Articles | Volume 15, issue 20
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1
Eawag: Swiss Federal Institute of Aquatic Science and Technology, Systems Analysis, Integrated Assessment and Modelling, Dübendorf, Switzerland
Eawag: Swiss Federal Institute of Aquatic Science and Technology, Surface Waters – Research and Management, Kastanienbaum, Switzerland
Swiss Data Science Center, ETH Zurich, Zurich, Switzerland
Cintia L. Ramón
Water Research Institute and Department of Civil Engineering, University of Granada, Granada, Spain
Eawag: Swiss Federal Institute of Aquatic Science and Technology, Surface Waters – Research and Management, Kastanienbaum, Switzerland
Swiss Data Science Center, ETH Zurich, Zurich, Switzerland
Swiss Data Science Center, EPFL, Lausanne, Switzerland
EPFL, Swiss Federal Institute of Technology, Physics of Aquatic Systems Laboratory, Margaretha Kamprad Chair, Lausanne, Switzerland
Eawag: Swiss Federal Institute of Aquatic Science and Technology, Systems Analysis, Integrated Assessment and Modelling, Dübendorf, Switzerland
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Olivia Desgué-Itier, Laura Melo Vieira Soares, Orlane Anneville, Damien Bouffard, Vincent Chanudet, Pierre Alain Danis, Isabelle Domaizon, Jean Guillard, Théo Mazure, Najwa Sharaf, Frédéric Soulignac, Viet Tran-Khac, Brigitte Vinçon-Leite, and Jean-Philippe Jenny
Hydrol. Earth Syst. Sci., 27, 837–859,Short summary
The long-term effects of climate change will include an increase in lake surface and deep water temperatures. Incorporating up to 6 decades of limnological monitoring into an improved 1D lake model approach allows us to predict the thermal regime and oxygen solubility in four peri-alpine lakes over the period 1850–2100. Our modeling approach includes a revised selection of forcing variables and provides a way to investigate the impacts of climate variations on lakes for centennial timescales.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623,Short summary
Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Tomy Doda, Cintia L. Ramón, Hugo N. Ulloa, Alfred Wüest, and Damien Bouffard
Hydrol. Earth Syst. Sci., 26, 331–353,Short summary
At night or during cold periods, the shallow littoral region of lakes cools faster than their deeper interior. This induces a cold downslope current that carries littoral waters offshore. From a 1-year-long database collected in a small temperate lake, we resolve the seasonality of this current and report its frequent occurrence from summer to winter. This study contributes to a better quantification of lateral exchange in lakes, with implications for the transport of dissolved compounds.
Danlu Guo, Camille Minaudo, Anna Lintern, Ulrike Bende-Michl, Shuci Liu, Kefeng Zhang, and Clément Duvert
Hydrol. Earth Syst. Sci., 26, 1–16,Short summary
We investigate the impact of baseflow contribution on concentration–flow (C–Q) relationships across the Australian continent. We developed a novel Bayesian hierarchical model for six water quality variables across 157 catchments that span five climate zones. For sediments and nutrients, the C–Q slope is generally steeper for catchments with a higher median and a greater variability of baseflow contribution, highlighting the key role of variable flow pathways in particulate and solute export.
Marco Toffolon, Luca Cortese, and Damien Bouffard
Geosci. Model Dev., 14, 7527–7543,Short summary
The time when lakes freeze varies considerably from year to year. A common way to predict it is to use negative degree days, i.e., the sum of air temperatures below 0 °C, a proxy for the heat lost to the atmosphere. Here, we show that this is insufficient as the mixing of the surface layer induced by wind tends to delay the formation of ice. To do so, we developed a minimal model based on a simplified energy balance, which can be used both for large-scale analyses and short-term predictions.
Pascal Perolo, Bieito Fernández Castro, Nicolas Escoffier, Thibault Lambert, Damien Bouffard, and Marie-Elodie Perga
Earth Syst. Dynam., 12, 1169–1189,Short summary
Wind blowing over the ocean creates waves that, by increasing the level of turbulence, promote gas exchange at the air–water interface. In this study, for the first time, we measured enhanced gas exchanges by wind-induced waves at the surface of a large lake. We adapted an ocean-based model to account for the effect of surface waves on gas exchange in lakes. We finally show that intense wind events with surface waves contribute disproportionately to the annual CO2 gas flux in a large lake.
