Articles | Volume 14, issue 9
https://doi.org/10.5194/gmd-14-5623-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-5623-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
13 Sep 2021
Development and technical paper |
| 13 Sep 2021
| 13 Sep 2021
Combining ensemble Kalman filter and reservoir computing to predict spatiotemporal chaotic systems from imperfect observations and models
Futo Tomizawa
CORRESPONDING AUTHOR
School of Engineering, the University of Tokyo, Tokyo, Japan
Yohei Sawada
School of Engineering, the University of Tokyo, Tokyo, Japan
Meteorological Research Institute, Japan Meteorological Agency,
Tsukuba, Japan
RIKEN Center for Computational Science, Kobe, Japan
Related authors
No articles found.
Sneha Kulkarni, Yohei Sawada, Yared Bayissa, and Brian Wardlow
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-245, https://doi.org/10.5194/hess-2024-245, 2024
Preprint under review for HESS
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Understanding how drought impacts communities is complex and not yet fully understood. We examined a disaster dataset and compared various drought measures to pinpoint affected regions. Our new combined drought indicator (CDI) was found to be the most effective in identifying more drought events than other traditional drought indices. This underscores the CDI's importance in evaluating drought risks and directing attention to the most impacted areas.
Yohei Sawada
EGUsphere, https://doi.org/https://doi.org/10.48550/arXiv.2403.06371, https://doi.org/https://doi.org/10.48550/arXiv.2403.06371, 2024
Preprint archived
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It is generally difficult to control large-scale and complex systems, such as Earth systems, using small forces. In this paper, a new method to control such systems is proposed. The new method is inspired by the similarity between simulation-observation integration methods in geoscience and model predictive control theory in control engineering. The proposed method is particularly suitable to find the efficient strategies of weather modification.
Le Duc and Yohei Sawada
Hydrol. Earth Syst. Sci., 27, 1827–1839, https://doi.org/10.5194/hess-27-1827-2023, https://doi.org/10.5194/hess-27-1827-2023, 2023
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The Nash–Sutcliffe efficiency (NSE) is a widely used score in hydrology, but it is not common in the other environmental sciences. One of the reasons for its unpopularity is that its scientific meaning is somehow unclear in the literature. This study attempts to establish a solid foundation for NSE from the viewpoint of signal progressing. This approach is shown to yield profound explanations to many open problems related to NSE. A generalized NSE that can be used in general cases is proposed.
Yuya Kageyama and Yohei Sawada
Hydrol. Earth Syst. Sci., 26, 4707–4720, https://doi.org/10.5194/hess-26-4707-2022, https://doi.org/10.5194/hess-26-4707-2022, 2022
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This study explores the link between hydrometeorological droughts and their socioeconomic impact at a subnational scale based on the newly developed disaster dataset with subnational location information. Hydrometeorological drought-prone areas were generally consistent with socioeconomic drought-prone areas in the disaster dataset. Our analysis clarifies the importance of the use of subnational disaster information.
Yohei Sawada, Rin Kanai, and Hitomu Kotani
Hydrol. Earth Syst. Sci., 26, 4265–4278, https://doi.org/10.5194/hess-26-4265-2022, https://doi.org/10.5194/hess-26-4265-2022, 2022
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Although flood early warning systems (FEWS) are promising, they inevitably issue false alarms. Many false alarms undermine the credibility of FEWS, which we call a cry wolf effect. Here, we present a simple model that can simulate the cry wolf effect. Our model implies that the cry wolf effect is important if a community is heavily protected by infrastructure and few floods occur. The cry wolf effects get more important as the natural scientific skill to predict flood events is improved.
Yohei Sawada and Risa Hanazaki
Hydrol. Earth Syst. Sci., 24, 4777–4791, https://doi.org/10.5194/hess-24-4777-2020, https://doi.org/10.5194/hess-24-4777-2020, 2020
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In socio-hydrology, human–water interactions are investigated. Researchers have two major methodologies in socio-hydrology, namely mathematical modeling and empirical data analysis. Here we propose a new method for bringing the synergic effect of models and data to socio-hydrology. We apply sequential data assimilation, which has been widely used in geoscience, to a flood risk model to analyze the human–flood interactions by model–data integration.
