Development and technical paper
Development and technical paper | 15 Jul 2019
Efficiency and robustness in Monte Carlo sampling for 3-D geophysical inversions with Obsidian v0.1.2: setting up for success
Richard Scalzo et al.
Blockworlds 0.1.0: a demonstration of anti-aliased geophysics for probabilistic inversions of implicit and kinematic geological models
Richard Scalzo, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps
This paper addresses numerical challenges in reasoning about geological models constrained by sensor data, especially models that describe the history of an area in terms of a sequence of events. Our method ensures that small changes in simulated geological features, such as the position of a boundary between two rock layers, do not result in unrealistically large changes to resulting sensor measurements, as occur presently using several popular modeling packages.
Into the Noddyverse: a massive data store of 3D geological models for machine learning and inversion applications
Mark Jessell, Jiateng Guo, Yunqiang Li, Mark Lindsay, Richard Scalzo, Jérémie Giraud, Guillaume Pirot, Ed Cripps, and Vitaliy Ogarko
To robustly train and test automated methods in the geosciences, we need to have access to large numbers of examples where we know the answer . We present a suite of synthetic 3D geological models with their gravity and magnetic responses that allow researchers to test their methods on a whole range of geologically plausible models, thus overcoming one of the fundamental limitations of automation studies.
Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models
Hugo K. H. Olierook, Richard Scalzo, David Kohn, Rohitash Chandra, Ehsan Farahbakhsh, Gregory Houseman, Chris Clark, Steven M. Reddy, and R. Dietmar Müller
Solid Earth Discuss., https://doi.org/10.5194/se-2019-4,https://doi.org/10.5194/se-2019-4, 2019
Resolving multiple geological events using in situ Rb–Sr geochronology: implications for metallogenesis at Tropicana, Western Australia
Hugo K. H. Olierook, Kai Rankenburg, Stanislav Ulrich, Christopher L. Kirkland, Noreen J. Evans, Stephen Brown, Brent I. A. McInnes, Alexander Prent, Jack Gillespie, Bradley McDonald, and Miles Darragh
Geochronology, 2, 283–303, https://doi.org/10.5194/gchron-2-283-2020,https://doi.org/10.5194/gchron-2-283-2020, 2020
Using a relatively new dating technique, in situ Rb–Sr geochronology, we constrain the ages of two generations of mineral assemblages from the Tropicana Zone, Western Australia. The first, dated at ca. 2535 Ma, is associated with exhumation of an Archean craton margin and gold mineralization. The second, dated at ca. 1210 Ma, has not been previously documented in the Tropicana Zone. It is probably associated with Stage II of the Albany–Fraser Orogeny and additional gold mineralization.
Surrogate-assisted Bayesian inversion for landscape and basin evolution models
Rohitash Chandra, Danial Azam, Arpit Kapoor, and R. Dietmar Müller
Forward landscape and sedimentary basin evolution models pose a major challenge in the development of efficient inference and optimization methods. Bayesian inference provides a methodology for estimation and uncertainty quantification of free model parameters. In this paper, we present an application of a surrogate-assisted Bayesian parallel tempering method where that surrogate mimics a landscape evolution model. We use the method for parameter estimation and uncertainty quantification.
Near-surface structure of the North Anatolian Fault zone from Rayleigh and Love wave tomography using ambient seismic noise
George Taylor, Sebastian Rost, Gregory A. Houseman, and Gregor Hillers
Solid Earth, 10, 363–378, https://doi.org/10.5194/se-10-363-2019,https://doi.org/10.5194/se-10-363-2019, 2019
We constructed a seismic velocity model of the North Anatolian Fault in Turkey. We found that the fault is located within a region of reduced seismic velocity and skirts the edges of a geological unit that displays high seismic velocity, indicating that this unit could be stronger than the surrounding material. Furthermore, we found that seismic waves travel fastest in the NE–SW direction, which is the direction of maximum extension for this part of Turkey and indicates mineral alignment.
