Articles | Volume 10, issue 12
https://doi.org/10.5194/gmd-10-4511-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-4511-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
| 
08 Dec 2017
Methods for assessment of models |
| 08 Dec 2017
Sensitivity analysis of a coupled hydrodynamic-vegetation model using the effectively subsampled quadratures method (ESQM v5.2)
Integrated Statistics, contracted to the US Geological Survey, Woods Hole, MA, USA
Alfredo Aretxabaleta
US Geological Survey, Woods Hole, MA, USA
Pranay Seshadri
University of Cambridge, Cambridge, UK
Neil K. Ganju
US Geological Survey, Woods Hole, MA, USA
Alexis Beudin
University of Bordeaux, Bordeaux, France
Related authors
Tarandeep S. Kalra, Neil K. Ganju, and Jeremy M. Testa
Geosci. Model Dev., 13, 5211–5228, https://doi.org/10.5194/gmd-13-5211-2020, https://doi.org/10.5194/gmd-13-5211-2020, 2020
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The paper covers the description of a 3-D open-source model that dynamically couples the biophysical interactions between submerged aquatic vegetation (SAV), hydrodynamics (currents, waves), sediment dynamics, and nutrient loading. Based on SAV growth model, SAV can use growth or dieback while contributing and sequestering nutrients from the water column (modifying the biological environment) and subsequently affect the hydrodynamics and sediment transport (modifying the physical environment).
Tarandeep S. Kalra, Neil K. Ganju, and Jeremy M. Testa
Geosci. Model Dev., 13, 5211–5228, https://doi.org/10.5194/gmd-13-5211-2020, https://doi.org/10.5194/gmd-13-5211-2020, 2020
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The paper covers the description of a 3-D open-source model that dynamically couples the biophysical interactions between submerged aquatic vegetation (SAV), hydrodynamics (currents, waves), sediment dynamics, and nutrient loading. Based on SAV growth model, SAV can use growth or dieback while contributing and sequestering nutrients from the water column (modifying the biological environment) and subsequently affect the hydrodynamics and sediment transport (modifying the physical environment).
Neil K. Ganju, Jeremy M. Testa, Steven E. Suttles, and Alfredo L. Aretxabaleta
Ocean Sci., 16, 593–614, https://doi.org/10.5194/os-16-593-2020, https://doi.org/10.5194/os-16-593-2020, 2020
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Seagrasses, as plants, need light for photosynthesis and production. This study measured the changes in productivity and light in a back-barrier estuary and connected those changes with the type of seabed within the estuary. We found that the locations with seagrass on the seabed had more light getting through the water and higher productivity because of the way seagrass keeps sediment on the seabed during wave events. When sediment stays on the bed, it cannot reduce the light in the water.
Alfredo L. Aretxabaleta, Neil K. Ganju, Zafer Defne, and Richard P. Signell
Nat. Hazards Earth Syst. Sci., 19, 1823–1838, https://doi.org/10.5194/nhess-19-1823-2019, https://doi.org/10.5194/nhess-19-1823-2019, 2019
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Water levels in bays are affected by open-ocean changes and wind. Tides are more dampened in the bays than storm surges and sea level rise. We compare observed and modeled levels with ocean conditions and combine them with analytical models. We consider the local setup, caused by wind along the bay. Expansion using the ADCIRC tidal database will allow coverage of other bay systems on the United States East Coast. Spatial estimates of water level can inform decisions about bay flooding hazards.
Neil K. Ganju, Jeremy M. Testa, Steven E. Suttles, and Alfredo L. Aretxabaleta
Biogeosciences Discuss., https://doi.org/10.5194/bg-2018-335, https://doi.org/10.5194/bg-2018-335, 2018
Revised manuscript not accepted
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Estuaries are productive ecosystems that provide habitat for flora and fauna. We measured changes in light and oxygen, along with variables such as tides and waves, to understand how productivity in the estuary changed over daily and seasonal time periods. We found large differences in productivity between channels and seagrass beds, as well as a link between light climate and productivity. This study will help us understand how estuaries will respond to future changes in conditions.