Stella Guillemot, Ophelie Fovet, Chantal Gascuel-Odoux, Gérard Gruau, Antoine Casquin, Florence Curie, Camille Minaudo, Laurent Strohmenger, and Florentina Moatar
Hydrol. Earth Syst. Sci., 25, 2491–2511,Short summary
This study investigates the drivers of spatial variations in stream water quality in poorly studied headwater catchments and includes multiple elements involved in major water quality issues, such as eutrophication. We used a regional public dataset of monthly stream water concentrations monitored for 10 years over 185 agricultural catchments. We found a spatial and seasonal opposition between carbon and nitrogen concentrations, while phosphorus concentrations showed another spatial pattern.
Cintia L. Ramón, Hugo N. Ulloa, Tomy Doda, Kraig B. Winters, and Damien Bouffard
Hydrol. Earth Syst. Sci., 25, 1813–1825,Short summary
When solar radiation penetrates the frozen surface of lakes, shallower zones underneath warm faster than deep interior waters. This numerical study shows that the transport of excess heat to the lake interior depends on the lake circulation, affected by Earth's rotation, and controls the lake warming rates and the spatial distribution of the heat flux across the ice–water interface. This work contributes to the understanding of the circulation and thermal structure patterns of ice-covered lakes.
Theo Baracchini, Philip Y. Chu, Jonas Šukys, Gian Lieberherr, Stefan Wunderle, Alfred Wüest, and Damien Bouffard
Geosci. Model Dev., 13, 1267–1284,Short summary
Lake physical processes occur at a wide range of spatiotemporal scales. 3D hydrodynamic lake models are the only information source capable of solving those scales; however, they still need observations to be calibrated and to constrain their uncertainties. The optimal combination of a 3D hydrodynamic model, in situ measurements, and remote sensing observations is achieved through data assimilation. Here we present a complete data assimilation experiment for lakes using open-source tools.
Adrien Gaudard, Love Råman Vinnå, Fabian Bärenbold, Martin Schmid, and Damien Bouffard
Geosci. Model Dev., 12, 3955–3974,Short summary
We have developed an openly accessible web-based platform for visualization and promotion of easy access to one-dimensional hydrodynamic lake model output data updated in near-real time (simstrat.eawag.ch). This platform was developed for 54 lakes in Switzerland, with potential for adaptation to other regional areas or even at a global worldwide scale using appropriate forcing input data.
Camille Minaudo, Florence Curie, Yann Jullian, Nathalie Gassama, and Florentina Moatar
Biogeosciences, 15, 2251–2269,Short summary
We developed the model QUALity-NETwork (QUAL-NET) to simulate water quality variations in large drainage networks. This model is accurate enough to represent processes occurring over short periods of time such as storm events and helps to fully understand water quality variations in stream networks in the context of climate change and varying human pressures. It was tested on the Loire River and provided good performances and a new understanding of the functioning of the river.
Love Råman Vinnå, Alfred Wüest, Massimiliano Zappa, Gabriel Fink, and Damien Bouffard
Hydrol. Earth Syst. Sci., 22, 31–51,Short summary
Responses of inland waters to climate change vary on global and regional scales. Shifts in river discharge regimes act as positive and negative feedbacks in influencing water temperature. The extent of this effect on warming is controlled by the change in river discharge and lake hydraulic residence time. A shift of deep penetrating river intrusions from summer towards winter can potentially counteract the otherwise negative climate effects on deep-water oxygen content.
Adrien Gaudard, Robert Schwefel, Love Råman Vinnå, Martin Schmid, Alfred Wüest, and Damien Bouffard
Geosci. Model Dev., 10, 3411–3423,Short summary
The study of lakes often uses numerical models to reproduce the processes occurring in nature as accurately as possible. Due to the complexity of natural systems, all numerical models need to leave aside or simplify many of the relevant processes. In this work, we improve the modelling of the impact of wind on the internal currents in deep lakes. This improves the reproduction of deep mixing, which influences the concentrations of oxygen and nutrients, with biological and chemical consequences.
