Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-3715-2021
© Author(s) 2021. 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-14-3715-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
22 Jun 2021
Development and technical paper |
| 22 Jun 2021
| 22 Jun 2021
Retrieval of process rate parameters in the general dynamic equation for aerosols using Bayesian state estimation: BAYROSOL1.0
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Aku Seppänen
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Jari P. Kaipio
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Department of Mathematics, Faculty of Science, University of Auckland, Auckland, New Zealand
Kari E. J. Lehtinen
Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
Finnish Meteorological Institute, Kuopio, Finland
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Ekaterina Ezhova, Veli-Matti Kerminen, Kari E. J. Lehtinen, and Markku Kulmala
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Lukas Pichelstorfer, Dominik Stolzenburg, John Ortega, Thomas Karl, Harri Kokkola, Anton Laakso, Kari E. J. Lehtinen, James N. Smith, Peter H. McMurry, and Paul M. Winkler
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Elham Baranizadeh, Tuomo Nieminen, Taina Yli-Juuti, Markku Kulmala, Tuukka Petäjä, Ari Leskinen, Mika Komppula, Ari Laaksonen, and Kari E. J. Lehtinen
Atmos. Chem. Phys., 17, 13361–13371, https://doi.org/10.5194/acp-17-13361-2017, https://doi.org/10.5194/acp-17-13361-2017, 2017
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Extrapolation of the particle formation rates from one measured larger size (e.g., 7 nm) to smaller sizes (e.g., 3 nm) based on simplified growth-scavenging dynamics works fairly well to estimate mean daily formation rates, but it fails to predict the time evolution of the particle population. This points to the challenges in predicting atmospheric nucleation rates for locations where the particle growth and loss rates are size- and time-dependent.
Petri Tiitta, Ari Leskinen, Liqing Hao, Pasi Yli-Pirilä, Miika Kortelainen, Julija Grigonyte, Jarkko Tissari, Heikki Lamberg, Anni Hartikainen, Kari Kuuspalo, Aki-Matti Kortelainen, Annele Virtanen, Kari E. J. Lehtinen, Mika Komppula, Simone Pieber, André S. H. Prévôt, Timothy B. Onasch, Douglas R. Worsnop, Hendryk Czech, Ralf Zimmermann, Jorma Jokiniemi, and Olli Sippula
Atmos. Chem. Phys., 16, 13251–13269, https://doi.org/10.5194/acp-16-13251-2016, https://doi.org/10.5194/acp-16-13251-2016, 2016
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Elham Baranizadeh, Benjamin N. Murphy, Jan Julin, Saeed Falahat, Carly L. Reddington, Antti Arola, Lars Ahlm, Santtu Mikkonen, Christos Fountoukis, David Patoulias, Andreas Minikin, Thomas Hamburger, Ari Laaksonen, Spyros N. Pandis, Hanna Vehkamäki, Kari E. J. Lehtinen, and Ilona Riipinen
Geosci. Model Dev., 9, 2741–2754, https://doi.org/10.5194/gmd-9-2741-2016, https://doi.org/10.5194/gmd-9-2741-2016, 2016
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The molecular mechanisms through which new ultrafine (< 100 nm) aerosol particles are formed in the atmosphere have puzzled the scientific community for decades. In the past few years, however, significant progress has been made in unraveling these processes through laboratory studies and computational efforts. In this work we have implemented these new developments to an air quality model and study the implications of anthropogenically driven particle formation for European air quality.
Olli Väisänen, Antti Ruuskanen, Arttu Ylisirniö, Pasi Miettinen, Harri Portin, Liqing Hao, Ari Leskinen, Mika Komppula, Sami Romakkaniemi, Kari E. J. Lehtinen, and Annele Virtanen
Atmos. Chem. Phys., 16, 10385–10398, https://doi.org/10.5194/acp-16-10385-2016, https://doi.org/10.5194/acp-16-10385-2016, 2016
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In-cloud measurements of aerosol hygroscopicity and cloud droplet activation were conducted in Kuopio, Finland. According to the observations, the less hygroscopic accumulation mode particles were present in the non-activated aerosol, whereas the more hygroscopic particles were scavenged into cloud droplets. The results illustrate the sensitivity of cloud droplet formation to varying chemical composition and highlight the need for proper treatment of anthropogenic aerosols in CCN predictions.