Predicting peak daily maximum 8 h ozone and linkages to emissions and meteorology in Southern California using machine learning methods (SoCAB-8HR V1.0)
Ziqi Gao, Yifeng Wang, Petros Vasilakos, Cesunica E. Ivey, Khanh Do, and Armistead G. Russell
While the national ambient air quality standard of ozone is based on the 3-year average of the fourth highest 8 h maximum (MDA8) ozone concentrations, these predicted extreme values using numerical methods are always biased low. We built four computational models (GAM, MARS, random forest and SVR) to predict the fourth highest MDA8 ozone in Southern California using precursor emissions, meteorology and climatological patterns. All models presented acceptable performance, with GAM being the best.
Transfer learning for landslide susceptibility modeling using domain adaptation and case-based reasoning
Zhihao Wang, Jason Goetz, and Alexander Brenning
A lack of inventory data can be a limiting factor in developing landslide predictive models, which are crucial for supporting hazard policy and decision-making. We show how case-based reasoning and domain adaptation (transfer-learning techniques) can effectively retrieve similar landslide modeling situations for prediction in new data-scarce areas. Using cases in Italy, Austria, and Ecuador, our findings support the application of transfer learning for areas that require rapid model development.
ISMIP-HOM benchmark experiments using Underworld
Till Sachau, Haibin Yang, Justin Lang, Paul D. Bons, and Louis Moresi
Knowledge of the internal structures of the major continental ice sheets is improving, thanks to new investigative techniques. These structures are an essential indication of the flow behavior and dynamics of ice transport, which in turn is important for understanding the actual impact of the vast amounts of water trapped in continental ice sheets on global sea-level rise. The software studied here is specifically designed to simulate such structures and their evolution.
spyro: a Firedrake-based wave propagation and full-waveform-inversion finite-element solver
Keith J. Roberts, Alexandre Olender, Lucas Franceschini, Robert C. Kirby, Rafael S. Gioria, and Bruno S. Carmo
Finite-element methods (FEMs) permit the use of more flexible unstructured meshes but are rarely used in full waveform inversions (FWIs), an iterative process that reconstructs velocity models of earth's subsurface, due to computational and memory storage costs. To reduce those costs, novel software is presented allowing the use of high-order mass-lumped FEMs on triangular meshes, together with a material-property mesh-adaptation performance-enhancing strategy, enabling its use in FWIs.
Spatial filtering in a 6D hybrid-Vlasov scheme to alleviate adaptive mesh refinement artifacts: a case study with Vlasiator (versions 5.0, 5.1, and 5.2.1)
Konstantinos Papadakis, Yann Pfau-Kempf, Urs Ganse, Markus Battarbee, Markku Alho, Maxime Grandin, Maxime Dubart, Lucile Turc, Hongyang Zhou, Konstantinos Horaites, Ivan Zaitsev, Giulia Cozzani, Maarja Bussov, Evgeny Gordeev, Fasil Tesema, Harriet George, Jonas Suni, Vertti Tarvus, and Minna Palmroth
Vlasiator is a plasma simulation code that simulates the entire near-Earth space at a global scale. As 6D simulations require enormous amounts of computational resources, Vlasiator uses adaptive mesh refinement (AMR) to lighten the computational burden. However, due to Vlasiator's grid topology, AMR simulations suffer from grid aliasing artifacts that affect the global results. In this work, we present and evaluate the performance of a mechanism for alleviating those artifacts.
A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1
Artur Safin, Damien Bouffard, Firat Ozdemir, Cintia L. Ramón, James Runnalls, Fotis Georgatos, Camille Minaudo, and Jonas Šukys
Reconciling the differences between numerical model predictions and observational data is always a challenge. In this paper, we investigate the viability of a novel approach to the calibration of a three-dimensional hydrodynamic model of Lake Geneva, where the target parameters are inferred in terms of distributions. We employ a filtering technique that generates physically consistent model trajectories and implement a neural network to enable bulk-to-skin temperature conversion.