Christopher R. Sherwood, Alfredo L. Aretxabaleta, Courtney K. Harris, J. Paul Rinehimer, Romaric Verney, and Bénédicte Ferré
Geosci. Model Dev., 11, 1849–1871, https://doi.org/10.5194/gmd-11-1849-2018, https://doi.org/10.5194/gmd-11-1849-2018, 2018
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Cohesive sediment (mud) is ubiquitous in the world's coastal regions, but its behavior is complicated and often oversimplified by computer models. This paper describes extensions to a widely used open-source coastal ocean model that allow users to simulate important components of cohesive sediment transport.
W. K. Oestreich, N. K. Ganju, J. W. Pohlman, and S. E. Suttles
Biogeosciences, 13, 583–595, https://doi.org/10.5194/bg-13-583-2016, https://doi.org/10.5194/bg-13-583-2016, 2016
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Colored dissolved organic matter (CDOM) is a factor in determining penetration of light in estuaries. Important plant species growing in the beds of estuaries depend on such light penetration for survival. Previous studies have used CDOM fluorescence to approximate light absorption by CDOM but have found variable relationships between fluorescence and absorbance. This paper describes this variability in three east coast estuaries, and shows that this conversion is dependent on CDOM source.
M. Grifoll, A. L. Aretxabaleta, J. L. Pelegrí, and M. Espino
Ocean Sci., 12, 137–151, https://doi.org/10.5194/os-12-137-2016, https://doi.org/10.5194/os-12-137-2016, 2016
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We investigate the rapidly changing equilibrium between the momentum sources and sinks during the passage of a single two-peak storm over the Catalan inner shelf (NW Mediterranean Sea). At 24m water depth, a primary momentum balance between acceleration, pressure gradient and frictional forces (surface and bottom) is established. The frictional adjustment timescale was around 10h, consistent with the e-folding time obtained from bottom drag parameterizations.
A. L. Aretxabaleta, K. W. Smith, and J. Ballabrera-Poy
Ocean Sci. Discuss., https://doi.org/10.5194/osd-12-983-2015, https://doi.org/10.5194/osd-12-983-2015, 2015
Revised manuscript has not been submitted
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We estimate global surface salinity means and trends using historical (1950-2014) monthly fields and recent SMOS satellite data. We separate the regimes by fitting a Gaussian Mixture Model with a non-subjective method. There are three separate regimes: A (1950-1990) with small trends; B (1990-2009) with enhanced trends; and C (2009-2014) with significantly larger trends. The trend acceleration could be related to an enhanced hydrological cycle or to changes in sampling methodology.
N. K. Ganju, J. L. Miselis, and A. L. Aretxabaleta
Biogeosciences, 11, 7193–7205, https://doi.org/10.5194/bg-11-7193-2014, https://doi.org/10.5194/bg-11-7193-2014, 2014
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Light availability to seagrass is an important factor in their success. We deployed instrumentation to measure light in Barnegat Bay, New Jersey, and found lower availability in the southern bay due to high turbidity (suspended sediment), while the northern bay has higher availability. In the northern bay, dissolved organic material and chlorophyll are most responsible for blocking light to the seagrass canopy. We also found that boat wakes do not have a large effect on sediment resuspension.
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James Kent, Thomas Melvin, and Golo Albert Wimmer
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Till Sachau, Haibin Yang, Justin Lang, Paul D. Bons, and Louis Moresi
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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.
Keith J. Roberts, Alexandre Olender, Lucas Franceschini, Robert C. Kirby, Rafael S. Gioria, and Bruno S. Carmo
Geosci. Model Dev., 15, 8639–8667, https://doi.org/10.5194/gmd-15-8639-2022, https://doi.org/10.5194/gmd-15-8639-2022, 2022
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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
Geosci. Model Dev., 15, 7903–7912, https://doi.org/10.5194/gmd-15-7903-2022, https://doi.org/10.5194/gmd-15-7903-2022, 2022
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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.