Jani Huttunen, Harri Kokkola, Tero Mielonen, Mika Esa Juhani Mononen, Antti Lipponen, Juha Reunanen, Anders Vilhelm Lindfors, Santtu Mikkonen, Kari Erkki Juhani Lehtinen, Natalia Kouremeti, Alkiviadis Bais, Harri Niska, and Antti Arola
Atmos. Chem. Phys., 16, 8181–8191, https://doi.org/10.5194/acp-16-8181-2016, https://doi.org/10.5194/acp-16-8181-2016, 2016
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For a good estimate of the current forcing by anthropogenic aerosols, knowledge in past is needed. One option to lengthen time series is to retrieve aerosol optical depth from solar radiation measurements. We have evaluated several methods for this task. Most of the methods produce aerosol optical depth estimates with a good accuracy. However, machine learning methods seem to be the most applicable not to produce any systematic biases, since they do not need constrain the aerosol properties.
Jenni Kontkanen, Tinja Olenius, Katrianne Lehtipalo, Hanna Vehkamäki, Markku Kulmala, and Kari E. J. Lehtinen
Atmos. Chem. Phys., 16, 5545–5560, https://doi.org/10.5194/acp-16-5545-2016, https://doi.org/10.5194/acp-16-5545-2016, 2016
J. Kim, L. Ahlm, T. Yli-Juuti, M. Lawler, H. Keskinen, J. Tröstl, S. Schobesberger, J. Duplissy, A. Amorim, F. Bianchi, N. M. Donahue, R. C. Flagan, J. Hakala, M. Heinritzi, T. Jokinen, A. Kürten, A. Laaksonen, K. Lehtipalo, P. Miettinen, T. Petäjä, M. P. Rissanen, L. Rondo, K. Sengupta, M. Simon, A. Tomé, C. Williamson, D. Wimmer, P. M. Winkler, S. Ehrhart, P. Ye, J. Kirkby, J. Curtius, U. Baltensperger, M. Kulmala, K. E. J. Lehtinen, J. N. Smith, I. Riipinen, and A. Virtanen
Atmos. Chem. Phys., 16, 293–304, https://doi.org/10.5194/acp-16-293-2016, https://doi.org/10.5194/acp-16-293-2016, 2016
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The hygroscopicity of nucleated nanoparticles was measured in the presence of sulfuric acid, sulfuric acid-dimethylamine, and sulfuric acid-organics derived from α-pinene oxidation during CLOUD7 at CERN in 2012. The hygroscopicity parameter κ decreased with increasing particle size, indicating decreasing acidity of particles.
A. Leskinen, P. Yli-Pirilä, K. Kuuspalo, O. Sippula, P. Jalava, M.-R. Hirvonen, J. Jokiniemi, A. Virtanen, M. Komppula, and K. E. J. Lehtinen
Atmos. Meas. Tech., 8, 2267–2278, https://doi.org/10.5194/amt-8-2267-2015, https://doi.org/10.5194/amt-8-2267-2015, 2015
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A 29 m3 Teflon chamber was characterized and tested with oxidation experiments of toluene. Secondary organic aerosol yields of 12-42 % were obtained. These yields are comparable to those obtained in other laboratories.
E. Giannakaki, A. Pfüller, K. Korhonen, T. Mielonen, L. Laakso, V. Vakkari, H. Baars, R. Engelmann, J. P. Beukes, P. G. Van Zyl, M. Josipovic, P. Tiitta, K. Chiloane, S. Piketh, H. Lihavainen, K. E. J. Lehtinen, and M. Komppula
Atmos. Chem. Phys., 15, 5429–5442, https://doi.org/10.5194/acp-15-5429-2015, https://doi.org/10.5194/acp-15-5429-2015, 2015
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In this study we summarize 1 year of Raman lidar observations over South Africa. The analyses of lidar measurements presented here could assist in bridging existing gaps in the knowledge of vertical distribution of aerosols above South Africa, since limited long-term data of this type are available for this region. For the first time, we have been able to cover the full seasonal cycle on geometrical characteristics and optical properties of free tropospheric aerosol layers in the region.