A fast, single-iteration ensemble Kalman smoother for sequential data assimilation
Colin Grudzien and Marc Bocquet
Iterative optimization techniques, the state of the art in data assimilation, have largely focused on extending forecast accuracy to moderate- to long-range forecast systems. However, current methodology may not be cost-effective in reducing forecast errors in online, short-range forecast systems. We propose a novel optimization of these techniques for online, short-range forecast cycles, simultaneously providing an improvement in forecast accuracy and a reduction in the computational cost.
Characterizing uncertainties of Earth system modeling with heterogeneous many-core architecture computing
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Lixin Wu, Dexun Chen, Yang Gao, Zhiqiang Wei, Dongning Jia, and Xiaopei Lin
To understand the scientific consequence of perturbations caused by slave cores in heterogeneous computing environments, we examine the influence of perturbation amplitudes on the determination of the cloud bottom and cloud top and compute the probability density function (PDF) of generated clouds. A series of comparisons of the PDFs between homogeneous and heterogeneous systems show consistently acceptable error tolerances when using slave cores in heterogeneous computing environments.
Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol applied to Earth system models
Vijay S. Mahadevan, Jorge E. Guerra, Xiangmin Jiao, Paul Kuberry, Yipeng Li, Paul Ullrich, David Marsico, Robert Jacob, Pavel Bochev, and Philip Jones
Coupled Earth system models require transfer of field data between multiple components with varying spatial resolutions to determine the correct climate behavior. We present the Metrics for Intercomparison of Remapping Algorithms (MIRA) protocol to evaluate the accuracy, conservation properties, monotonicity, and local feature preservation of four different remapper algorithms for various unstructured mesh problems of interest. Future extensions to more practical use cases are also discussed.
Impact of the numerical solution approach of a plant hydrodynamic model (v0.1) on vegetation dynamics
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Islet: interpolation semi-Lagrangian element-based transport
Andrew M. Bradley, Peter A. Bosler, and Oksana Guba
Tracer transport in atmosphere models can be computationally expensive. We describe a flexible and efficient interpolation semi-Lagrangian method, the Islet method. It permits using up to three grids that share an element grid: a dynamics grid for computing quantities such as the wind velocity; a physics parameterizations grid; and a tracer grid. The Islet method performs well on a number of verification problems and achieves high performance in the E3SM Atmosphere Model version 2.
Intelligent prospector v1.0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration
John Mern and Jef Caers
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-166,https://doi.org/10.5194/gmd-2022-166, 2022
Revised manuscript accepted for GMD
In this work, we formulate sequential geoscientific data acquisition problem as problem that is similar to playing chess against nature, except the pieces are not fully observed. Solutions to these problems are given in AI, and rarely used in geoscientific data planning. We illustrate our approach to a simple 2D problem of mineral exploration.
Multi-dimensional hydrological–hydraulic model with variational data assimilation for river networks and floodplains
Léo Pujol, Pierre-André Garambois, and Jérôme Monnier
This contribution presents a new numerical model for representing hydraulic–hydrological quantities at the basin scale. It allows modeling large areas at a low computational cost, with fine zooms where needed. It allows the integration of local and satellite measurements, via data assimilation methods, to improve the model's match to observations. Using this capability, good matches to in situ observations are obtained on a model of the complex Adour river network with fine zooms on floodplains.
Assessing the robustness and scalability of the accelerated pseudo-transient method
Ludovic Räss, Ivan Utkin, Thibault Duretz, Samuel Omlin, and Yuri Y. Podladchikov
Continuum mechanics-based modelling of physical processes at large scale requires huge computational resources provided by massively parallel hardware such as graphical processing units. We present a suite of numerical algorithms, implemented using the Julia language, that efficiently leverages the parallelism. We demonstrate that our implementation is efficient, scalable and robust and showcase applications to various geophysical problems.