Yohei Sawada
Hydrol. Earth Syst. Sci., 24, 3881–3898, https://doi.org/10.5194/hess-24-3881-2020, https://doi.org/10.5194/hess-24-3881-2020, 2020
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Hydrologic data assimmilation is the area in which methods to integrate hydrological models and observations are investigated. Recently, hydrological or land models have been increasing their complexity, with very high spatial resolution. However, it is unclear that the current data assimilation method can directly be applied to those hyperresolution models, so that I investigated the applicability and limitation of the existing method by minimalistic numerical experiments.
Yohei Sawada
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-324, https://doi.org/10.5194/hess-2019-324, 2019
Manuscript not accepted for further review
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Hydrologic data assimmilation is the area in which methods to integrate hydrological models and observations are investigated. Recently, hydrological or land models are increasing their complexity with very high spatial resolution. However, it is unclear that the current data assimilation method can directly be applied to those hyperresolution models so that I investigated the applicability and limitation of the existing method by minimalistic numerical experiments.
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Future sea-level rise is of big significance for coastal regions. The melting and acceleration of glaciers plays a major role in sea-level change. Computer simulation of glaciers costs a lot of computational resources. In this publication, we test a new way of simulating glaciers. This approach produces the same results but has the advantage that it needs much less computation time. As simulations can be obtained with fewer computation resources, higher resolution and physics become affordable.
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Yuta Kawai and Hirofumi Tomita
EGUsphere, https://doi.org/10.5194/egusphere-2024-1477, https://doi.org/10.5194/egusphere-2024-1477, 2024
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Chayan Banerjee, Kien Nguyen, Clinton Fookes, Gregory Hancock, and Thomas Coulthard
EGUsphere, https://doi.org/10.5194/egusphere-2024-1191, https://doi.org/10.5194/egusphere-2024-1191, 2024
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In geosciences, the reliance on numerical models necessitates the precise calibration of their parameters to effectively translate information from observed to unobserved settings. We introduce a generalizable framework for calibrating numerical models, with a case study of the geomorphological model CAESAR-Lisflood. This approach efficiently identifies the optimal set of parameters for a given numerical model, enabling retrospective and prospective analyses at various temporal resolutions.
Gianandrea Mannarini, Mario Leonardo Salinas, Lorenzo Carelli, Nicola Petacco, and Josip Orović
Geosci. Model Dev., 17, 4355–4382, https://doi.org/10.5194/gmd-17-4355-2024, https://doi.org/10.5194/gmd-17-4355-2024, 2024
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Ship weather routing has the potential to reduce CO2 emissions, but it currently lacks open and verifiable research. The Python-refactored VISIR-2 model considers currents, waves, and wind to optimise routes. The model was validated, and its computational performance is quasi-linear. For a ferry sailing in the Mediterranean Sea, VISIR-2 yields the largest percentage emission savings for upwind navigation. Given the vessel performance curve, the model is generalisable across various vessel types.
Soyoung Ha, Jonathan J. Guerrette, Ivette Hernández Baños, William C. Skamarock, and Michael G. Duda
Geosci. Model Dev., 17, 4199–4211, https://doi.org/10.5194/gmd-17-4199-2024, https://doi.org/10.5194/gmd-17-4199-2024, 2024
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To mitigate the imbalances in the initial conditions, this study introduces our recent implementation of the incremental analysis update (IAU) in the Model for Prediction Across Scales – Atmospheric (MPAS-A) component coupled with the Joint Effort for Data assimilation Integration (JEDI) through the cycling system. A month-long cycling run demonstrates the successful implementation of the IAU capability in the MPAS–JEDI cycling system.