L. Q. Hao, A. Kortelainen, S. Romakkaniemi, H. Portin, A. Jaatinen, A. Leskinen, M. Komppula, P. Miettinen, D. Sueper, A. Pajunoja, J. N. Smith, K. E. J. Lehtinen, D. R. Worsnop, A. Laaksonen, and A. Virtanen
Atmos. Chem. Phys., 14, 13483–13495, https://doi.org/10.5194/acp-14-13483-2014, https://doi.org/10.5194/acp-14-13483-2014, 2014
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Positive matrix factorization (PMF) was applied to the unified high-resolution mass spectra organic species with NO+ and NO2+ ions from the measurement in a mixed region between the boreal forestland and the urban area. The PMF analysis succeeded in separating the mixed spectra into three distinct organic factors and one inorganic factor. The particulate organic nitrate was distinguished by PMF for the first time, with a contribution of one-third of the total nitrate mass.
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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
Benjamin C. Sapper, Sean Youn, Daven K. Henze, Manjula Canagaratna, Harald Stark, and Jose L. Jimenez
Geosci. Model Dev., 18, 2891–2919, https://doi.org/10.5194/gmd-18-2891-2025, https://doi.org/10.5194/gmd-18-2891-2025, 2025
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Positive matrix factorization (PMF) has been used by atmospheric scientists to extract underlying factors present in large datasets. This paper presents a new technique for error-weighted PMF that drastically reduces the computational costs of previously developed algorithms. We use this technique to deliver interpretable factors and solution diagnostics from an atmospheric chemistry dataset.
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.
Xiong Tang, Wei Liu, Siming He, Lei Zhu, Michel Jaboyedoff, Huanhuan Zhang, Yuqing Sun, and Zenan Huo
EGUsphere, https://doi.org/10.5194/egusphere-2025-707, https://doi.org/10.5194/egusphere-2025-707, 2025
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This manuscript presents a numerical model about an explicit stabilized coupled two phase material point method. The novelty lies in the employment of stabilized techniques including the strain smoothing method and the multi-field variational principleto to mitigate the spurious pore pressure and maintain the numerical stability. The proposed formulation provides an effective and reliable approach for simulating solid-fluid porous media under static and dynamic conditions.
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.
Rozan Rosandi, Yudi Rosandi, and Bernd Simeon
EGUsphere, https://doi.org/10.5194/egusphere-2024-1093, https://doi.org/10.5194/egusphere-2024-1093, 2024
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We model Earth's lithosphere as a thin elastic shell and present numerical methods of isogeometric finite element analysis to simulate its deformation in isostatic equilibrium using technologies from computer-aided design. The simulations also serve as a basis for identifying parameters of the model that are most plausible to explain observed data. This research has been done to showcase the capabilities of isogeometric analysis in solving higher-order problems in geoscientific applications.
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.
Cited articles
Ackleh, A.: Parameter estimation in a structured algal
coagulation-fragmentation model, Nonlinear Anal., 28, 837–854, https://doi.org/10.1016/0362-546X(95)00195-2,
1997. a
Ackleh, A. S. and Miller, R. L.: A model for the interaction of phytoplankton aggregates and the environment: approximation and parameter estimation, Inverse Probl. Sci. En., 26, 152–182, https://doi.org/10.1080/17415977.2017.1310856, 2018. a
Aitken, J.: On the Number of Dust Particles in the Atmosphere, T. Roy. Soc. Edin.-Earth, 35, 1–19, https://doi.org/10.1017/S0080456800017592, 1889. a