Assessment of stochastic weather forecast of precipitation near European cities, based on analogs of circulation
Meriem Krouma, Pascal Yiou, Céline Déandreis, and Soulivanh Thao
We evaluated the skill of a stochastic weather generator (SWG) to forecast precipitation at different time scales and in different areas of western Europe from analogs of Z500 hPa. The SWG has the skill to simulate precipitation for 5 and 10 d. We found that forecast weaknesses can be associated with specific weather patterns. The comparison with ECMWF forecasts confirms the skill of our model. This work is important because it provides information about weather forecasts over specific areas.
University of Warsaw Lagrangian Cloud Model (UWLCM) 2.0: adaptation of a mixed Eulerian–Lagrangian numerical model for heterogeneous computing clusters
Piotr Dziekan and Piotr Zmijewski
Detailed computer simulations of clouds are important for understanding Earth's atmosphere and climate. The paper describes how the UWLCM has been adapted to work on supercomputers. A distinctive feature of UWLCM is that air flow is calculated by processors at the same time as cloud droplets are modeled by graphics cards. Thanks to this, use of computing resources is maximized and the time to complete simulations of large domains is not affected by communications between supercomputer nodes.
Prediction error growth in a more realistic atmospheric toy model with three spatiotemporal scales
Hynek Bednář and Holger Kantz
A scale-dependent error growth described by a power law or by a quadratic hypothesis is studied in Lorenz's system with three spatiotemporal levels. The validity of power law is extended by including a saturation effect. The quadratic hypothesis can only serve as a first guess. In addition, we study the initial error growth for the ECMWF forecast system. Fitting the parameters, we conclude that there is an intrinsic limit of predictability after 22 days.
On numerical broadening of particle-size spectra: a condensational growth study using PyMPDATA 1.0
Michael A. Olesik, Jakub Banaśkiewicz, Piotr Bartman, Manuel Baumgartner, Simon Unterstrasser, and Sylwester Arabas
In systems such as atmospheric clouds, droplets undergo growth through condensation of vapor. The broadness of the resultant size spectrum of droplets influences precipitation likelihood and the radiative properties of clouds. One of the inherent limitations of simulations of the problem is the so-called numerical diffusion causing overestimation of the spectrum width, hence the term numerical broadening. In the paper, we take a closer look at one of the algorithms used in this context: MPDATA.
Lossy checkpoint compression in full waveform inversion: a case study with ZFPv0.5.5 and the overthrust model
Navjot Kukreja, Jan Hückelheim, Mathias Louboutin, John Washbourne, Paul H. J. Kelly, and Gerard J. Gorman
Full waveform inversion (FWI) is a partial-differential equation (PDE)-constrained optimization problem that is notorious for its high computational load and memory footprint. In this paper we present a method that combines recomputation with lossy compression to accelerate the computation with minimal loss of precision in the results. We show this using experiments running FWI with a variety of compression settings on a popular academic dataset.
Efficient high-dimensional variational data assimilation with machine-learned reduced-order models
Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, and Rao Kotamarthi
In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.
Improved double Fourier series on a sphere and its application to a semi-implicit semi-Lagrangian shallow-water model
Hiromasa Yoshimura
This paper proposes a new double Fourier series (DFS) method on a sphere that improves the numerical stability of a model compared with conventional DFS methods. The shallow-water model and the advection model using the new DFS method give stable results without the appearance of high-wavenumber noise near the poles. The model using the new DFS method is faster than the model using spherical harmonics (especially at high resolutions) and gives almost the same results.
SciKit-GStat 1.0: a SciPy-flavored geostatistical variogram estimation toolbox written in Python
Mirko Mälicke
I preset SciKit-GStat, a well-documented and tested Python package for variogram estimation. The variogram is the core means of geostatistics, which almost all other methods rely on. Geostatistical interpolation and field generation are widely spread in geoscience, i.e., for data assimilation or modeling. While SciKit-GStat focuses on effective and intuitive variogram estimation, it can interface with other prominent packages and make its variograms available for a multitude of methods.
