niigata tsunami and 1968 Tokachi-Oki tsunami, B. Earthq. Res. I. Tokyo, 47, 673–700, 1969. a
Aida, I.:
Numerical computational of a tsunami based on a fault origin model of an earthquake, J. Seismol. Soc. Jpn., 27, 141–154, 1974. a
Allgeyer, S. and Cummins, P. R.:
Numerical tsunami simulation including elastic loading and seawater density stratification, Geophys. Res. Lett., 41, 2368–2375, 2014. a
Atwater, B. F.:
Geologic evidence for earthquakes during the past 2000 years along the Copalis River, southern coastal Washington, J. Geophys. Res.-Sol. Ea., 97, 1901–1919, 1992. a
Atwater, B. F., Musumi-Rokkaku, S., Satake, K., Tsuji, Y., and Yamaguchi, D. K.:
The orphan tsunami of 1700: Japanese clues to a parent earthquake in North America, University of Washington Press, Seattle, Washington, USA, https://doi.org/10.3133/pp1707, 2011. a
Bates, P. D. and Hervouet, J.-M.:
A new method for moving–boundary hydrodynamic problems in shallow water, P. Roy. Soc. Lond. A Mat., 455, 3107–3128, 1999. a
Behrens, J. and Dias, F.:
New computational methods in tsunami science, Philos. T. R. Soc. A, 373, 20140382, https://doi.org/10.1098/rsta.2014.0382, 2015. a
Behrens, J., Løvholt, F., Jalayer, F., et al.:
Probabilistic tsunami hazard and risk analysis: a review of research gaps, Front. Earth Sci., 9, 628772, https://doi.org/10.3389/feart.2021.628772, 2021. a
Behrens, J., Schulz, A., and Simon, K.:
Performance Assessment of the Cloud for Prototypical Instant Computing Approaches in Geoscientific Hazard Simulations, Front. Earth Sci., 10, 762768, https://doi.org/10.3389/feart.2022.762768, 2022. a
Belletti, F., King, D. Yang, K., Nelet, R., Shafi, Y., and Shen, Y.-F.and Anderson, J.:
Tensor processing units for financial Monte Carlo, in: Proceedings of the 2020 SIAM Conference on Parallel Processing for Scientific Computing, Seattle, Washington, USA, 12–15 February 2020, 12–23, 2020. a
Bindoff, N. L., Willebrand, J., Artale, V., Cazenave, A., Gregory, J., Gulev, S., Hanawa, K., Le Quéré, C., Levitus, S., Nojiri, Y., Shum, C. K., Talley, L. D., and Unnikrishnan, A.:
Observations: oceanic climate change and sea level, chap. in: Climate change 2007–the physical science basis: Working group I contribution to the fourth assessment report of the IPCC, Cambridge University Press, Cambridge, United Kingdom, and New York, New York, USA, 2007. a
Bonev, B., Hesthaven, J. S., Giraldo, F. X., and Kopera, M. A.:
Discontinuous Galerkin scheme for the spherical shallow water equations with applications to tsunami modeling and prediction, J. Comput. Phys., 362, 425–448, 2018. a
Borrero, J. C., Legg, M. R., and Synolakis, C. E.:
Tsunami sources in the southern California bight, Geophys. Res. Lett., 31, L13211, https://doi.org/10.1029/2004GL020078, 2004. a
Bulleri, F. and Chapman, M.:
The introduction of coastal infrastructure as a driver of change in marine environments, J. Appl. Ecol., 47, 26–35, https://doi.org/10.1111/j.1365-2664.2009.01751.x, 2010. a
Bunya, S., Kubatko, E. J., Westerink, J. J., and Dawson, C.:
A wetting and drying treatment for the Runge–Kutta discontinuous Galerkin solution to the shallow water equations, Comput. Methods Appl. Mech. Engr., 198, 1548–1562, 2009. a
Carson, J.:
Model verification and validation, in: Proceedings of the Winter Simulation Conference, San Diego, California, USA, 8–11 December 2002, vol. 1, pp. 52–58, https://doi.org/10.1109/WSC.2002.1172868, 2002. a, b, c
Chen, C., Liu, H., and Beardsley, R. C.:
An unstructured grid, finite-volume, three dimensional, primitive equations ocean model: application to coastal ocean and estuaries, J. Atmos. Ocean Techn., 20, 159–186, 2003. a