Artur Safin, Damien Bouffard, Firat Ozdemir, Cintia L. Ramón, James Runnalls, Fotis Georgatos, Camille Minaudo, and Jonas Šukys
Geosci. Model Dev., 15, 7715–7730, https://doi.org/10.5194/gmd-15-7715-2022, https://doi.org/10.5194/gmd-15-7715-2022, 2022
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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.
Colin Grudzien and Marc Bocquet
Geosci. Model Dev., 15, 7641–7681, https://doi.org/10.5194/gmd-15-7641-2022, https://doi.org/10.5194/gmd-15-7641-2022, 2022
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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.
Yangyang Yu, Shaoqing Zhang, Haohuan Fu, Lixin Wu, Dexun Chen, Yang Gao, Zhiqiang Wei, Dongning Jia, and Xiaopei Lin
Geosci. Model Dev., 15, 6695–6708, https://doi.org/10.5194/gmd-15-6695-2022, https://doi.org/10.5194/gmd-15-6695-2022, 2022
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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.
Vijay S. Mahadevan, Jorge E. Guerra, Xiangmin Jiao, Paul Kuberry, Yipeng Li, Paul Ullrich, David Marsico, Robert Jacob, Pavel Bochev, and Philip Jones
Geosci. Model Dev., 15, 6601–6635, https://doi.org/10.5194/gmd-15-6601-2022, https://doi.org/10.5194/gmd-15-6601-2022, 2022
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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.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
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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.
Andrew M. Bradley, Peter A. Bosler, and Oksana Guba
Geosci. Model Dev., 15, 6285–6310, https://doi.org/10.5194/gmd-15-6285-2022, https://doi.org/10.5194/gmd-15-6285-2022, 2022
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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.
Léo Pujol, Pierre-André Garambois, and Jérôme Monnier
Geosci. Model Dev., 15, 6085–6113, https://doi.org/10.5194/gmd-15-6085-2022, https://doi.org/10.5194/gmd-15-6085-2022, 2022
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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.
Ludovic Räss, Ivan Utkin, Thibault Duretz, Samuel Omlin, and Yuri Y. Podladchikov
Geosci. Model Dev., 15, 5757–5786, https://doi.org/10.5194/gmd-15-5757-2022, https://doi.org/10.5194/gmd-15-5757-2022, 2022
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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.
Bruno K. Zürcher
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-116, https://doi.org/10.5194/gmd-2022-116, 2022
Revised manuscript accepted for GMD
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We present a novel algorithm to efficiently compute Barnes interpolation, which is a method for transforming data values recorded at irregularly spaced points into a corresponding regular grid. In contrast to naive implementations that have an algorithmic complexity that depends directly on both the number of sample points and the grid size, our approach reduces this dependency essentially to the number of grid points.
Meriem Krouma, Pascal Yiou, Céline Déandreis, and Soulivanh Thao
Geosci. Model Dev., 15, 4941–4958, https://doi.org/10.5194/gmd-15-4941-2022, https://doi.org/10.5194/gmd-15-4941-2022, 2022
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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.
Piotr Dziekan and Piotr Zmijewski
Geosci. Model Dev., 15, 4489–4501, https://doi.org/10.5194/gmd-15-4489-2022, https://doi.org/10.5194/gmd-15-4489-2022, 2022
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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.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 15, 4147–4161, https://doi.org/10.5194/gmd-15-4147-2022, https://doi.org/10.5194/gmd-15-4147-2022, 2022
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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.