Almeida, J., Schobesberger, S., Kürten, A., Ortega, I. K., Kupiainen-Määttä, O., Praplan, A. P., Adamov, A., Amorim, A., Bianchi, F., Breitenlechner, M., David, A., Dommen, J., Donahue, N. M., Downard, A., Dunne, E., Duplissy, J., Ehrhart, S., Flagan, R. C., Franchin, A., Guida, R., Hakala, J., Hansel, A., Heinritzi, M., Henschel, H., Jokinen, T., Junninen, H., Kajos, M., Kangasluoma, J., Keskinen, H., Kupc, A., Kurtén, T., Kvashin,A. N., Laaksonen, A., Lehtipalo, K., Leiminger, M., Leppä,J., Loukonen, V., Makhmutov, V., Mathot, S., McGrath, M. J., Nieminen, T., Olenius, T., Onnela, A., Petäjä, T., Riccobono, F., Riipinen, I., Rissanen, M., Rondo, L., Ruuskanen, T., Santos, F. D., Sarnela, N., Schallhart, S., Schnitzhofer, R., Seinfeld, J. H., Simon, M., Sipilä, M., Stozhkov, Y., Stratmann, F., Tomé, A., Tröstl, J., Tsagkogeorgas, G., Vaattovaara, P., Viisanen, Y., Vir-tanen, A., Vrtala, A., Wagner, P. E., Weingartner, E., Wex, H., Williamson, C., Wimmer, D., Ye, P., Yli-Juuti, T., Carslaw, K. S., Kulmala, M., Curtius, J., Baltensperger, U., Worsnop, D. R.,Vehkamäki, H., and Kirkby, J.: Molecular understanding of sulphuric acid-amine particle nucleation in the atmosphere, Nature, 502, 359–363, https://doi.org/10.1038/nature12663, 2013. a
Banks, H., Tran, H., and Woodward, D.: Estimation of Variable Cefficients in the Fokker–Planck Quations Using Moving Node Finite Elements, SIAM J. Numer. Anal., 30, 1574–1602, https://doi.org/10.1137/0730082, 1993. a
Boisdron, Y. and Brock, J.: On the stochastic nature of the acquisition of electrical charge and radioactivity by aerosol particles, Atmos. Environ., 4, 35–50, 1970. a
Bortz, D., Byrne, E., and Mirzaev, I.: Inverse Problems for a Class of Conditional Probability Measure-Dependent Evolution Equations, arXiv preprint, arXiv:1510.01355, 2015. a
Cho, C.-K. and Kwon, Y.: Parameter Estimation for Age-Structured Population
Dynamics, Journal of the Korean Society for Industrial and Applied
Mathematics, 1, 83–104, available at:
https://www.koreascience.or.kr/article/JAKO199725051942749.pdf (last access: 18 July 2017),
1997. a
Courant, R., Friedrichs, K., and Lewy, H.: Über die partiellen
Differenzengleichungen der mathematischen Physik, Math. Ann., 100, 32–74,
https://doi.org/10.1007/BF01448839, 1928. a
Dada, L., Lehtipalo, K., Kontkanen, J., Nieminen, T., Baalbaki, R., Ahonen, L., Duplissy, J., Yan, C., Chu, B., Petäjä, T., Lehtinen, K., Kerminen, V.-M., Kulmala, M., and Kangasluoma, J.: Formation and growth of sub-3-nm aerosol particles in experimental chambers, Nat. Protoc., 15, 1013–1040, 2020. a, b
Dimitriu, G.: Parameter estimation in size/age structured population models
using the moving finite element method, in: International Conference on
Numerical Methods and Applications, edited by: Dimov, I., Lirkov, I., Margenov, S., and Zlatev, Z., Numerical Methods and Applications, NMA 2002, Lecture Notes in Computer Science, vol. 2542, Springer, Berlin, Heidelberg, 420–429,
https://doi.org/10.1007/3-540-36487-0_47, 2002. a
Dugas, C., Bengio, Y., Bélisle, F., Nadeau, C., and Garcia, R.:
Incorporating second-order functional knowledge for better option pricing, in:
Advances in neural information processing systems, 472–478, available at:
http://papers.nips.cc/paper/1920-incorporating-second-order-functional-knowledge-for-better-option-pricing.pdf (last access: May 2002), 2001. a
Dullemond, C. and Dominik, C.: Dust coagulation in protoplanetary disks: A rapid depletion of small grains, Astron. Astrophys., 434, 971–986, https://doi.org/10.1051/0004-6361:20042080, 2005. a
Flagan, R.: History of electrical aerosol measurements, Aerosol Sci. Tech., 28, 301–380, 1998. a