Federica Castino, Feijia Yin, Volker Grewe, Hiroshi Yamashita, Sigrun Matthes, Simone Dietmüller, Sabine Baumann, Manuel Soler, Abolfazl Simorgh, Maximilian Mendiguchia Meuser, Florian Linke, and Benjamin Lührs
Geosci. Model Dev., 17, 4031–4052, https://doi.org/10.5194/gmd-17-4031-2024, https://doi.org/10.5194/gmd-17-4031-2024, 2024
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We introduce SolFinder 1.0, a decision-making tool to select trade-offs between different objective functions for optimal aircraft trajectories, including fuel use, flight time, NOx emissions, contrail distance, and climate impact. The module is included in the AirTraf 3.0 submodel and uses weather conditions simulated by the EMAC atmospheric model. This paper focuses on the ability of SolFinder to identify eco-efficient trajectories, reducing a flight's climate impact at limited cost penalties.
Ali Dashti, Jens C. Grimmer, Christophe Geuzaine, Florian Bauer, and Thomas Kohl
Geosci. Model Dev., 17, 3467–3485, https://doi.org/10.5194/gmd-17-3467-2024, https://doi.org/10.5194/gmd-17-3467-2024, 2024
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This study developed new meshing workflows to enable the automatic generation of meshes that follow geological models. The workflow allows for importing several geological models as input for Gmsh and later exporting the same number of high-quality meshes. This way, geological uncertainty is directly included in the numerical simulations. This study evaluates the impact of the geological uncertainty on thermohydraulic performance of two reservoirs for high-temperature heat storage applications.
Wangbin Shen, Zhaohui Lin, Zhengkun Qin, and Juan Li
Geosci. Model Dev., 17, 3447–3465, https://doi.org/10.5194/gmd-17-3447-2024, https://doi.org/10.5194/gmd-17-3447-2024, 2024
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In this study, a land surface image assimilation system capable of optimizing the spatial structure of the background field is constructed by introducing the curvelet analysis method and taking the similarity of image structure as a weak constraint. The findings demonstrate that the assimilation of surface soil moisture observation images effectively and reasonably enhances the spatial structure of soil moisture analysis field.
Luan Carlos de Sena Monteiro Ozelim, Michéle Dal Toé Casagrande, and André Luís Brasil Cavalcante
Geosci. Model Dev., 17, 3175–3197, https://doi.org/10.5194/gmd-17-3175-2024, https://doi.org/10.5194/gmd-17-3175-2024, 2024
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The paper addresses synthetic dataset challenges in nonlinear constitutive modeling of soils, providing two datasets: one with 2000 soil types, 40 test conditions each (160 000 triaxial tests), and a second with 2048 soil types, 42 test conditions each (172 032 triaxial tests). Each dataset is a 4000×10 matrix applicable for multivariate forecasting and geotechnical simulations. In addition, a new Python code is introduced, empowering researchers to leverage Python packages for NorSand analyses.
Daniel Giles, Matthew M. Graham, Mosè Giordano, Tuomas Koskela, Alexandros Beskos, and Serge Guillas
Geosci. Model Dev., 17, 2427–2445, https://doi.org/10.5194/gmd-17-2427-2024, https://doi.org/10.5194/gmd-17-2427-2024, 2024
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Digital twins of physical and human systems informed by real-time data are becoming ubiquitous across a wide range of settings. Progress for researchers is currently limited by a lack of tools to run these models effectively and efficiently. A key challenge is the optimal use of high-performance computing environments. The work presented here focuses on a developed open-source software platform which aims to improve this usage, with an emphasis placed on flexibility, efficiency, and scalability.
Stefan J. Miller, Paul A. Makar, and Colin J. Lee
Geosci. Model Dev., 17, 2197–2219, https://doi.org/10.5194/gmd-17-2197-2024, https://doi.org/10.5194/gmd-17-2197-2024, 2024
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This work outlines a new solver written in Fortran to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the forward algorithms of ISORROPIA II. The new code includes numerical improvements that decrease the computational speed (compared to ISORROPIA II) while improving the accuracy of the partitioning solution.