Michael A. Olesik, Jakub Banaśkiewicz, Piotr Bartman, Manuel Baumgartner, Simon Unterstrasser, and Sylwester Arabas
Geosci. Model Dev., 15, 3879–3899, https://doi.org/10.5194/gmd-15-3879-2022, https://doi.org/10.5194/gmd-15-3879-2022, 2022
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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.
Navjot Kukreja, Jan Hückelheim, Mathias Louboutin, John Washbourne, Paul H. J. Kelly, and Gerard J. Gorman
Geosci. Model Dev., 15, 3815–3829, https://doi.org/10.5194/gmd-15-3815-2022, https://doi.org/10.5194/gmd-15-3815-2022, 2022
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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.
Richard Scalzo, Mark Lindsay, Mark Jessell, Guillaume Pirot, Jeremie Giraud, Edward Cripps, and Sally Cripps
Geosci. Model Dev., 15, 3641–3662, https://doi.org/10.5194/gmd-15-3641-2022, https://doi.org/10.5194/gmd-15-3641-2022, 2022
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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.
Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, and Rao Kotamarthi
Geosci. Model Dev., 15, 3433–3445, https://doi.org/10.5194/gmd-15-3433-2022, https://doi.org/10.5194/gmd-15-3433-2022, 2022
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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.
Hiromasa Yoshimura
Geosci. Model Dev., 15, 2561–2597, https://doi.org/10.5194/gmd-15-2561-2022, https://doi.org/10.5194/gmd-15-2561-2022, 2022
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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.
Mirko Mälicke
Geosci. Model Dev., 15, 2505–2532, https://doi.org/10.5194/gmd-15-2505-2022, https://doi.org/10.5194/gmd-15-2505-2022, 2022
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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.
Christopher J. L. D'Amboise, Michael Neuhauser, Michaela Teich, Andreas Huber, Andreas Kofler, Frank Perzl, Reinhard Fromm, Karl Kleemayr, and Jan-Thomas Fischer
Geosci. Model Dev., 15, 2423–2439, https://doi.org/10.5194/gmd-15-2423-2022, https://doi.org/10.5194/gmd-15-2423-2022, 2022
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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.
Evan Baker, Anna B. Harper, Daniel Williamson, and Peter Challenor
Geosci. Model Dev., 15, 1913–1929, https://doi.org/10.5194/gmd-15-1913-2022, https://doi.org/10.5194/gmd-15-1913-2022, 2022
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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.
Daichun Wang, Wei You, Zengliang Zang, Xiaobin Pan, Yiwen Hu, and Yanfei Liang
Geosci. Model Dev., 15, 1821–1840, https://doi.org/10.5194/gmd-15-1821-2022, https://doi.org/10.5194/gmd-15-1821-2022, 2022
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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.
Kevin Bulthuis and Eric Larour
Geosci. Model Dev., 15, 1195–1217, https://doi.org/10.5194/gmd-15-1195-2022, https://doi.org/10.5194/gmd-15-1195-2022, 2022
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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.
Urmas Raudsepp and Ilja Maljutenko
Geosci. Model Dev., 15, 535–551, https://doi.org/10.5194/gmd-15-535-2022, https://doi.org/10.5194/gmd-15-535-2022, 2022
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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.
Emmanuel Wyser, Yury Alkhimenkov, Michel Jaboyedoff, and Yury Y. Podladchikov
Geosci. Model Dev., 14, 7749–7774, https://doi.org/10.5194/gmd-14-7749-2021, https://doi.org/10.5194/gmd-14-7749-2021, 2021
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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.
Rafael Lago, Thomas Gastine, Tilman Dannert, Markus Rampp, and Johannes Wicht
Geosci. Model Dev., 14, 7477–7495, https://doi.org/10.5194/gmd-14-7477-2021, https://doi.org/10.5194/gmd-14-7477-2021, 2021
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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.