Friedlander, S. and Wang, C.: The self-preserving particle size distribution for coagulation by Brownian motion, J. Colloid Interf. Sci., 22, 126–132, https://doi.org/10.1016/0021-9797(66)90073-7, 1966. a
Gordon, H., Sengupta, K., Rap, A., Duplissy, J., Frege, C., Williamson, C., Heinritzi, M., Simon, M., Yan, C., Almeida, J., Tröstl, J., Nieminen, T., Ortega, I. K., Wagner, R., Dunne, E. M., Adamov, A., Amorim, A., Bernhammer, A.-K., Bianchi, F., Breitenlechner, M., Brilke, S., Chen, X., Craven, J. S., Dias, A., Ehrhart, S., Fischer, L., Flagan, R. C., Franchin, A., Fuchs, C., Guida, R., Hakala, J., Hoyle, C. R., Jokinen, T., Junninen, H., Kangasluoma, J., Kim, J., Kirkby, J., Krapf, M., Kürten, A., Laaksonen, A., Lehtipalo, K., Makhmutov, V., Mathot, S., Molteni, U., Monks, S. A., Onnela, A., Peräkylä, O., Piel, F., Petäjä, T., Praplan, A. P., Pringle, K. J., Richards, N. A. D., Rissanen, M. P., Rondo, L., Sarnela, N., Schobesberger, S., Scott, C. E., Seinfeld, J. H., Sharma, S., Sipilä, M., Steiner, G., Stozhkov, Y., Stratmann, F., Tomé, A., Virtanen, A., Vogel, A. L., Wagner, A. C., Wagner, P. E., Weingartner, E., Wimmer, D., Winkler, P. M., Ye, P., Zhang, X., Hansel, A., Dommen, J., Donahue, N. M., Worsnop, D. R., Baltensperger, U., Kulmala, M., Curtius, J., and Carslaw, K. S.: Reduced Anthropogenic Aerosol Radiative Forcing Caused by Biogenic New Particle Formation, P. Natl. Acad. Sci. USA, 113, 12053–12058, https://doi.org/10.1073/pnas.1602360113, 2016. a
Hansen, J., Johnson, D., Lacis, A., Lebedeff, S., Lee, P., Rind, D., and Russell, G.: Climate impact of increasing atmospheric carbon dioxide, Science, 213, 957–966, 1981. a
Henze, D., Seinfeld, J., Liao, W., Sandu, A., and Carmichael, G.: Inverse modeling of aerosol dynamics: Condensational growth, J. Geophys. Res.-Atmos., 109, D14201, https://doi.org/10.1029/2004JD004593, 2004. a
Huttunen, J., Kaipio, J., and Haario, H.: Approximation error approach in spatiotemporally chaotic models with application to Kuramoto–Sivashinsky equation, Comput. Stat. Data An., 123, 13–31, https://doi.org/10.1016/j.csda.2018.01.015, 2018. a
Huttunen, J. M. J. and Kaipio, J. P.: Approximation error analysis in nonlinear state estimation with an application to state-space identification, Inverse Probl., 23, 2141–2157, https://doi.org/10.1088/0266-5611/23/5/019, 2007. a
Kaipio, J. and Somersalo, E.: Statistical and computational inverse problems,
Vol. 160, Springer Science and Business Media, Springer, New York, NY, https://doi.org/10.1007/b138659, 2006. a, b, c, d
Kalman, R.: A New Approach to Linear Filtering and Prediction Problems,
J. Basic Eng.-T. ASME, 82, 35–45, https://doi.org/10.1115/1.3662552, 1960. a
Kirkby, J., Duplissy, J., Sengupta, K., Frege, C., Gordon, H. Williamson, C., Heinritzi, M., Simon, M., Yan, C., Almeida, J., Tröstl, J., Nieminen, Ortega, T., Wagner, R., Adamov, A., Amorim, A., Bernhammer, A., Bianchi, F., Breitenlechner, M., Brilke, S., Chen, X., Craven, J., Dias, A., Ehrhart, S., Flagan, R. C., Franchin, A., Fuchs, C., Guida, R., Hakala, J., Hoyle, C. R., Jokinen, T., Junninen, H. Kangasluoma, J., Kim, J., Krapf, M. Kürten, A., Laaksonen, A., Lehtipalo, K., Makhmutov, V., Mathot, S., Molteni, U., Onnela, A., Peräkylä, O., Piel, F., Petäjä, T., Praplan, A. P., Pringle, K., Rap, A., Richards, N., Riipinen, I., Rissanen, M. P., Rondo, L., Sarnela, N., Schobesberger, S., Scott, C., Seinfeld, J. H., Sipilä, M., Steiner, G., Stozhkov, Y., Stratmann, F., Tomé, A., Virtanen, A., Vogel, A., Wagner, A., Wagner, P., Weingartner, E., Wimmer, D., Winkler, P., Ye, P., Zhang, X., Hansel, A., Dommen, J., Donahue, N. M., Worsnop, D., Baltensperger, U., Kulmala, M., Carslaw, K. S., and Curtius, J.: Ion-Induced Nucleation of Pure Biogenic Particles, Nature, 476, 429–433, https://doi.org/0.1038/nature17953, 2016.