Fernanda Alvarado-Neves, Laurent Ailleres, Lachlan Grose, Alexander R. Cruden, and Robin Armit
Geosci. Model Dev., 17, 1975–1993, https://doi.org/10.5194/gmd-17-1975-2024, https://doi.org/10.5194/gmd-17-1975-2024, 2024
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Previous work has demonstrated that adding geological knowledge to modelling methods creates more accurate and reliable models. Following this reasoning, we added constraints from magma emplacement mechanisms into existing modelling frameworks to improve the 3D characterisation of igneous intrusions. We tested the method on synthetic and real-world case studies, and the results show that our method can reproduce intrusion morphologies with no manual processing and using realistic datasets.
André R. Brodtkorb, Anna Benedictow, Heiko Klein, Arve Kylling, Agnes Nyiri, Alvaro Valdebenito, Espen Sollum, and Nina Kristiansen
Geosci. Model Dev., 17, 1957–1974, https://doi.org/10.5194/gmd-17-1957-2024, https://doi.org/10.5194/gmd-17-1957-2024, 2024
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It is vital to know the extent and concentration of volcanic ash in the atmosphere during a volcanic eruption. Whilst satellite imagery may give an estimate of the ash right now (assuming no cloud coverage), we also need to know where it will be in the coming hours. This paper presents a method for estimating parameters for a volcanic eruption based on satellite observations of ash in the atmosphere. The software package is open source and applicable to similar inversion scenarios.
Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, José Antonio Álvarez Antolínez, Tim Leijnse, and Dano Roelvink
Geosci. Model Dev., 17, 1789–1811, https://doi.org/10.5194/gmd-17-1789-2024, https://doi.org/10.5194/gmd-17-1789-2024, 2024
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Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.
Younghun Kang and Ethan J. Kubatko
Geosci. Model Dev., 17, 1603–1625, https://doi.org/10.5194/gmd-17-1603-2024, https://doi.org/10.5194/gmd-17-1603-2024, 2024
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Models used to simulate the flow of coastal and riverine waters, including flooding, require a geometric representation (or mesh) of geographic features that exhibit a range of disparate spatial scales from large, open waters to small, narrow channels. Representing these features in an accurate way without excessive computational overhead presents a challenge. Here, we develop an automatic mesh generation tool to help address this challenge. Our results demonstrate the efficacy of our approach.
Hui Wan, Kai Zhang, Christopher J. Vogl, Carol S. Woodward, Richard C. Easter, Philip J. Rasch, Yan Feng, and Hailong Wang
Geosci. Model Dev., 17, 1387–1407, https://doi.org/10.5194/gmd-17-1387-2024, https://doi.org/10.5194/gmd-17-1387-2024, 2024
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Sophisticated numerical models of the Earth's atmosphere include representations of many physical and chemical processes. In numerical simulations, these processes need to be calculated in a certain sequence. This study reveals the weaknesses of the sequence of calculations used for aerosol processes in a global atmosphere model. A revision of the sequence is proposed and its impacts on the simulated global aerosol climatology are evaluated.
Christopher J. Vogl, Hui Wan, Carol S. Woodward, and Quan M. Bui
Geosci. Model Dev., 17, 1409–1428, https://doi.org/10.5194/gmd-17-1409-2024, https://doi.org/10.5194/gmd-17-1409-2024, 2024
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Generally speaking, accurate climate simulation requires an accurate evolution of the underlying mathematical equations on large computers. The equations are typically formulated and evolved in process groups. Process coupling refers to how the evolution of each group is combined with that of other groups to evolve the full set of equations for the whole atmosphere. This work presents a mathematical framework to evaluate methods without the need to first implement the methods.
Tom Keel, Chris Brierley, and Tamsin Edwards
Geosci. Model Dev., 17, 1229–1247, https://doi.org/10.5194/gmd-17-1229-2024, https://doi.org/10.5194/gmd-17-1229-2024, 2024
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Jet streams are an important control on surface weather as their speed and shape can modify the properties of weather systems. Establishing trends in the operation of jet streams may provide some indication of the future of weather in a warming world. Despite this, it has not been easy to establish trends, as many methods have been used to characterise them in data. We introduce a tool containing various implementations of jet stream statistics and algorithms that works in a standardised manner.