Moritz Lange, Henri Suominen, Mona Kurppa, Leena Järvi, Emilia Oikarinen, Rafael Savvides, and Kai Puolamäki
Geosci. Model Dev., 14, 7411–7424, https://doi.org/10.5194/gmd-14-7411-2021, https://doi.org/10.5194/gmd-14-7411-2021, 2021
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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.
Hynek Bednář, Aleš Raidl, and Jiří Mikšovský
Geosci. Model Dev., 14, 7377–7389, https://doi.org/10.5194/gmd-14-7377-2021, https://doi.org/10.5194/gmd-14-7377-2021, 2021
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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.
Denise Degen, Cameron Spooner, Magdalena Scheck-Wenderoth, and Mauro Cacace
Geosci. Model Dev., 14, 7133–7153, https://doi.org/10.5194/gmd-14-7133-2021, https://doi.org/10.5194/gmd-14-7133-2021, 2021
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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.
Geoffroy Kirstetter, Olivier Delestre, Pierre-Yves Lagrée, Stéphane Popinet, and Christophe Josserand
Geosci. Model Dev., 14, 7117–7132, https://doi.org/10.5194/gmd-14-7117-2021, https://doi.org/10.5194/gmd-14-7117-2021, 2021
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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.
Sojung Park and Seon K. Park
Geosci. Model Dev., 14, 6241–6255, https://doi.org/10.5194/gmd-14-6241-2021, https://doi.org/10.5194/gmd-14-6241-2021, 2021
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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.
Olivier Pannekoucke and Philippe Arbogast
Geosci. Model Dev., 14, 5957–5976, https://doi.org/10.5194/gmd-14-5957-2021, https://doi.org/10.5194/gmd-14-5957-2021, 2021
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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.
Annika Günther, Johannes Gütschow, and Mairi Louise Jeffery
Geosci. Model Dev., 14, 5695–5730, https://doi.org/10.5194/gmd-14-5695-2021, https://doi.org/10.5194/gmd-14-5695-2021, 2021
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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.
Futo Tomizawa and Yohei Sawada
Geosci. Model Dev., 14, 5623–5635, https://doi.org/10.5194/gmd-14-5623-2021, https://doi.org/10.5194/gmd-14-5623-2021, 2021
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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.
Chloe Leach, Tom Coulthard, Andrew Barkwith, Daniel R. Parsons, and Susan Manson
Geosci. Model Dev., 14, 5507–5523, https://doi.org/10.5194/gmd-14-5507-2021, https://doi.org/10.5194/gmd-14-5507-2021, 2021
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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.
Steven J. Phipps, Jason L. Roberts, and Matt A. King
Geosci. Model Dev., 14, 5107–5124, https://doi.org/10.5194/gmd-14-5107-2021, https://doi.org/10.5194/gmd-14-5107-2021, 2021
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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.
Axel Peytavin, Bruno Sainte-Rose, Gael Forget, and Jean-Michel Campin
Geosci. Model Dev., 14, 4769–4780, https://doi.org/10.5194/gmd-14-4769-2021, https://doi.org/10.5194/gmd-14-4769-2021, 2021
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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.
Kang Pan, Mei Qi Lim, Markus Kraft, and Epaminondas Mastorakos
Geosci. Model Dev., 14, 4509–4534, https://doi.org/10.5194/gmd-14-4509-2021, https://doi.org/10.5194/gmd-14-4509-2021, 2021
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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.
Sebastian Springer, Heikki Haario, Jouni Susiluoto, Aleksandr Bibov, Andrew Davis, and Youssef Marzouk
Geosci. Model Dev., 14, 4319–4333, https://doi.org/10.5194/gmd-14-4319-2021, https://doi.org/10.5194/gmd-14-4319-2021, 2021
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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.
Cited articles
Aretxabaleta, A. L. and Smith, K. W.: Analyzing state-dependent model-data comparison in multi-regime systems, Computat. Geosci., 15, 627–636, 2011.