Flow-Py v1.0: a customizable, open-source simulation tool to estimate runout and intensity of gravitational mass flows
Christopher J. L. D'Amboise, Michael Neuhauser, Michaela Teich, Andreas Huber, Andreas Kofler, Frank Perzl, Reinhard Fromm, Karl Kleemayr, and Jan-Thomas Fischer
The term gravitational mass flow (GMF) covers various natural hazard processes such as snow avalanches, rockfall, landslides, and debris flows. Here we present the open-source GMF simulation tool Flow-Py. The model equations are based on simple geometrical relations in three-dimensional terrain. We show that Flow-Py is an educational, innovative GMF simulation tool with three computational experiments: 1. validation of implementation, 2. performance, and 3. expandability.
Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES
Evan Baker, Anna B. Harper, Daniel Williamson, and Peter Challenor
We have adapted machine learning techniques to build a model of the land surface in Great Britain. The model was trained using data from a very complex land surface model called JULES. Our model is faster at producing simulations and predictions and can investigate many different scenarios, which can be used to improve our understanding of the climate and could also be used to help make local decisions.
A three-dimensional variational data assimilation system for aerosol optical properties based on WRF-Chem v4.0: design, development, and application of assimilating Himawari-8 aerosol observations
Daichun Wang, Wei You, Zengliang Zang, Xiaobin Pan, Yiwen Hu, and Yanfei Liang
This paper presents a 3D variational data assimilation system for aerosol optical properties, including aerosol optical thickness (AOT) retrievals and lidar-based aerosol profiles, which was developed for a size-resolved sectional model in WRF-Chem. To directly assimilate aerosol optical properties, an observation operator based on the Mie scattering theory was designed. The results show that Himawari-8 AOT assimilation can significantly improve model aerosol analyses and forecasts.
Implementation of a Gaussian Markov random field sampler for forward uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19
Kevin Bulthuis and Eric Larour
We present and implement a stochastic solver to sample spatially and temporal varying uncertain input parameters in the Ice-sheet and Sea-level System Model, such as ice thickness or surface mass balance. We represent these sources of uncertainty using Gaussian random fields with Matérn covariance function. We generate random samples of this random field using an efficient computational approach based on solving a stochastic partial differential equation.
A method for assessment of the general circulation model quality using the K-means clustering algorithm: a case study with GETM v2.5
Urmas Raudsepp and Ilja Maljutenko
A model's ability to reproduce the state of a simulated object is always a subject of discussion. A new method for the multivariate assessment of numerical model skills uses the K-means algorithm for clustering model errors. All available data that fall into the model domain and simulation period are incorporated into the skill assessment. The clustered errors are used for spatial and temporal analysis of the model accuracy. The method can be applied to different types of geoscientific models.
An explicit GPU-based material point method solver for elastoplastic problems (ep2-3De v1.0)
Emmanuel Wyser, Yury Alkhimenkov, Michel Jaboyedoff, and Yury Y. Podladchikov
We propose an implementation of the material point method using graphical processing units (GPUs) to solve elastoplastic problems in three-dimensional configurations, such as the granular collapse or the slumping mechanics, i.e., landslide. The computational power of GPUs promotes fast code executions, compared to a traditional implementation using central processing units (CPUs). This allows us to study complex three-dimensional problems tackling high spatial resolution.
MagIC v5.10: a two-dimensional message-passing interface (MPI) distribution for pseudo-spectral magnetohydrodynamics simulations in spherical geometry
Rafael Lago, Thomas Gastine, Tilman Dannert, Markus Rampp, and Johannes Wicht
In this work we discuss a two-dimensional distributed parallelization of MagIC, an open-source code for the numerical solution of the magnetohydrodynamics equations. Such a parallelization involves several challenges concerning the distribution of work and data. We detail our algorithm and compare it with the established, optimized, one-dimensional distribution in the context of the dynamo benchmark and discuss the merits of both implementations.