a
Korhonen, H., Lehtinen, K. E. J., and Kulmala, M.: Multicomponent aerosol dynamics model UHMA: model development and validation, Atmos. Chem. Phys., 4, 757–771, https://doi.org/10.5194/acp-4-757-2004, 2004. a
Kuang, C., Chen, M., Zhao, J., Smith, J., McMurry, P. H., and Wang, J.: Size and time-resolved growth rate measurements of 1 to 5 nm freshly formed atmospheric nuclei, Atmos. Chem. Phys., 12, 3573–3589, https://doi.org/10.5194/acp-12-3573-2012, 2012. a
Kürten, A., Li, C., Bianchi, F., Curtius, J., Dias, A., Donahue, N. M.,
Duplissy, J., Flagan, R. C., Hakala, J., Jokinen, T., Kirkby, J., Kulmala, M.,
Laaksonen, A., Lehtipalo, K., Makhmutov, V., Onnela, A., Rissanen, M. P.,
Simon, M., Sipilä, M., Stozhkov, Y., Tröstl, J., Ye, P., and McMurry, P. H.:
New particle formation in the sulfuric aciddimethylaminewater system:
reevaluation of CLOUD chamber measurements and comparison to an aerosol
nucleation and growth model, Atmos. Chem. Phys., 18, 845–863,
https://doi.org/10.5194/acp-18-845-2018, 2018. a
Kulmala, M., Vehkamäki, H., Petäjä, T., Maso, M. D., Lauri, A., Kerminen, V.-M., Birmili, W., and McMurry, P.: Formation and growth rates of ultrafine atmospheric particles: a review of observations, J. Aerosol Sci., 35, 143–176, https://doi.org/10.1016/j.jaerosci.2003.10.003, 2004. a
Kulmala, M., Petäjä, T., Nieminen, T., Sipilä, M., Manninen, H., Lehtipalo, K., Maso, M. D., Aalto, P., Junninen, H., Paasonen, P., Riipinen, I., Lehtinen, K., Laaksonen, A., and Kerminen, V.-M.: Measurement of the nucleation of atmospheric aerosol particles, Nat. Protoc., 7, 1651, https://doi.org/10.1038/nprot.2012.091, 2012. a, b
Kupiainen-Määttä, O.: A Monte Carlo approach for determining cluster evaporation rates from concentration measurements, Atmos. Chem. Phys., 16, 14585–14598, https://doi.org/10.5194/acp-16-14585-2016, 2016. a
Lehtinen, K. E. J. and Kulmala, M.: A model for particle formation and growth in the atmosphere with molecular resolution in size, Atmos. Chem. Phys., 3, 251–257, https://doi.org/10.5194/acp-3-251-2003, 2003. a
Lehtinen, K. and Zachariah, M.: Self-preserving theory for the volume distribution of particles undergoing Brownian coagulation, J. Colloid Interf. Sci., 242, 314–318, https://doi.org/10.1006/jcis.2001.7791, 2001. a, b
Lehtinen, K., Rannik, Ü., Petäjä, T., Kulmala, M., and Hari, P.:
Nucleation rate and vapor concentration estimations using a least squares
aerosol dynamics method, J. Geophys. Res.-Atmos., 109, 521–526, https://doi.org/10.1038/nature17953, 2004. a
Lehtinen, K. E., Dal Maso, M., Kulmala, M., and Kerminen, V.-M.: Estimating nucleation rates from apparent particle formation rates and vice versa: Revised formulation of the Kerminen–Kulmala equation, J. Aerosol Sci., 38, 988–994, https://doi.org/10.1016/j.jaerosci.2007.06.009, 2007. a
Lehtipalo, K., Leppä, J., Kontkanen, J., Kangasluoma, J., Franchin, A.,
Wimmer, D., Schobesberger, S., Junninen, H., Petäjä, T.,
Sipilä, M., Mikkilä, J., Vanhanen, J., Worsnop, D. R., and Kulmala, M.: Methods for
determining particle size distribution and growth rates between 1 and 3 nm
using the Particle Size Magnifier, Boreal Environ. Res., 19, 215–236,
http://hdl.handle.net/10138/228762 (last access: 15 June 2021), 2014.