Bacchi, V., Gagnaire, E., Durand, N., and Benoit, M.: Wave energy dissipation in TOMAWAC, Telemac & Mascaret User Club, 15–17 October 2014, Grenoble, France, 2014.
Bastidas, L. A., Gupta, H. V., Sorooshian, S., Shuttleworth, W. J., and Yang, Z. L.: Sensitivity analysis of land surface scheme using multicriteria methods, J. Geophys. Res., 104, 481–490, 1999.
Beudin, A., Kalra, T. S., Ganju, N. K., and Warner, J. C.: Development of a Coupled Wave-Current-Vegetation Interaction, Comput. Geosci., 100, 76–86, 2017.
Booij, N., Ris, R. C., and Holthuijsen, L. H.: A third-generation wave model for coastal regions: 1. Model description and validation, J. Geophys. Res.-Oceans, 104, 7649–7666, 1999.
Bryan, F.: Parameter Sensitivity of Primitive Equation Ocean General circulation models, J. Phys. Oceanogr., 17, 970–985, https://doi.org/10.1175/1520-0485(1987)017<0970:PSOPEO>2.0.CO;2, 1987.
Carr, J., D'Odorico P., McGlathery K., and Wiberg, P.: Stability and bistability of seagrass ecosystems in shallow coastal lagoons: role of feedbacks with sediment resuspension and light attenuation, J. Geophys. Res.-Biogeo., 115, G03011, 2010.
Chen, S. N., Sanford L., Koch, E. W., Shi F., and North, E. W.: A nearshore model to investigate the effects of seagrass bed geometry on wave attenuation and suspended sediment transport, Estuar. Coast., 30, 296–310, 2007.
Costanza, R., d'Arge, R., de Groot, R., Farberk, S., Grasso, M., Hannon, B., Limburg, K., Naeem, S., O'Neill, R. V., Paruelo, J., and Raskin, R. G.: The value of the world's ecosystem services and natural capital, Nature, 387, 253–260, 1997.
Dalrymple, R. A., Kirby, J. T., and Hwang, P. A.: Wave diffraction due to areas of energy dissipation, J. Waterw. Port C., 110, 67–79, 1984.
de Vriend, H. J.: Integrated Research Results on Hydrobiosedimentary Processes in Estuaries, Final Report of the Estuary Process Research Project (EstProc): Algorithms and Scientific Information, R&D Technical Report FD1905/TR3, Defra – Flood Management Division Ergon House, London, UK, 2006.
Fennel, K., Losch, M., Schroter, J., and Wenzel, M.: Testing a marine ecosystem model: sensitivity analysis and parameter optimization, J. Marine Syst., 28, 45–63, https://doi.org/10.1016/S0924-7963(00)00083-X, 2001.
Ganju, N. K., Sherwood, C. R.: Effect of roughness formulation on the performance of a coupled wave, hydrodynamic, and sediment transport model, Ocean Model., 33, 299–313, 2010.
Geraci, G., Congedo, P. M., Abgrall, R., and Iaccarino, G.: High-order statistics in global sensitivity analysis: Decomposition and model reduction. Comput. Method. Appl. M., 301, 80–115, 2016.
Haidvogel, D. B., Arango, H. G., Budgell, W. P., Cornuelle, B. D., Curchitser, E., Di Lorenzo, E., Fennel, K., Geyer, W. R., Hermann, A. J., Lanerolle, L., Levin, J., McWilliams, J. C., Miller, A. J.,. Moore, A. M, Powell, T. M., Shchepetkin, A. F., Sherwood, C. R., Signell, R. P., Warner, J. C., and Wilkin, J.: Ocean forecasting in terrain-following coordinates: formulation and assessment of the regional ocean modeling system, J. Comput. Phys., 227, 3595–3624, 2008.
Heck, K. L., Hays, G., and Orth, R. J.: Critical evaluation of the nursery role hypothesis for seagrass meadows, Mar. Ecol. Prog. Ser., 253, 123–136, 2003.