Church, J. A. and White, N. J.:
A 20th century acceleration in global sea-level rise, Geophys. Res. Lett., 33, L01602, https://doi.org/10.1029/2005GL024826, 2006. a
Clague, J. J.:
Evidence for large earthquakes at the Cascadia subduction zone, Rev. Geophys, 35, 439–460, 1997. a
Clawpack Development Team:
Clawpack software, version 5.7.1, Zenodo [code and data set], https://doi.org/10.5281/zenodo.4025432, 2020. a, b
Dalton, M. M., Mote, P. W., and Snover, A. K.:
Climate Change in the Northwest, Island Press, Washington, DC,
ISBN 978-1610915601, 2013. a
Dean, R. G. and Dalrymple, R. A.:
Coastal processes with engineering applications, Cambridge University Press, Cambridge, United Kingdom, ISBN 9780511754500, 2002. a
Dugan, J. and Hubbard, D.:
Ecological Effects of Coastal Armoring: A Summary of Recent Results for Exposed Sandy Beaches in Southern California, in: Puget Sound Shorelines and the Impacts of Armoring, U. S. Geol. Surv. Sci. Invest. Rep., edited by: Shipman, H., Dethier, M., Gelfenbaum, G., Fresh, K., and Dinicola, R., 2010. a
Fauzi, A. and Mizutani, N.:
Machine Learning Algorithms for Real-time Tsunami Inundation Forecasting: A Case Study in Nankai Region, Pure Appl. Geophy., 177, 1437–1450, 2020. a
Fourestey, G., Cumming, B., Gilly, L., and Schulthess, T. C.:
First Experiences With Validating and Using the Cray Power Management Database Tool, arXiv, arXiv:1408.2657, 2014. a
Freitag, B., Wiser, J., Engstfeld, A., Killebrew, K., Scott, C., Kasprisin, R., DeMarco, T., Vitulli, J., El-Anwar, O., Hochstatter, K., Schelling, J., Nelson, D., Mooney, J., Walker, B., Walsh, T., Biasco, T., Wood, N., González, F., Wilde, T., Fritts, S., Rowlett, D., Nelson, C., Shipman, L., and Miles, G.:
Project Safe Haven: Tsunami Vertical Evacuation on the Washington Coast, Pacific County, Tech. rep., University of Washington and Washington Emergency Management Division, Seattle, Washington, USA, 2011. a, b, c
Fuhrer, O., Chadha, T., Hoefler, T., Kwasniewski, G., Lapillonne, X., Leutwyler, D., Lüthi, D., Osuna, C., Schär, C., Schulthess, T. C., and Vogt, H.:
Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0, Geosci. Model Dev., 11, 1665–1681, https://doi.org/10.5194/gmd-11-1665-2018, 2018. a
Galvez, P., Ampuero, J.-P., Dalguer, L., S. N., S., and Nissen-Meyer, T.:
Dynamic earthquake rupture modelled with an unstructured 3-D spectral element method applied to the 2011 M9 Tohoku earthquake, Geophys. J. Int., 198, 1222–1240, 2014. a
Ge, R., Feng, X., Song, S., Chang, H.-C., Li, D., and Cameron, K. W.:
PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications, IEEE T. Parall. Distr., 21, 658–671, https://doi.org/10.1109/TPDS.2009.76, 2010. a
Giles, D., Gopinathan, D., Guillas, S., and Dias, F.:
Faster Than Real Time Tsunami Warning with Associated Hazard Uncertainties, Front. Earth Sci., 8, https://doi.org/10.3389/feart.2020.597865, 2021. a
Google:
Google Environmental Report 2022, Tech. rep., Google, https://www.gstatic.com/gumdrop/sustainability/google-2022-environmental-report.pdf (last access: 12 June 2023), 2022. a
Gourgue, O., Comblen, R., Lambrechts, J., Kärnä, T., Legat, V., and Deleersnijder, E.:
A flux-limiting wetting-drying method for finite-element shallow-water models, with application to the Scheldt estuary, Adv. Water Res., 32, 1726–1739, 2009. a
Graham, N. E. and Diaz, H. F.:
Evidence for intensification of North Pacific winter cyclones since 1948, B. Am. Meteorol. Soc., 82, 1869–1894, 2001. a
Grothe, P. R., Taylor, L. A., Eakins, B. W., Carignan, K. S., Caldwell, R. J., Lim, E., and Friday, D. Z.:
Digital elevation models of Crescent City, California : procedures, data sources, and analysis, Tech. rep., National Geophysical Data Center, Marine Geology and Geophysics Division, https://repository.library.noaa.gov/view/noaa/1188 (last access: 12 June 2023), 2011. a
Heaton, T. H. and Hartzell, S. H.:
Earthquake hazards on the Cascadia subduction zone, Science, 236, 162–169, 1987. a
Horrillo, J., Grilli, S., Nicolsky, D., Roeber, V., and Zhang, J.:
Performance benchmarking tsunami models for NTHMP's inundation mapping activities, Pure Appl. Geophys., 172, 869–884, 2015. a
Intel:
Intel Xeon Processor E5 v4 Product Family Datasheet, Volume One: Electrical, Tech. Rep. 33809, rev. 003US, Intel, Santa Clara, California, USA, 2016. a
Isozaki, I. and Unoki, S.:
The numerical computation of the tsunami in Tokyo Bay caused by the Chilean earthquake in May, 1960, in: Studies on Oceanography – A Collection of Papers Dedicated to Koji Hidaka, 389–402, 1964. a
Jiang, G.-S. and Shu, C.-W.:
Efficient Implementation of Weighted ENO Schemes, J. Comput. Phys., 126, 202–228, https://doi.org/10.1006/jcph.1996.0130, 1996. a, b
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., and Borchers, A.:
In-datacenter performance analysis of a tensor processing unit, in: ACM/IEEE 44th Ann. Int. Symp. on Comp. Architecture (ISCA), Toronto, Ontario, Canada, 24–28 June 2017, 1–12, 2017. a
Jouppi, N. P., Yoon, D. H., Ashcraft, M., Gottscho, M., Jablin, T. B., Kurian, G., Laudon, J., Li, S., Ma, P., Ma, X., Norrie, T., Patil, N., Prasad, S., Young, C., Zhou, Z., and Patterson, D.:
Ten Lessons From Three Generations Shaped Google's TPUv4i: Industrial Product, in: 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), Valencia, Spain, 14–18 June 2021, https://doi.org/10.1109/isca52012.2021.00010, 2021. a
Kamiya, M., Igarashi, Y., Okada, M., and Baba, T.:
Numerical experiments on tsunami flow depth prediction for clustered areas using regression and machine learning models, Earth Planets Space, 74, 127, https://doi.org/10.1186/s40623-022-01680-9, 2022. a
Kärnä, T. de Brye, B., Gourgue, O., Lambrechts, J., Comblen, R., Legat, V., and Deleersnijder, E.:
A fully implicit wetting–drying method for DG-FEM shallow water models, with an application to the Scheldt Estuary, Comp. Method. Appl. M., 200, 509–524, 2011. a
Kennedy, A., Chen, Q., Kirby, J., and Dalrymple, R.:
Boussinesq modeling of wave transformation, breaking and runup, part I: 1D, J. Waterw. Port Coast., 126, 39–47, 2000. a
Kim, D. and Lynett, P.:
Turbulent mixing and passive scalar transport in shallow flows, Phys. Fluids, 23, 016603, https://doi.org/10.1063/1.3531716, 2011. a
Knudson, B. and Bettinardi, A.:
Estimated Economic Impact Analysis Due to Failure of the Transportation Infrastructure in the Event of a 9.0 Cascadia Subduction Zone Earthquake, State of Oregon Department of Transportation, Salem, Oregon, https://www.oregon.gov/odot/Data/Documents/Cascadia-Subduction-Zone-Earthquake-Economic-Impact.pdf (last access: 12 June 2023), 2013. a
Komar, P.:
Beach processes and sedimentation, Prentice Hall, Hoboken, New Jersey, USA,
ISBN 978-0137549382, 1998. a
Leschka, S. and Oumeraci, H.:
Solitary waves and bores passing three cylinders-effect of distance and arrangement, Coast. Eng. Proc, 39, 34,
https://doi.org/10.9753/icce.v34.structures.39, 2014. a
LeVeque, R., George, D., and Berger, M.:
Tsunami modelling with adaptively refined finite volume methods, Acta Numer., 20, 211–289, 2011. a
LeVeque, R. J.:
Finite volume methods for hyperbolic problems, Cambridge Univ. Press, Cambridge, United Kingdom, https://doi.org/10.1017/CBO9780511791253, 2011. a