Damien Bouffard and Marie-Elodie Perga
Biogeosciences, 13, 3573–3584,Short summary
This survey of an exceptional flood over Lake Geneva challenges the long-standing hypothesis that dense, particle-loaded and oxygenated rivers plunging into lakes necessarily contribute to deep-oxygen replenishment. We identified some river intrusions as hot spots for oxygen consumption, where inputs of fresh river-borne organic matter reactivate the respiration of more refractory lacustrine organic matter in a process referred to as "priming effect".
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David H. Marsico and Paul A. Ullrich
Geosci. Model Dev., 16, 1537–1551,Short summary
Climate models involve several different components, such as the atmosphere, ocean, and land models. Information needs to be exchanged, or remapped, between these models, and devising algorithms for performing this exchange is important for ensuring the accuracy of climate simulations. In this paper, we examine the efficacy of several traditional and novel approaches to remapping on the sphere and demonstrate where our approaches offer improvement.
Moritz Liebl, Jörg Robl, Stefan Hergarten, David Lundbek Egholm, and Kurt Stüwe
Geosci. Model Dev., 16, 1315–1343,Short summary
In this study, we benchmark a topography-based model for glacier erosion (OpenLEM) with a well-established process-based model (iSOSIA). Our experiments show that large-scale erosion patterns and particularly the transformation of valley length geometry from fluvial to glacial conditions are very similar in both models. This finding enables the application of OpenLEM to study the influence of climate and tectonics on glaciated mountains with reasonable computational effort on standard PCs.
James Kent, Thomas Melvin, and Golo Albert Wimmer
Geosci. Model Dev., 16, 1265–1276,Short summary
This paper introduces the Met Office's new shallow water model. The shallow water model is a building block towards the Met Office's new atmospheric dynamical core. The shallow water model is tested on a number of standard spherical shallow water test cases, including flow over mountains and unstable jets. Results show that the model produces similar results to other shallow water models in the literature.
Anthony Gruber, Max Gunzburger, Lili Ju, Rihui Lan, and Zhu Wang
Geosci. Model Dev., 16, 1213–1229,Short summary
This work applies a novel technical tool, multifidelity Monte Carlo (MFMC) estimation, to three climate-related benchmark experiments involving oceanic, atmospheric, and glacial modeling. By considering useful quantities such as maximum sea height and total (kinetic) energy, we show that MFMC leads to predictions which are more accurate and less costly than those obtained by standard methods. This suggests MFMC as a potential drop-in replacement for estimation in realistic climate models.
Piyoosh Jaysaval, Glenn E. Hammond, and Timothy C. Johnson
Geosci. Model Dev., 16, 961–976,Short summary
We present a robust and highly scalable implementation of numerical forward modeling and inversion algorithms for geophysical electrical resistivity tomography data. The implementation is publicly available and developed within the framework of PFLOTRAN (http://www.pflotran.org), an open-source, state-of-the-art massively parallel subsurface flow and transport simulation code. The paper details all the theoretical and implementation aspects of the new capabilities along with test examples.
Lucas Schauer, Michael J. Schmidt, Nicholas B. Engdahl, Stephen D. Pankavich, David A. Benson, and Diogo Bolster
Geosci. Model Dev., 16, 833–849,Short summary
We develop a multi-dimensional, parallelized domain decomposition strategy for mass-transfer particle tracking methods in two and three dimensions, investigate different procedures for decomposing the domain, and prescribe an optimal tiling based on physical problem parameters and the number of available CPU cores. For an optimally subdivided diffusion problem, the parallelized algorithm achieves nearly perfect linear speedup in comparison with the serial run-up to thousands of cores.
John Mern and Jef Caers
Geosci. Model Dev., 16, 289–313,Short summary
In this work, we formulate the sequential geoscientific data acquisition problem as a problem that is similar to playing chess against nature, except the pieces are not fully observed. Solutions to these problems are given in AI and rarely used in geoscientific data planning. We illustrate our approach to a simple 2D problem of mineral exploration.