Kennish, M. J., Fertig, M. B., and Sakowicz, G. P.: In situ Surveys of Seagrass Habitat in the Northern Segment of the Barnegat Bay-Little Egg Harbor Estuary, Barnegat Bay Partnership, Final Report, Toms River, NJ, USA, 2013.
Kombiadou, K., Ganthy, F., Verney, R., Plus, M., and Sottolichio, A.: Modelling the effects of Zostera noltei meadows on sediment dynamics: application to the Arcachon lagoon, Ocean Dynam., 64, 1499–1516, 2014.
Lapetina, A. and Sheng, Y. P.: Three-dimensional modeling of storm surge and inundation including the effects of coastal vegetation, Estuar. Coast., 37, 1028–1040, 2014.
Larkum, A. W. D., Orth, R. J., and Duarte, C. M. (Eds.): Seagrasses: Biology, Ecology and Conservation, Springer Netherlands, 2007.
Le Bouteiller, C. and Venditti, J. G.: Sediment transport and shear stress partitioning in a vegetated flow, Water Resour. Res., 51, 2901–2922, https://doi.org/10.1002/2014WR015825, 2015.
Luhar, M., Coutu, S., Infantes, E., Fox, S., and Nepf, H. M.: Wave-induced velocities inside a model seagrass bed, J. Geophys. Res.-Oceans, 115, C12005, 2010.
Madsen, O. S.: Spectral wave–current bottom boundary layer flows, Proceedings of the 24th International Conference on Coastal Engineering Research Council, Kobe, Japan, 384–398, 1994.
Mendez, F. M. and Losada, I. J.: An empirical model to estimate the propagation of random breaking and nonbreaking waves over vegetation fields, Coast. Eng., 51, 103–118, 2004.
Möller, I., Spencer, T., French, J. R., Legget, D. J., and Dixon, M.: Wave transformation over salt marshes: a field and numerical modeling study from Norfolk, England. Estuarine, Coastal and Shelf Science, 49, 411–426, 1999.
Morin, J., Leclerc, M., Secretan, Y., and Boudreau, P.: Integrated two-dimensional macrophytes-hydrodynamic modelling, J. Hydraul. Res., 38, 163–172, 2000.
Mourre, B., Ballabrera-Poy, J., García-Ladona, E., Font, J.: Surface salinity response to changes in the model parameters and forcings in a climatological simulation of the eastern North-Atlantic Ocean, Ocean Model., 23, 37–49, 2008.
Ree, W. O.: Hydraulic characteristics of vegetation for vegetated waterways, Agr. Eng., 30, 184–189, 1949.
Rodi, W.: Turbulence models and their application in hydraulics, a state of the art review, Technical report, International Association of Hydraulics Research, Delft, 1984.
Rosero, E., Yang, Z. L., Wagener, T., Gulden, L. E., Yatheendradas, S., and Niu, G. Y.: Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface model over transition zones during the warm season, J. Geophys. Res., 115, D03106, https://doi.org/10.1029/2009JD012035, 2010.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.: Global sensitivity analysis: the primer, John Wiley & Sons, Chichester, 312 pp., 2008.
Schwarz, G.: Estimating the dimension of a model, Ann. Stat., 6, 461–464, 1978.
Seshadri, P.: Effective-Quadratures/Effective-Quadratures: Suite of tools for polynomial approximations, GitHub, available at: https://github.com/Effective-Quadratures/Effective-Quadratures, last access: 20 November 2017.
Seshadri, P. and Parks, G. T.: Effective Quadratures (EQ): Polynomials for Computational Engineering Studies, Journal of Open Source Software, 2, https://doi.org/10.21105/joss.00166, 2017.
Seshadri, P., Narayan, A., and Mahadevan, S.: Effectively Subsampled Quadratures for Least Squares Polynomial Approximations, SIAM/ASA, Journal of Uncertainty Quantification, 5, 1003–1023, https://doi.org/10.1137/16M1057668, 2017.