Liu, C. M., Rim, D., Baraldi, R., and LeVeque, R. J.:
Comparison of Machine Learning Approaches for Tsunami Forecasting from Sparse Observations, Pure Appl. Geophy., 178, 5129–5153, 2021. a
López-Venegas, A., Horrillo, J., Pampell-Manis, A., Huérfano, V., and Mercado, A.:
Advanced Tsunami Numerical Simulations and Energy Considerations by use of 3D-2D coupled Models: The October 11, 1918, Mona Passage Tsunami, Pure App. Geophys., 171, 2863–3174, 2014. a
Løvholt, F., Lorito, S., Macías, J., Volpe, M., Selva, J., and Gibbons, S.:
Urgent tsunami computing, in: 2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC), Denver, Colorado, USA, 17 November 2019, 45–50, https://doi.org/10.1109/UrgentHPC49580.2019.00011, 2019. a, b
Lu, T., Chen, Y., Hechtman, B., Wang, T., and Anderson, J.:
Large-Scale Discrete Fourier Transform on TPUs, arXiv [preprint], arXiv:2002.03260, 2020a. a
Lu, T., Marin, T. Zhuo, Y., Chen, Y., and Ma, C.:
Accelerating MRI Reconstruction on TPUs, in: 2020 IEEE High Performance Extreme Computing Conference (HPEC), 21–25 September 2020, 1–9, 2020b. a
Lunghino, B., Santiago Tate, A., Mazereeuw, M., Muhari, A., Giraldo, F., Marras, S., and Suckale, J.:
The protective benefits of tsunami mitigation parks and ramifications for their strategic design, P. Natl. Acad. Sci. USA, 117, 1911857117, https://doi.org/10.1073/pnas.1911857117, 2020. a, b, c, d, e, f, g
Lynett, P., Wu, T., and P. L. F., L.:
Modeling wave runup with depth-integrated equations, Coast. Eng., 46, 89–107, 2002. a
Lynett, P. J.:
Effect of a shallow water obstruction on long wave runup and overland flow velocity, J. Waterw. Port Coast., 133, 455–462, 2007. a
Ma, G., Shi, F., and Kirby, J.:
Shock-capturing non-hydrostatic model for fully dispersive surface wave processes, Ocean Modeling, 43–44, 22–35, 2012. a
Macías, J., Castro, M., Ortega, S., Escalante, C., and González-Vida, J.:
Performance Benchmarking of Tsunami-HySEA Model for NTHMP's Inundation Mapping Activities, Pure Appl. Geophys., 174, 3147–3183, 2017. a
Macías, J., Castro, M., and Escalante, C.:
Performance assessment of the Tsunami-HySEA model for NTHMP tsunami currents benchmarking. Laboratory data, Coast. Eng., 158, 103667, https://doi.org/10.1016/j.coastaleng.2020.103667, 2020a. a
Macías, J., Castro, M., Ortega, S., and González-Vida, J.:
Performance assessment of Tsunami-HySEA model for NTHMP tsunami currents benchmarking. Field cases, Ocean Modelling, 152, 101645, https://doi.org/10.1016/j.ocemod.2020.101645, 2020b. a
Madden, I., Marras, S., and Suckale, J.: tsunamiTPUlab (1.0.0), Zenodo [code and data set], https://doi.org/10.5281/zenodo.7574655, 2023. a
Mandli, K. T., Ahmadia, A. J., Berger, M., Calhoun, D., George, D. L., Hadjimichael, Y., Ketcheson, D. I., Lemoine, G. I., and LeVeque, R. J.:
Clawpack: building an open source ecosystem for solving hyperbolic PDEs, PeerJ Computer Science, 2, e68, https://doi.org/10.7717/peerj-cs.68, 2016. a
Mao, Z., Jagtap, A. D., and Karniadakis, G. E.:
Physics-informed neural networks for high-speed flows, Comp. Method. Appl. M., 360, 112789, https://doi.org/10.1016/j.cma.2019.112789, 2020. a
Marras, S. and Mandli, K. T.:
Modeling and Simulation of Tsunami Impact: A Short Review of Recent Advances and Future Challenges, Geosciences, 11, 5, https://doi.org/10.3390/geosciences11010005, 2021. a
Marras, S., Kopera, M., Constantinescu, E., Suckale, J., and Giraldo, F.:
A Residual-based Shock Capturing Scheme for the Continuous/Discontinuous Spectral Element Solution of the 2D Shallow Water Equations, Adv. Water Res., 114, 45–63, 2018. a