Amir Golparvar, Matthias Kästner, and Martin Thullner
Geosci. Model Dev., 17, 881–898, https://doi.org/10.5194/gmd-17-881-2024, https://doi.org/10.5194/gmd-17-881-2024, 2024
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Coupled reaction transport modelling is an established and beneficial method for studying natural and synthetic porous material, with applications ranging from industrial processes to natural decompositions in terrestrial environments. Up to now, a framework that explicitly considers the porous structure (e.g. from µ-CT images) for modelling the transport of reactive species is missing. We presented a model that overcomes this limitation and represents a novel numerical simulation toolbox.
Stefan Hergarten
Geosci. Model Dev., 17, 781–794, https://doi.org/10.5194/gmd-17-781-2024, https://doi.org/10.5194/gmd-17-781-2024, 2024
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The Voellmy rheology has been widely used for simulating snow and rock avalanches. Recently, a modified version of this rheology was proposed, which turned out to be able to predict the observed long runout of large rock avalanches theoretically. The software MinVoellmy presented here is the first numerical implementation of the modified rheology. It consists of MATLAB and Python classes, where simplicity and parsimony were the design goals.
Arjun Babu Nellikkattil, Danielle Lemmon, Travis Allen O'Brien, June-Yi Lee, and Jung-Eun Chu
Geosci. Model Dev., 17, 301–320, https://doi.org/10.5194/gmd-17-301-2024, https://doi.org/10.5194/gmd-17-301-2024, 2024
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This study introduces a new computational framework called Scalable Feature Extraction and Tracking (SCAFET), designed to extract and track features in climate data. SCAFET stands out by using innovative shape-based metrics to identify features without relying on preconceived assumptions about the climate model or mean state. This approach allows more accurate comparisons between different models and scenarios.
Mohammad Mortezazadeh, Jean-François Cossette, Ashu Dastoor, Jean de Grandpré, Irena Ivanova, and Abdessamad Qaddouri
Geosci. Model Dev., 17, 335–346, https://doi.org/10.5194/gmd-17-335-2024, https://doi.org/10.5194/gmd-17-335-2024, 2024
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The interpolation process is the most computationally expensive step of the semi-Lagrangian (SL) approach. In this paper we implement a new interpolation scheme into the semi-Lagrangian approach which has the same computational cost as a third-order polynomial scheme but with the accuracy of a fourth-order interpolation scheme. This improvement is achieved by using two third-order backward and forward polynomial interpolation schemes in two consecutive time steps.
Boris Gailleton, Luca C. Malatesta, Guillaume Cordonnier, and Jean Braun
Geosci. Model Dev., 17, 71–90, https://doi.org/10.5194/gmd-17-71-2024, https://doi.org/10.5194/gmd-17-71-2024, 2024
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This contribution presents a new method to numerically explore the evolution of mountain ranges and surrounding areas. The method helps in monitoring with details on the timing and travel path of material eroded from the mountain ranges. It is particularly well suited to studies juxtaposing different domains – lakes or multiple rock types, for example – and enables the combination of different processes.
Denise Degen, Daniel Caviedes Voullième, Susanne Buiter, Harrie-Jan Hendricks Franssen, Harry Vereecken, Ana González-Nicolás, and Florian Wellmann
Geosci. Model Dev., 16, 7375–7409, https://doi.org/10.5194/gmd-16-7375-2023, https://doi.org/10.5194/gmd-16-7375-2023, 2023
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In geosciences, we often use simulations based on physical laws. These simulations can be computationally expensive, which is a problem if simulations must be performed many times (e.g., to add error bounds). We show how a novel machine learning method helps to reduce simulation time. In comparison to other approaches, which typically only look at the output of a simulation, the method considers physical laws in the simulation itself. The method provides reliable results faster than standard.