Ziqi Gao, Yifeng Wang, Petros Vasilakos, Cesunica E. Ivey, Khanh Do, and Armistead G. Russell
Geosci. Model Dev., 15, 9015–9029,Short summary
While the national ambient air quality standard of ozone is based on the 3-year average of the fourth highest 8 h maximum (MDA8) ozone concentrations, these predicted extreme values using numerical methods are always biased low. We built four computational models (GAM, MARS, random forest and SVR) to predict the fourth highest MDA8 ozone in Southern California using precursor emissions, meteorology and climatological patterns. All models presented acceptable performance, with GAM being the best.
Zhihao Wang, Jason Goetz, and Alexander Brenning
Geosci. Model Dev., 15, 8765–8784,Short summary
A lack of inventory data can be a limiting factor in developing landslide predictive models, which are crucial for supporting hazard policy and decision-making. We show how case-based reasoning and domain adaptation (transfer-learning techniques) can effectively retrieve similar landslide modeling situations for prediction in new data-scarce areas. Using cases in Italy, Austria, and Ecuador, our findings support the application of transfer learning for areas that require rapid model development.
Till Sachau, Haibin Yang, Justin Lang, Paul D. Bons, and Louis Moresi
Geosci. Model Dev., 15, 8749–8764,Short summary
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,Short summary
Finite-element methods (FEMs) permit the use of more flexible unstructured meshes but are rarely used in full waveform inversions (FWIs), an iterative process that reconstructs velocity models of earth’s subsurface, due to computational and memory storage costs. To reduce those costs, novel software is presented allowing the use of high-order mass-lumped FEMs on triangular meshes, together with a material-property mesh-adaptation performance-enhancing strategy, enabling its use in FWIs.
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,Short summary
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.
Colin Grudzien and Marc Bocquet
Geosci. Model Dev., 15, 7641–7681,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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.,
Revised manuscript accepted for GMDShort summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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.
Geosci. Model Dev., 15, 2561–2597,Short summary
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.
Geosci. Model Dev., 15, 2505–2532,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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.
Alexander Schaaf, Miguel de la Varga, Florian Wellmann, and Clare E. Bond
Geosci. Model Dev., 14, 3899–3913,Short summary
Uncertainty is an inherent property of any model of the subsurface. We show how geological topology information – how different regions of rocks in the subsurface are connected – can be used to train uncertain geological models to reduce uncertainty. More widely, the method demonstrates the use of probabilistic machine learning (Bayesian inference) to train structural geological models on auxiliary geological knowledge that can be encoded in graph structures.
Adcroft, A., Hill, C., and Marshall, J.: Representation of Topography by Shaved Cells in a Height Coordinate Ocean Model, Mon. Weather Rev., 125, 2293–2315, https://doi.org/10.1175/1520-0493(1997)125<2293:ROTBSC>2.0.CO;2, 1997. a, b