a, b
Leppä, J., Anttila, T., Kerminen, V.-M., Kulmala, M., and Lehtinen, K. E. J.:
Atmospheric new particle formation: real and apparent growth of neutral and
charged particles, Atmos. Chem. Phys., 11, 4939–4955,
https://doi.org/10.5194/acp-11-4939-2011, 2011. a
Maso, M. D., Kulmala, M., Riipinen, I., Wagner, R., Hussein, T., Aalto, P.,
and Lehtinen, K.: Formation and growth of fresh atmospheric aerosols: eight
years of aerosol size distribution data from SMEAR II, Hyytiala, Finland,
Boreal Environ. Res., 10, 323–336, available at: http://www.borenv.net/BER/archive/pdfs/ber10/ber10-323.pdf (last access: 15 June 2021),
2005. a, b
McMurry, P.: The history of condensation nucleus counters, Aerosol Sci. Tech., 33, 297–322, 2000. a
Merikanto, J., Spracklen, D. V., Pringle, K. J., and Carslaw, K. S.: Effects of boundary layer particle formation on cloud droplet number and changes in cloud albedo from 1850 to 2000, Atmos. Chem. Phys., 10, 695–705, https://doi.org/10.5194/acp-10-695-2010, 2010. a
Millikan, R.: The general law of fall of a small spherical body through a gas, and its bearing upon the nature of molecular reflection from surfaces, Phys. Rev., 22, 1–23, https://doi.org/10.1103/PhysRev.22.1, 1923. a
Ozon, M.: BAYROSOL (Version 1.0), Zenodo, https://doi.org/10.5281/zenodo.4061728, 2020. a
Pachauri, R. K., Allen, M. R., Barros, V. R., Broome, J., Cramer, W., Christ, R., Church, J. A., Clarke, L., Dahe, Q., Dasgupta, P., Dubash, N. K., Edenhofer, O., Elgizouli, I., Field, C. B., Forster, P., Friedlingstein, P., Fuglestvedt, J., Gomez-Echeverri, L., Hallegatte, S., Hegerl, G., Howden, M., Jiang, K., Jimenez Cisneroz, B., Kattsov, V., Lee, H., Mach, K. J., Marotzke, J., Mastrandrea, M. D., Meyer, L., Minx, J., Mulugetta, Y., O'Brien, K., Oppenheimer, M., Pereira, J. J., Pichs-Madruga, R., Plattner, G.-K., Pörtner, Hans-Otto , Power, S. B., Preston, B., Ravindranath, N. H., Reisinger, A., Riahi, K., Rusticucci, M., Scholes, R., Seyboth, K., Sokona, Y., Stavins, R., Stocker, T. F., Tschakert, P., van Vuuren, D., and van Ypserle, J.-P.: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pachauri, R. and Meyer, L., Geneva, Switzerland, IPCC, 151 pp., ISBN: 978-92-9169-143-2, https://doi.org/hdl:10013/epic.45156,
2014. a
Prakash, A., Bapat, A., and Zachariah, M.: A simple numerical algorithm and
software for solution of nucleation, surface growth, and coagulation problems,
Aerosol Sci. Tech., 37, 892–898,
https://doi.org/10.1080/02786820300933, 2003. a
Ramachandran, R. and Barton, P.: Effective parameter estimation within a multi-dimensional population balance model framework, Chem. Eng. Sci., 65, 4884–4893, https://doi.org/10.1016/j.ces.2010.05.039, 2010. a
Ramanathan, V., Crutzen, P., Kiehl, J., and Rosenfeld, D.: Aerosols, climate, and the hydrological cycle, Science, 294, 2119–2124, 2001. a
Rundell, W.: Determining the death rate for an age-structured population from census data, SIAM J. Appl. Math., 53, 1731–1746, https://doi.org/10.1137/0153080, 1993. a
Sandu, A., Liao, W., Carmichael, G., Henze, D., and Seinfeld, J.: Inverse modeling of aerosol dynamics using adjoints: Theoretical and numerical considerations, Aerosol Sci. Tech., 39, 677–694, https://doi.org/10.1080/02786820500182289, 2005. a
Shcherbacheva, A., Balehowsky, T., Kubečka, J., Olenius, T., Helin, T., Haario, H., Laine, M., Kurtén, T., and Vehkamäki, H.: Identification of molecular cluster evaporation rates, cluster formation enthalpies and entropies by Monte Carlo method, Atmos. Chem. Phys., 20, 15867–15906, https://doi.org/10.5194/acp-20-15867-2020, 2020. a