Carlos Spa, Otilio Rojas, and Josep de la Puente
Geosci. Model Dev., 16, 7237–7252, https://doi.org/10.5194/gmd-16-7237-2023, https://doi.org/10.5194/gmd-16-7237-2023, 2023
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This paper develops a calibration methodology of all absorbing techniques typically used by Fourier pseudo-spectral time-domain (PSTD) methods for geoacoustic wave simulations. The main contributions of the paper are (a) an implementation and quantitative comparison of all absorbing techniques available for PSTD methods through a simple and robust numerical experiment, and (b) a validation of these absorbing techniques in several 3-D seismic scenarios with gradual heterogeneity complexities.
Michael Hillier, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard
Geosci. Model Dev., 16, 6987–7012, https://doi.org/10.5194/gmd-16-6987-2023, https://doi.org/10.5194/gmd-16-6987-2023, 2023
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Neural networks can be used effectively to model three-dimensional geological structures from point data, sampling geological interfaces, units, and structural orientations. Existing neural network approaches for this type of modelling are advanced by the efficient incorporation of unconformities, new knowledge inputs, and improved data fitting techniques. These advances permit the modelling of more complex geology in diverse geological settings, different-sized areas, and various data regimes.
Ane Lopez de Gamiz-Zearra, Cecilie Hansen, Xavier Corrales, Iñaki Quincoces, Izaskun Preciado, and Eider Andonegi
EGUsphere, https://doi.org/10.5194/egusphere-2023-1368, https://doi.org/10.5194/egusphere-2023-1368, 2023
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This paper describes the development of the first calibrated end-to-end Atlantis model for the Bay of Biscay that will help us improve the comprehension of the spatial functioning of the Bay of Biscay ecosystem and help establishing management measures of human activities. Our results highlighted the importance of lower trophic levels to the pelagic system and demonstrate the importance of having accurate and precise data for biological processes.
Tor Nordam, Ruben Kristiansen, Raymond Nepstad, Erik van Sebille, and Andy M. Booth
Geosci. Model Dev., 16, 5339–5363, https://doi.org/10.5194/gmd-16-5339-2023, https://doi.org/10.5194/gmd-16-5339-2023, 2023
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We describe and compare two common methods, Eulerian and Lagrangian models, used to simulate the vertical transport of material in the ocean. They both solve the same transport problems but use different approaches for representing the underlying equations on the computer. The main focus of our study is on the numerical accuracy of the two approaches. Our results should be useful for other researchers creating or using these types of transport models.
Mathieu Gravey and Grégoire Mariethoz
Geosci. Model Dev., 16, 5265–5279, https://doi.org/10.5194/gmd-16-5265-2023, https://doi.org/10.5194/gmd-16-5265-2023, 2023
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Multiple‐point geostatistics are widely used to simulate complex spatial structures based on a training image. The use of these methods relies on the possibility of finding optimal training images and parametrization of the simulation algorithms. Here, we propose finding an optimal set of parameters using only the training image as input. The main advantage of our approach is to remove the risk of overfitting an objective function.
Siting Li, Ping Wang, Hong Wang, Yue Peng, Zhaodong Liu, Wenjie Zhang, Hongli Liu, Yaqiang Wang, Huizheng Che, and Xiaoye Zhang
Geosci. Model Dev., 16, 4171–4191, https://doi.org/10.5194/gmd-16-4171-2023, https://doi.org/10.5194/gmd-16-4171-2023, 2023
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Optimizing the initial state of atmospheric chemistry model input is one of the most essential methods to improve forecast accuracy. Considering the large computational load of the model, we introduce an ensemble optimal interpolation scheme (EnOI) for operational use and efficient updating of the initial fields of chemical components. The results suggest that EnOI provides a practical and cost-effective technique for improving the accuracy of chemical weather numerical forecasts.
Thomas Richter, Véronique Dansereau, Christian Lessig, and Piotr Minakowski
Geosci. Model Dev., 16, 3907–3926, https://doi.org/10.5194/gmd-16-3907-2023, https://doi.org/10.5194/gmd-16-3907-2023, 2023
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Sea ice covers not only the pole regions but affects the weather and climate globally. For example, its white surface reflects more sunlight than land. The oceans around the poles are therefore kept cool, which affects the circulation in the oceans worldwide. Simulating the behavior and changes in sea ice on a computer is, however, very difficult. We propose a new computer simulation that better models how cracks in the ice change over time and show this by comparing to other simulations.