Marsooli, R. and Wu, W.:
Numerical investigation of wave attenuation by vegetation using a 3D RANS model, Adv. Water Resour., 74, 245–257, 2014. a
Maza, M., Lara, J., and Losada, I.:
Tsunami wave interaction with mangrove forests:a 3-D numerical approach, Coast. Eng., 98, 33–54, https://doi.org/10.1016/j.coastaleng.2015.01.002, 2015. a
Mukherjee, A., Cajas, J., Houzeaux, G., Lehmkuhl, O., Suckale, J., and Marras, S.:
Forest density is more effective than tree rigidity at reducing the onshore energy flux of tsunamis, Coast. Eng., 182, 104286, 2023. a
Nelson, A. R., Atwater, B. F., Bobrowsky, P. T., Bradley, L.-A., Clague, J. J., Carver, G. A., Darienzo, M. E., Grant, W. C., Krueger, H. W., Sparks, R., Stafford Jr., T. W., and Stuiver, M.:
Radiocarbon evidence for extensive plate-boundary rupture about 300 years ago at the Cascadia subduction zone, Nature, 378, 371–374, 1995. a
Nikolos, I. and Delis, A.:
An unstructured node-centered finite volume scheme for shallow water flows with wet/dry fronts over complex topography, Comp. Method. Appl. M., 198, 3723–3750, 2009. a
NOAA National Geophysical Data Center:
Crescent City, California 1/3 arc-second NAVD 88 Coastal Digital Elevation Model, type: dataset, NOAA National Centers for Environmental Information, Silver Spring, Maryland, USA, 2010. a
Oishi, Y., Imamura, F., and Sugawara, D.:
Near-field tsunami inundation forecast using the parallel TUNAMI-N2 model: Application to the 2011 Tohoku-Oki earthquake combined with source inversions, Geophys. Res. Lett., 42, 1083–1091, 2015. a
Park, H., Cox, D. T., Lynett, P. J., Wiebe, D. M., and Shin, S.:
Tsunami inundation modeling in constructed environments: A physical and numerical comparison of free-surface elevation, velocity, and momentum flux, Coast. Eng., 79, 9–21, 2013. a
Pelties, C., de la Punte, P., Ampuero, J.-P., Brietzke, G., and Käser, M.:
Three-dimensional dynamic rupture simulation with a high-order discontinuous Galerkin method on unstructured tetrahedral meshes, J. Geophys. Res., 117, 2156–2202, 2012. a
Petersen, M. D., Cramer, C. H., and Frankel, A. D.:
Simulations of seismic hazard for the Pacific Northwest of the United States from earthquakes associated with the Cascadia subduction zone, in: Earthquake Processes: Physical Modelling, Numerical Simulation and Data Analysis Part I, Springer, Berlin, Germany, 2147–2168, https://doi.org/10.1007/s00024-002-8728-5, 2002. a, b
Peterson, M. and Lowe, M.:
Implications of Cumulative Impacts to Estuarine and Marine Habitat Quality for Fish and Invertebrate Resources, Rev. Fish. Sci., 17, 505–523, 2009. a
Prasetyo, A., Yasuda, T., Miyashita, T., and Mori, N.:
Physical Modeling and Numerical Analysis of Tsunami Inundation in a Coastal City, Front. Built Environ., 5, 46, https://doi.org/10.3389/fbuil.2019.00046, 2019. a
Rasp, S., Pritchard, M. S., and Gentine, P.:
Deep learning to represent subgrid processes in climate models, P. Natl. Acad. Sci. USA, 115, 9684–9689, 2018. a
Roelvink, J. and Van Banning, G.:
Design and development of DELFT3D and application to coastal morphodynamics, Oceanographic Lit. Review, 42, 925, 1995. a
Ruggiero, P.:
Impacts of climate change on coastal erosion and flood probability in the US Pacific Northwest, in: Solutions to Coastal Disasters 2008, Oahu, Hawaii, USA, 13–16 April 2008, 158–169, 2008. a
Ruggiero, P.:
Is the intensifying wave climate of the US Pacific Northwest increasing flooding and erosion risk faster than sea-level rise?, J. Waterw. Port Coast., 139, 88–97, 2013. a
Ruggiero, P., Komar, P. D., and Allan, J. C.:
Increasing wave heights and extreme value projections: The wave climate of the US Pacific Northwest, Coast. Eng., 57, 539–552, 2010. a
Ruggiero, P., Kratzmann, M. G., Himmelstoss, E. A., Reid, D., Allan, J., and Kaminsky, G.:
National assessment of shoreline change: historical shoreline change along the Pacific Northwest coast, Tech. rep., US Geological Survey, Reston, Virginia, USA, 2013. a
Satake, K., Shimazaki, K., Tsuji, Y., and Ueda, K.:
Time and size of a giant earthquake in Cascadia inferred from Japanese tsunami records of January 1700, Nature, 379, 246–249, 1996. a
Satria, M. T., Huang, B., Hsieh, T.-J., Chang, Y.-L., and Liang, W.-Y.:
GPU Acceleration of Tsunami Propagation Model, IEEE J. Sel. Top. Appl., 5, 1014–1023, https://doi.org/10.1109/JSTARS.2012.2199468, 2012. a
Shi, F., Kirby, J., Harris, J., Geiman, J., and Grilli, S.:
A high-order adaptive time-stepping TVD solver for Boussinesq modeling of breaking waves and coastal inundation, Ocean Model., 43–44, 36–51, 2012. a
Shu, C.-W.:
Total-Variation-Diminishing Time Discretizations, SIAM J. Sci. Stat. Comp., 9, 1073–1084, https://doi.org/10.1137/0909073, 1988. a, b
Stoker, J. J.:
Water Waves, the Mathematical Theory with Applications, Interscience Publishers, New York, New York, USA, 333–341, ISBN-13: 978-0471570349, 1957. a
Takada, K. and Atwater, B. F.:
Evidence for liquefaction identified in peeled slices of Holocene deposits along the lower Columbia River, Washington, B. Seismol. Soc. Am., 94, 550–575, 2004. a
Thacker, W. C.:
Some exact solutions to the nonlinear shallow-water wave equations, J. Fluid Mech., 107, 499, https://doi.org/10.1017/s0022112081001882, 1981. a
Titov, V., González, F., Bernard, E., Eble, M. C., Mofjeld, H., Newman, J. C., and Venturato, A.:
Real-Time Tsunami Forecasting: Challenges and Solutions, Nat. Hazards, 35, 41–58, 2005. a
tsunamiTPUlab:
tsunamiTPUlab, Github [code and dataset], https://github.com/smarras79/tsunamiTPUlab/releases/tag/v1.0.0 (last access: 12 June 2023), 2023. a
Ueno, T.:
Numerical computations for the Chilean Earthquak Tsunami, Oceanogr. Mag., 17, 87–94, 1960. a
Ulrich, T., Vater, S., Madden, E., Behrens, J., van Dinther, Y., van Zelst, I., Fielding, J., Liang, C., and Gabriel, A.-A.:
Coupled, Physics-Based Modeling Reveals Earthquake Displacements are Critical to the 2018 Palu, Sulawesi Tsunami, Pure Appl. Geophys., 176, 4069–4109, 2019. a
Wang, Q., Ihme, M., Chen, Y.-F., and Anderson, J.:
A TensorFlow simulation framework for scientific computing of fluid flows on tensor processing units, Comput. Phys. Commun., 274, 108292, https://doi.org/10.1016/j.cpc.2022.108292, 2022. a
Wessels, H., Weißenfels, C., and Wriggers, P.:
The neural particle method – An updated Lagrangian physics informed neural network for computational fluid dynamics, Comp. Method. Appl. M., 368, 113127, https://doi.org/10.1016/j.cma.2020.113127, 2020. a
Xia, X. and Liang, Q.:
A new efficient implicit scheme for discretising the stiff friction terms in the shallow water equations, Adv. Water Resour., 117, 87–97, https://doi.org/10.1016/j.advwatres.2018.05.004, 2018.
a, b
Xing, Y. and Shu, C.-W.:
High order finite difference WENO schemes with the exact conservation property for the shallow water equations, J. Comput. Phys., 208, 206–227, https://doi.org/10.1016/j.jcp.2005.02.006, 2005. a, b
Zhang, Q., Cheng, L., and Boutaba, R.:
Cloud computing: state-of-the-art and research challenges, Journal of Internet Services and Applications, 1, 7–18, https://doi.org/10.1007/s13174-010-0007-6, 2010. a
Short summary
To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.
To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance...