Smoluchowski, M. V.: Drei vortrage uber diffusion. Brownsche Bewegung und Koagulation von Kolloidteilchen, Z. Phys., 17, 557–585, 1916. a
Stocker, T F., Qin, D., Plattner, G.-K., Tignor, M. M. B., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P. M., Alexander, L. V., Allen, S. K., Bindoff, N. L., Breon, F.-M., Church, J. A., Cubasch, U., Emori, S., Forster, P., Friedlingstein, P., Gillett, N., Gregory, J. M., Hartmann, D. L., Jansen, E., Kirtman, B., Knutti, R., Kumar Kanikicharla, K., Lemke, P., Marotzke, J., Masson-Delmotte, V., Meehl, G. A., Mokhov, I. I., Piao, S., Plattner, G.-K., Dahe, Q., Ramaswamy, V., Randall, D., Rhein, M., Rojas, M., Sabine, C., Shindell, D., Talley, L. D., Vaughan, D. G., Xie, S.-P., Allen, M. R., Boucher, O., Chambers, D., Hesselbjerg Christensen, J., Ciais, P., Clark, P. U., Collins, M., Comiso, J. C., Vasconcellos de Menezes, V., Feely, R. A., Fichefet, T., Fiore, A. M., Flato, G., Fuglestvedt, J., Hegerl, G., Hezel, P. J., Johnson, G. C., Kaser, G., Kattsov, V., Kennedy, J., Klein Tank, A. M. G., Le Quere, C., Myhre, G., Osborn, T., Payne, A. J., Perlwitz, J., Power, S., Prather, M., Rintoul, S. R., Rogelj, J., Rusticucci, M., Schulz, M., Sedlacek, J., Stott, P. A., Sutton, R., Thorne, P. W., and Wuebbles, D.: Climate Change 2013. The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change – Abstract for decision-makers; Changements climatiques 2013. a
Stolzenburg, M.: An ultrafine aerosol size distribution measuring system, University of Minnesota, Ann Arbor, Michigan, USA, 1989. a
Trangenstein, J. A.: Numerical solution of elliptic and parabolic partial differential equations with CD-ROM, Cambridge University Press, Cambridge, 2013. a
Verheggen, B. and Mozurkewich, M.: An inverse modeling procedure to determine particle growth and nucleation rates from measured aerosol size distributions, Atmos. Chem. Phys., 6, 2927–2942, https://doi.org/10.5194/acp-6-2927-2006, 2006. a
Viskari, T., Asmi, E., Kolmonen, P., Vuollekoski, H., Petäjä, T., and Järvinen, H.: Estimation of aerosol particle number distributions with Kalman Filtering Part 1: Theory, general aspects and statistical validity, Atmos. Chem. Phys., 12, 11767–11779, https://doi.org/10.5194/acp-12-11767-2012, 2012. a
Voutilainen, A. and Kaipio, J.: Estimation of time-varying aerosol size distributions – exploitation of modal aerosol dynamical models, J. Aerosol Sci., 33, 1181–1200, 2002. a
Voutilainen, A., Stratmann, F., and Kaipio, J.: A non-homogeneous regularization method for the estimation of narrow aerosol size distributions, J. Aerosol Sci., 31, 1433–1445, 2000. a
Voutilainen, A., Kolehmainen, V., and Kaipio, J.: Statistical inversion of aerosol size measurement data, Inverse Probl. Eng., 9, 67–94, https://doi.org/10.1080/174159701088027753, 2001. a
Wiedensohler, A.: An approximation of the bipolar charge distribution for particles in the submicron size range, J. Aerosol Sci., 19, 387–389, 1988. a
Zhang, Y., Seigneur, C., Seinfeld, J. H., Jacobson, M. Z., and Binkowski, F. S.: Simulation of aerosol dynamics: A comparative review of algorithms used in air quality models, Aerosol Sci. Tech., 31, 487–514, https://doi.org/10.1080/027868299304039, 1999. a
Short summary
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).
Experimental research has provided large amounts of high-quality data on aerosol over the last 2...