Emma J. MacKie, Michael Field, Lijing Wang, Zhen Yin, Nathan Schoedl, Matthew Hibbs, and Allan Zhang
Geosci. Model Dev., 16, 3765–3783, https://doi.org/10.5194/gmd-16-3765-2023, https://doi.org/10.5194/gmd-16-3765-2023, 2023
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Earth scientists often have to fill in spatial gaps in measurements. This gap-filling or interpolation can be accomplished with geostatistical methods, where the statistical relationships between measurements are used to inform how these gaps should be filled. Despite the broad utility of these methods, there are few freely available geostatistical software applications. We present GStatSim, a Python package for performing different geostatistical interpolation methods.
Ian Madden, Simone Marras, and Jenny Suckale
Geosci. Model Dev., 16, 3479–3500, https://doi.org/10.5194/gmd-16-3479-2023, https://doi.org/10.5194/gmd-16-3479-2023, 2023
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To aid risk managers who may wish to rapidly assess tsunami risk but may lack high-performance computing infrastructure, we provide an accessible software package able to rapidly model tsunami inundation over real topography by leveraging Google's Tensor Processing Unit, a high-performance hardware. Minimally trained users can take advantage of the rapid modeling abilities provided by this package via a web browser thanks to the ease of use of Google Cloud Platform.
Youtong Rong, Paul Bates, and Jeffrey Neal
Geosci. Model Dev., 16, 3291–3311, https://doi.org/10.5194/gmd-16-3291-2023, https://doi.org/10.5194/gmd-16-3291-2023, 2023
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A novel subgrid channel (SGC) model is developed for river–floodplain modelling, allowing utilization of subgrid-scale bathymetric information while performing computations on relatively coarse grids. By including adaptive artificial diffusion, potential numerical instability, which the original SGC solver had, in low-friction regions such as urban areas is addressed. Evaluation of the new SGC model through structured tests confirmed that the accuracy and stability have improved.
Xiaqiong Zhou and Hann-Ming Henry Juang
Geosci. Model Dev., 16, 3263–3274, https://doi.org/10.5194/gmd-16-3263-2023, https://doi.org/10.5194/gmd-16-3263-2023, 2023
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The National Centers for Environmental Prediction Global Forecast System version 16 experienced model instability failures in real-time runs resolved by increasing the minimum thickness depth parameter. Further investigation revealed that the issue was caused by the advection of geopotential heights at the model's layer interfaces. By replacing high-order boundary conditions with zero-gradient boundary conditions for interface-wind reconstruction, the instability was effectively addressed.
Grant T. Euen, Shangxin Liu, Rene Gassmöller, Timo Heister, and Scott D. King
Geosci. Model Dev., 16, 3221–3239, https://doi.org/10.5194/gmd-16-3221-2023, https://doi.org/10.5194/gmd-16-3221-2023, 2023
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Due to the increasing availability of high-performance computing over the past few decades, numerical models have become an important tool for research. Here we test two geodynamic codes that produce such models: ASPECT, a newer code, and CitcomS, an older one. We show that they produce solutions that are extremely close. As methods and codes become more complex over time, showing reproducibility allows us to seamlessly link previously known information to modern methodologies.
Mohammad Kazem Sharifian, Georges Kesserwani, Alovya Ahmed Chowdhury, Jeffrey Neal, and Paul Bates
Geosci. Model Dev., 16, 2391–2413, https://doi.org/10.5194/gmd-16-2391-2023, https://doi.org/10.5194/gmd-16-2391-2023, 2023
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This paper describes a new release of the LISFLOOD-FP model for fast and efficient flood simulations. It features a new non-uniform grid generator that uses multiwavelet analyses to sensibly coarsens the resolutions where the local topographic variations are smooth. Moreover, the model is parallelised on the graphical processing units (GPUs) to further boost computational efficiency. The performance of the model is assessed for five real-world case studies, noting its potential applications.
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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.
A new method to predict chaotic systems from observation and process-based models is proposed by...