Alappattu, D. P., Wang, Q., Yamaguchi, R., Lind, R. J., Reynolds, M., and Christman, A. J.: Warm layer and cool skin corrections for bulk water temperature measurements for air-sea interaction studies, J. Geophys. Res.-Oceans, 122, 6470–6481, https://doi.org/10.1002/2017JC012688, 2017. a, b
Anderson, E. J., Fujisaki-Manome, A., Kessler, J., Lang, G. A., Chu, P. Y., Kelley, J. G., Chen, Y., and Wang, J.: Ice Forecasting in the Next-Generation Great Lakes Operational Forecast System (GLOFS), J. Marine Sci. Eng., 6, 123, https://doi.org/10.3390/jmse6040123, 2018. a, b
Andrieu, C., Doucet, A., and Holenstein, R.: Particle Markov chain Monte Carlo methods, J. Roy. Stat. Soc. B, 72, 269–342, https://doi.org/10.1111/j.1467-9868.2009.00736.x, 2010. a, b
Baracchini, T.: From observations to 3D forecasts: Data assimilation for high resolution lakes monitoring, PhD thesis, EPFL, Lausanne, https://doi.org/10.5075/epfl-thesis-9475, 2019. a, b
Baracchini, T., Chu, P. Y., Šukys, J., Lieberherr, G., Wunderle, S., Wüest, A., and Bouffard, D.: Data assimilation of in situ and satellite remote sensing data to 3D hydrodynamic lake models: a case study using Delft3D-FLOW v4.03 and OpenDA v2.4, Geosci. Model Dev., 13, 1267–1284, https://doi.org/10.5194/gmd-13-1267-2020, 2020a. a, b, c
Bouffard, D. and Lemmin, U.: Kelvin waves in Lake Geneva, J. Great Lakes Res., 39, 637–645, https://doi.org/10.1016/j.jglr.2013.09.005, 2013. a, b
Bouffard, D. and Wüest, A.: Convection in Lakes, Annu. Rev. Fluid Mech., 51, 189–215, https://doi.org/10.1146/annurev-fluid-010518-040506, 2019. a, b
Bouffard, D., Kiefer, I., Wüest, A., Wunderle, S., and Odermatt, D.: Are surface temperature and chlorophyll in a large deep lake related? An analysis based on satellite observations in synergy with hydrodynamic modelling and in-situ data, Remote Sens. Environ., 209, 510–523, https://doi.org/10.1016/j.rse.2018.02.056, 2018. a, b
Campin, J.-M., Heimbach, P., Losch, M., Forget, G., edhill3, Adcroft, A., amolod, Menemenlis, D., dfer22, Hill, C., Jahn, O., Scott, J., stephdut, Mazloff, M., Fox-Kemper, B., antnguyen13, Doddridge, E., Fenty, I., safinenko, Bates, M., Eichmann, A., Smith, T., mitllheisey, Martin, T., Lauderdale, J., Abernathey, R., samarkhatiwala, hongandyan, Deremble, B., and dngoldberg: safinenko/MITgcm: Datalakes-MITgcm (8d98ed1), Zenodo [code], https://doi.org/10.5281/zenodo.5634042, 2021. a
Chen, C., Beardsley, R. C., and Cowles, G.: An Unstructured Grid, Finite-Volume Coastal Ocean Model (FVCOM) System, Oceanography, 19, 78–89, https://doi.org/10.5670/oceanog.2006.92, 2006. a
Chu, P. Y., Kelley, J. G. W., Mott, G. V., Zhang, A., and Lang, G. A.: Development, implementation, and skill assessment of the NOAA/NOS Great Lakes Operational Forecast System, Ocean Dynam., 61, 1305–1316, https://doi.org/10.1007/s10236-011-0424-5, 2011. a
Cimatoribus, A. A., Lemmin, U., Bouffard, D., and Barry, D. A.: Nonlinear Dynamics of the Nearshore Boundary Layer of a Large Lake (Lake Geneva), J. Geophys. Res.-Oceans, 123, 1016–1031, https://doi.org/10.1002/2017JC013531, 2018. a, b
Cogley, J. G.: The Albedo of Water as a Function of Latitude, Mon. Weather Rev., 107, 775–781, https://doi.org/10.1175/1520-0493(1979)107<0775:TAOWAA>2.0.CO;2, 1979. a
Deltares: Delft3D-FLOW user manual, vol. 330, https://content.oss.deltares.nl/delft3d/manuals/Delft3D-FLOW_User_Manual.pdf (last access: 1 May 2021), 2013. a
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res.-Oceans, 99, 10143–10162, https://doi.org/10.1029/94JC00572, 1994. a
Fink, G., Schmid, M., and Wüest, A.: Large lakes as sources and sinks of anthropogenic heat: Capacities and limits, Water Resour. Res., 50, 7285–7301, https://doi.org/10.1002/2014WR015509, 2014. a