Machine-learning models to replicate large-eddy simulations of air pollutant concentrations along boulevard-type streets
Moritz Lange, Henri Suominen, Mona Kurppa, Leena Järvi, Emilia Oikarinen, Rafael Savvides, and Kai Puolamäki
This study aims to replicate computationally expensive high-resolution large-eddy simulations (LESs) with regression models to simulate urban air quality and pollutant dispersion. The model development, including feature selection, model training and cross-validation, and detection of concept drift, has been described in detail. Of the models applied, log-linear regression shows the best performance. A regression model can replace LES unless high accuracy is needed.
Recalculation of error growth models' parameters for the ECMWF forecast system
Hynek Bednář, Aleš Raidl, and Jiří Mikšovský
Forecast errors in numerical weather prediction systems grow in time. To quantify the impacts of this growth, parametric error growth models may be employed. This study recalculates and newly defines parameters for several statistic models approximating error growth in the ECMWF forecasting system. Accurate values of parameters are important because they are used to evaluate improvements of the forecasting systems or to estimate predictability.
How biased are our models? – a case study of the alpine region
Denise Degen, Cameron Spooner, Magdalena Scheck-Wenderoth, and Mauro Cacace
In times of worldwide energy transitions, an understanding of the subsurface is increasingly important to provide renewable energy sources such as geothermal energy. To validate our understanding of the subsurface we require data. However, the data are usually not distributed equally and introduce a potential misinterpretation of the subsurface. Therefore, in this study we investigate the influence of measurements on temperature distribution in the European Alps.
B-flood 1.0: an open-source Saint-Venant model for flash-flood simulation using adaptive refinement
Geoffroy Kirstetter, Olivier Delestre, Pierre-Yves Lagrée, Stéphane Popinet, and Christophe Josserand
The development of forecasting tools may help to limit the impacts of flash floods. Our purpose here is to demonstrate the possibility of using b-flood, which is a 2D tool based on shallow-water equations and adaptive mesh refinement.
A micro-genetic algorithm (GA v1.7.1a) for combinatorial optimization of physics parameterizations in the Weather Research and Forecasting model (v4.0.3) for quantitative precipitation forecast in Korea
Sojung Park and Seon K. Park
One of the biggest uncertainties in numerical weather predictions (NWPs) comes from treating subgrid-scale physical processes. Physical processes, such as cumulus, microphysics, and planetary boundary layer processes, are parameterized in NWP models by empirical and theoretical backgrounds. We developed an interface between a micro-genetic algorithm and the WRF model for a combinatorial optimization of physics for heavy rainfall events in Korea. The system improved precipitation forecasts.
SymPKF (v1.0): a symbolic and computational toolbox for the design of parametric Kalman filter dynamics
Olivier Pannekoucke and Philippe Arbogast
This contributes to research on uncertainty prediction, which is important either for determining the weather today or estimating the risk in prediction. The problem is that uncertainty prediction is numerically very expensive. An alternative has been proposed wherein uncertainty is presented in a simplified form with only the dynamics of certain parameters required. This tool allows for the determination of the symbolic equations of these parameter dynamics and their numerical computation.
NDCmitiQ v1.0.0: a tool to quantify and analyse greenhouse gas mitigation targets
Annika Günther, Johannes Gütschow, and Mairi Louise Jeffery
The mitigation components of the nationally determined contributions (NDCs) under the Paris Agreement are essential in our fight against climate change. Regular updates with increased ambition are requested to limit global warming to 1.5–2 °C. The new and easy-to-update open-source tool NDCmitiQ can be used to quantify the NDCs' mitigation targets and construct resulting emissions pathways. In use cases, we show target uncertainties from missing clarity, data, and methodological challenges.