Smith, R. C.: Uncertainty quantification: theory, implementation, and applications, SIAM, Computational Science and Engineering Series, CS12, 2014.
Sobol', I. M.: Sensitivity estimates for nonlinear mathematical models, Mathematical Modelling and Computational Experiments, 1.4, 407–414, 1993.
Soulsby, R. L.: Dynamics of Marine Sands, Thomas Telford, London, 249 pp., 1997.
Sudret, B.: Global sensitivity analyzing using polynomial chaos expansions, Reliability Engineering and System Safety, 93, 964–979, 2008.
Suzuki, T., Zijlema, M., Burger, B., Meijer, M. C., and Narayan, S.: Wave dissipation by vegetation with layer schematization in SWAN, Coast. Eng., 59, 64–71, 2012.
Temmerman, S., Bouma, T. J., Govers, G., Wang, Z. B., De Vries, M. B., and Herman, P. M. J.: Impact of vegetation on flow routing and sedimentation patterns: three-dimensional modeling for a tidal marsh, J. Geophys. Res.-Earth, 110, F04019, https://doi.org/10.1029/2005JF000301, 2005.
Uittenbogaard, R.: Modelling turbulence in vegetated aquatic flows, International workshop on Riparian Forest vegetated channels: hydraulic, morphological and ecological aspects, Trento, Italy, 2003.
USGS/WHCMSC: Model output of sensitivity analysis for the COAWST vegetation model, United States Geological Survey, Woods Hole Coastal and Marine Science Center (WHCMSC) Sediment Transport Group, MA, USA, available at: http://geoport.whoi.edu/thredds/catalog/clay/usgs/users/tkalra/senstivity_study/catalog.html, last access: 1 December 2017.
Wamsley, T. V., Cialone, M. A., Smith, J. M., Atkinson, J. H. and Rosati, J. D.: The potential of wetlands in reducing storm surge, Ocean Eng., 37, 59–68, 2010.
Warner, J. C.: COAWST: Coupled-Ocean-Atmosphere-Wave-Sediment Transport Model, United States Geological Survey (USGS), Woods Hole Coastal and Marine Science Center (WHCMSC), Sediment Transport Group, MA, USA, available at: https://coawstmodel-trac.sourcerepo.com/coawstmodel_COAWST, last access: 1 December 2017.
Warner, J. C., Perlin, N., and Skyllingstad, E. D.: Using the Model Coupling Toolkit to couple earth system models, Environ. Modell. Softw., 23, 1240–1249, 2008a.
Warner J. C., Sherwood, C. R., Signell, R. P., Harris, C. K., and Arango, H.G.: Development of a three-dimensional, regional, coupled wave, current, and sediment-transport model, Comput. Geosci., 34, 1284–1306, 2008b.
Warner, J. C., Armstrong, B., He, R., and Zambon, J. B.: Development of a Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modeling System, Ocean Model., 35, 230–244, https://doi.org/10.1016/j.ocemod.2010.07.010, 2010.
Wu, W.: A 3-D phase-averaged model for shallow-water flow with waves in vegetated water, Ocean Dynam., 64, 1061–1071, 2014.
Xiu, D. and Karniadakis, G. E.: The Wiener–Askey polynomial chaos for stochastic differential equations, SIAM J. Sci. Comput., 24, 614–644, 2002.
Short summary
The paper details the sensitivity of vegetation properties that are input to a 3-D submerged aquatic vegetation model within a coupled hydrodynamics and wave model. It describes a novel strategy to perform sensitivity analysis efficiently by using a combination of the Effective Quadratures method and Sobol' indices. This method reduces the number of simulations to understand the sensitivity patterns and also quantifies the amount of sensitivity.
The paper details the sensitivity of vegetation properties that are input to a 3-D submerged...