Gaudard, A., Råman Vinnå, L., Bärenbold, F., Schmid, M., and Bouffard, D.: Toward an open access to high-frequency lake modeling and statistics data for scientists and practitioners – the case of Swiss lakes using Simstrat v2.1, Geosci. Model Dev., 12, 3955–3974, https://doi.org/10.5194/gmd-12-3955-2019, 2019. a
Goodman, J. and Weare, J.: Ensemble samplers with affine invariance, Commun. Appl. Math. Comput. Sci., 5, 65–80, https://doi.org/10.2140/camcos.2010.5.65, 2010. a, b
Griffies, S. M. and Hallberg, R. W.: Biharmonic Friction with a Smagorinsky-Like Viscosity for Use in Large-Scale Eddy-Permitting Ocean Models, Mon. Weather Rev., 128, 2935–2946, https://doi.org/10.1175/1520-0493(2000)128<2935:BFWASL>2.0.CO;2, 2000. a
Gustafsson, N., Janjić, T., Schraff, C., Leuenberger, D., Weissmann, M., Reich, H., Brousseau, P., Montmerle, T., Wattrelot, E., Bučánek, A., Mile, M., Hamdi, R., Lindskog, M., Barkmeijer, J., Dahlbom, M., Macpherson, B., Ballard, S., Inverarity, G., Carley, J., Alexander, C., Dowell, D., Liu, S., Ikuta, Y., and Fujita, T.: Survey of data assimilation methods for convective-scale numerical weather prediction at operational centres, Q. J. Roy. Meteor. Soc., 144, 1218–1256, https://doi.org/10.1002/qj.3179, 2018. a, b
Kendall, A. and Gal, Y.: What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?, in: Advances in Neural Information Processing Systems, edited by: Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., vol. 30, Curran Associates, Inc., https://proceedings.neurips.cc/paper/2017/file/2650d6089a6d640c5e85b2b88265dc2b-Paper.pdf (last access: 30 July 2021), 2017. a, b
Kilpatrick, K. A., Podestá, G. P., and Evans, R.: Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database, J. Geophys. Res.-Oceans, 106, 9179–9197, https://doi.org/10.1029/1999JC000065, 2001. a
Lahoz, W. A. and Schneider, P.: Data assimilation: making sense of Earth Observation, Front. Environ. Sci., 2, 16, https://doi.org/10.3389/fenvs.2014.00016, 2014. a, b, c, d
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization, Rev. Geophys., 32, 363–403, https://doi.org/10.1029/94RG01872, 1994. a
Lieberherr, G. and Wunderle, S.: Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European Lakes, Remote Sens., 10, https://doi.org/10.3390/rs10070990, 2018. a, b
McDougall, T. J., Jackett, D. R., Wright, D. G., and Feistel, R.: Accurate and Computationally Efficient Algorithms for Potential Temperature and Density of Seawater, J. Atmos. Ocean. Tech., 20, 730–741, https://doi.org/10.1175/1520-0426(2003)20<730:AACEAF>2.0.CO;2, 2003. a
Prather, M. J.: Numerical advection by conservation of second-order moments, J. Geophys. Res.-Atmos., 91, 6671–6681, https://doi.org/10.1029/JD091iD06p06671, 1986. a
Rahaghi, A. I., Lemmin, U., Cimatoribus, A., Bouffard, D., Riffler, M., Wunderle, S., and Barry, D. A.: Improving surface heat flux estimation for a large lake through model optimization and two-point calibration: The case of Lake Geneva, Limnol. Oceanogr.-Methods, 16, 576–593, https://doi.org/10.1002/lom3.10267, 2018. a
Riffler, M., Lieberherr, G., and Wunderle, S.: Lake surface water temperatures of European Alpine lakes (1989–2013) based on the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set, Earth Syst. Sci. Data, 7, 1–17, https://doi.org/10.5194/essd-7-1-2015, 2015. a
Safin, A.: Datalakes-Hydrodynamics. In Geoscientific Model Development (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.5637216, 2021. a