Combining ensemble Kalman filter and reservoir computing to predict spatiotemporal chaotic systems from imperfect observations and models
Futo Tomizawa and Yohei Sawada
A new method to predict chaotic systems from observation and process-based models is proposed by combining machine learning with data assimilation. Our method is robust to the sparsity of observation networks and can predict more accurately than a process-based model when it is biased. Our method effectively works when both observations and models are imperfect, which is often the case in geoscience. Therefore, our method is useful to solve a wide variety of prediction problems in this field.
The Coastline Evolution Model 2D (CEM2D) V1.1
Chloe Leach, Tom Coulthard, Andrew Barkwith, Daniel R. Parsons, and Susan Manson
Numerical models can be used to understand how coastal systems evolve over time, including likely responses to climate change. However, many existing models are aimed at simulating 10- to 100-year time periods do not represent a vertical dimension and are thus unable to include the effect of sea-level rise. The Coastline Evolution Model 2D (CEM2D) presented in this paper is an advance in this field, with the inclusion of the vertical coastal profile against which the water level can be altered.
An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3
Steven J. Phipps, Jason L. Roberts, and Matt A. King
Simplified schemes, known as parameterisations, are sometimes used to describe physical processes within numerical models. However, the values of the parameters are uncertain. This introduces uncertainty into the model outputs. We develop a simple approach to identify plausible ranges for model parameters. Using a model of the Antarctic Ice Sheet, we find that the value of one parameter can depend on the values of others. We conclude that a single optimal set of parameter values does not exist.
Ocean Plastic Assimilator v0.2: assimilation of plastic concentration data into Lagrangian dispersion models
Axel Peytavin, Bruno Sainte-Rose, Gael Forget, and Jean-Michel Campin
We present a new algorithm developed at The Ocean Cleanup to update ocean plastic models based on measurements from the field to improve future cleaning operations. Prepared in collaboration with MIT researchers, this initial study presents its use in several analytical and real test cases in which two observers in a flow field record regular observations to update a plastic forecast. We demonstrate this improves the prediction, even with inaccurate knowledge of the water flows driving plastic.
Development of a moving point source model for shipping emission dispersion modeling in EPISODE–CityChem v1.3
Kang Pan, Mei Qi Lim, Markus Kraft, and Epaminondas Mastorakos
A new moving point source (MPS) model was developed to simulate the dispersion of emissions generated by the moving ships. Compared to the commonly used line source (LS) or fixed point source (FPS) model, the MPS model provides more emission distribution details generated by the moving ships and matches reasonably with the measurements. Therefore, the MPS model should be a valuable alternative for the environmental society to evaluate the pollutant dispersion contributed from the moving ships.
Efficient Bayesian inference for large chaotic dynamical systems
Sebastian Springer, Heikki Haario, Jouni Susiluoto, Aleksandr Bibov, Andrew Davis, and Youssef Marzouk
Model predictions always contain uncertainty. But in some cases, such as weather forecasting or climate modeling, chaotic unpredictability increases the difficulty to say exactly how much uncertainty there is. We combine two recently proposed mathematical methods to show how the uncertainty can be analyzed in models that are simplifications of true weather models. The results can be extended in the future to show how forecasts from large-scale models can be improved.
Constraining stochastic 3-D structural geological models with topology information using approximate Bayesian computation in GemPy 2.1
Alexander Schaaf, Miguel de la Varga, Florian Wellmann, and Clare E. Bond
Uncertainty is an inherent property of any model of the subsurface. We show how geological topology information – how different regions of rocks in the subsurface are connected – can be used to train uncertain geological models to reduce uncertainty. More widely, the method demonstrates the use of probabilistic machine learning (Bayesian inference) to train structural geological models on auxiliary geological knowledge that can be encoded in graph structures.