Safin, A., Runnalls, J., Šukys, J., and Bouffard, D.: Datalakes Observational model predictions (2019), Zenodo [data set], https://doi.org/10.5281/zenodo.5642898, 2021. a
Safin, A., Šukys, J., and Bacci, M.: SPUX-MITgcm (SPUX-MITgcm_v1), Zenodo [code], https://doi.org/10.5281/zenodo.5638313, 2021. a
Soulignac, F., Danis, P.-A., Bouffard, D., Chanudet, V., Dambrine, E., Guénand, Y., Harmel, T., Ibelings, B. W., Trevisan, D., Uittenbogaard, R., and Anneville, O.: Using 3D modeling and remote sensing capabilities for a better understanding of spatio-temporal heterogeneities of phytoplankton abundance in large lakes, J. Great Lakes Res., 44, 756–764, https://doi.org/10.1016/j.jglr.2018.05.008, 2018. a
Soulignac, F., Anneville, O., Bouffard, D., Chanudet, V., Dambrine, E., Guénand, Y., Harmel, T., Ibelings, B. W., Trevisan, D., Uittenbogaard, R., and Danis, P.: Contribution of 3D coupled hydrodynamic-ecological modeling to assess the representativeness of a sampling protocol for lake water quality assessment, Knowl. Manag. Aquat. Ecosyst., 420, 42, https://doi.org/10.1051/kmae/2019034, 2019. a, b
Stalder, M., Ozdemir, F., Safin, A., Šukys, J., Bouffard, D., and Perez-Cruz, F.: Probabilistic modeling of lake surface water temperature using a Bayesian spatio-temporal graph convolutional neural network, arXiv, https://doi.org/10.48550/ARXIV.2109.13235, 2021. a
Swiss Data Science Center: RENKU, https://renkulab.io/, last access: 30 August 2021. a
van Leeuwen, P. J., Künsch, H. R., Nerger, L., Potthast, R., and Reich, S.: Particle filters for high-dimensional geoscience applications: A review, Q. J. Roy. Meteor. Soc., 145, 2335–2365, https://doi.org/10.1002/qj.3551, 2019. a
Verburg, P. and Antenucci, J. P.: Persistent unstable atmospheric boundary layer enhances sensible and latent heat loss in a tropical great lake: Lake Tanganyika, J. Geophys. Res.-Atmos., 115, D11, https://doi.org/10.1029/2009JD012839, 2010. a
Vinnå, L. R., Medhaug, I., Schmid, M., and Bouffard, D.: The vulnerability of lakes to climate change along an altitudinal gradient, Commun. Earth Environ., 2, 35, https://doi.org/10.1038/s43247-021-00106-w, 2021. a
Wick, G. A., Emery, W. J., Kantha, L. H., and Schlüssel, P.: The Behavior of the Bulk – Skin Sea Surface Temperature Difference under Varying Wind Speed and Heat Flux, J. Phys. Oceanogr., 26, 1969–1988, https://doi.org/10.1175/1520-0485(1996)026<1969:TBOTBS>2.0.CO;2, 1996. a
Wilson, R. C., Hook, S. J., Schneider, P., and Schladow, S. G.: Skin and bulk temperature difference at Lake Tahoe: A case study on lake skin effect, J. Geophys. Res.-Atmos., 118, 10332–10346, https://doi.org/10.1002/jgrd.50786, 2013. a
Wüest, A. and Lorke, A.: SMALL-SCALE HYDRODYNAMICS IN LAKES, Annu. Rev. Fluid Mech., 35, 373–412, https://doi.org/10.1146/annurev.fluid.35.101101.161220, 2003. a
Wüest, A., Bouffard, D., Guillard, J., Ibelings, B. W., Lavanchy, S., Perga, M.-E., and Pasche, N.: LéXPLORE: A floating laboratory on Lake Geneva offering unique lake research opportunities, WIREs Water, 8, e1544, https://doi.org/10.1002/wat2.1544, 2021. a
Ye, X., Chu, P. Y., Anderson, E. J., Huang, C., Lang, G. A., and Xue, P.: Improved thermal structure simulation and optimized sampling strategy for Lake Erie using a data assimilative model, J. Great Lakes Res., 46, 144–158, https://doi.org/10.1016/j.jglr.2019.10.018, 2020. a, b
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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.
Reconciling the differences between numerical model predictions and observational data is always...