Retrieval of process rate parameters in the general dynamic equation for aerosols using Bayesian state estimation: BAYROSOL1.0
Matthew Ozon, Aku Seppänen, Jari P. Kaipio, and Kari E. J. Lehtinen
Experimental research has provided large amounts of high-quality data on aerosol over the last 2 decades. However, inference of the process rates (e.g., the rates at which particles are generated) is still typically done by simple curve-fitting methods and does not assess the credibility of the estimation. The devised method takes advantage of the Bayesian framework to not only retrieve the state of the observed aerosol system but also to estimate the process rates (e.g., growth rate).
A discontinuous Galerkin finite-element model for fast channelized lava flows v1.0
Colton J. Conroy and Einat Lev
Lava flows present a natural hazard to communities around volcanoes and are usually slow-moving (< 1-5 cm/s). Lava flows during the 2018 eruption of Kilauea volcano, Hawai'i, however, reached speeds as high as 11 m/s. To investigate these dynamics we develop a new lava flow computer model that incorporates a nonlinear expression for the fluid viscosity. Model results indicate that the lava flows at Site 8 of the eruption displayed shear thickening behavior due to the flow's high bubble content.
A nested multi-scale system implemented in the large-eddy simulation model PALM model system 6.0
Antti Hellsten, Klaus Ketelsen, Matthias Sühring, Mikko Auvinen, Björn Maronga, Christoph Knigge, Fotios Barmpas, Georgios Tsegas, Nicolas Moussiopoulos, and Siegfried Raasch
Large-eddy simulation (LES) of the urban atmospheric boundary layer involves a large separation of turbulent scales, leading to prohibitive computational costs. An online LES–LES nesting scheme is implemented into the PALM model system 6.0 to overcome this problem. Test results show that the accuracy within the high-resolution nest domains approach the non-nested high-resolution reference results. The nesting can reduce the CPU by time up to 80 % compared to the fine-resolution reference runs.
Extending legacy climate models by adaptive mesh refinement for single-component tracer transport: a case study with ECHAM6-HAMMOZ (ECHAM6.3-HAM2.3-MOZ1.0)
Yumeng Chen, Konrad Simon, and Jörn Behrens
Mesh adaptivity can reduce overall model error by only refining meshes in specific areas where it us necessary in the runtime. Here we suggest a way to integrate mesh adaptivity into an existing Earth system model, ECHAM6, without having to redesign the implementation from scratch. We show that while the additional computational effort is manageable, the error can be reduced compared to a low-resolution standard model using an idealized test and relatively realistic dust transport tests.
Using the Després and Lagoutière (1999) antidiffusive transport scheme: a promising and novel method against excessive vertical diffusion in chemistry-transport models
Sylvain Mailler, Romain Pennel, Laurent Menut, and Mathieu Lachâtre
Representing the advection of thin polluted plumes in numerical models is a challenging task since these models usually tend to excessively diffuse these plumes in the vertical direction. This numerical diffusion process is the cause of major difficulties in representing such dense and thin polluted plumes in numerical models. We propose here, and test in an academic framework, a novel method to solve this problem through the use of an antidiffusive advection scheme in the vertical direction.
Porosity and permeability prediction through forward stratigraphic simulations using GPM™ and Petrel™: application in shallow marine depositional settings
Daniel Otoo and David Hodgetts
The forward stratigraphic simulation method is used to predict lithofacies, porosity, and permeability in a reservoir model. The objective of using this approach is to enhance subsurface property modelling through geologic realistic 3-D stratigraphic patterns. Results show realistic stratigraphic sequences. Given this, we can derive spatial and geometric data as secondary data to constrain property simulation in a reservoir model. The approach can reduce the uncertainty of property modelling.
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Producing 3-D models of structures under the Earth's surface based on sensor data is a key problem in geophysics (for example, in mining exploration). There may be multiple models that explain the data well. We use the open-source Obsidian software to look at the efficiency of different methods for exploring the model space and attaching probabilities to models, leading to less biased results and a better idea of how sensor data interact with geological assumptions.
Producing 3-D models of structures under the Earth's surface based on sensor data is a key...