Articles | Volume 12, issue 8
https://doi.org/10.5194/gmd-12-3449-2019
© Author(s) 2019. 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-12-3449-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
07 Aug 2019
Model description paper |
| 07 Aug 2019
VISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigation
Gianandrea Mannarini
CORRESPONDING AUTHOR
CMCC – Centro Euro-Mediterraneo sui Cambiamenti Climatici, Via Augusto Imperatore 16, 73100 Lecce, Italy
Lorenzo Carelli
CMCC – Centro Euro-Mediterraneo sui Cambiamenti Climatici, Via Augusto Imperatore 16, 73100 Lecce, Italy
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Updated situational sea awareness requires an advanced technological system to make data available for decision makers, improving the capacity of intervention and supporting users in managing emergency situations due to natural hazards. The TESSA data platform meets the request of near-real-time access to heterogeneous data with different accuracy, resolution or degrees of aggregation providing efficient and secure data access and strong support to operational oceanographic high-level services.
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Nat. Hazards Earth Syst. Sci., 16, 2713–2727, https://doi.org/10.5194/nhess-16-2713-2016, https://doi.org/10.5194/nhess-16-2713-2016, 2016
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A new web-based and mobile Decision Support System (DSS) for Search-And-Rescue (SAR) at sea is presented, and its performance is evaluated using real case scenarios. The system, named OCEAN-SAR, is accessible via the website http://www.ocean-sar.com. OCEAN-SAR simulates drifting objects at sea, using as input ocean currents and wind. The performance of the service is evaluated by comparing simulations to data from the Italian Coast Guard pertaining to actual incidents in the Mediterranean Sea.
Gianandrea Mannarini, Giuseppe Turrisi, Alessandro D'Anca, Mario Scalas, Nadia Pinardi, Giovanni Coppini, Francesco Palermo, Ivano Carluccio, Matteo Scuro, Sergio Cretì, Rita Lecci, Paola Nassisi, and Luca Tedesco
Nat. Hazards Earth Syst. Sci., 16, 1791–1806, https://doi.org/10.5194/nhess-16-1791-2016, https://doi.org/10.5194/nhess-16-1791-2016, 2016
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Safety and efficiency of navigation can be enhanced through a better situational awareness at sea. We designed and realized an operational infrastructure for providing the navigators with optimal routes through various devices: PC, tablets, and smartphones. Sea-state and wind forecasts are used as inputs. Both motor- and sailboat routes are addressed by VISIR.
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Giovanni Coppini, Palmalisa Marra, Rita Lecci, Nadia Pinardi, Sergio Cretì, Mario Scalas, Luca Tedesco, Alessandro D'Anca, Leopoldo Fazioli, Antonio Olita, Giuseppe Turrisi, Cosimo Palazzo, Giovanni Aloisio, Sandro Fiore, Antonio Bonaduce, Yogesh Vittal Kumkar, Stefania Angela Ciliberti, Ivan Federico, Gianandrea Mannarini, Paola Agostini, Roberto Bonarelli, Sara Martinelli, Giorgia Verri, Letizia Lusito, Davide Rollo, Arturo Cavallo, Antonio Tumolo, Tony Monacizzo, Marco Spagnulo, Rorberto Sorgente, Andrea Cucco, Giovanni Quattrocchi, Marina Tonani, Massimiliano Drudi, Paola Nassisi, Laura Conte, Laura Panzera, Antonio Navarra, and Giancarlo Negro
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SeaConditions aims to support the users by providing the environmental information in due time and with adequate accuracy in the marine and coastal environments, enforcing users' sea situational awareness. SeaConditions consists of a web and mobile application for the provision of meteorological and oceanographic observation and forecasting products. The iOS/Android apps were downloaded by more than 105 000 users and more than 100 000 users have visited the web version (www.sea-conditions.com).
Alessandro D'Anca, Laura Conte, Paola Nassisi, Cosimo Palazzo, Rita Lecci, Sergio Cretì, Marco Mancini, Alessandra Nuzzo, Maria Mirto, Gianandrea Mannarini, Giovanni Coppini, Sandro Fiore, and Giovanni Aloisio
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Updated situational sea awareness requires an advanced technological system to make data available for decision makers, improving the capacity of intervention and supporting users in managing emergency situations due to natural hazards. The TESSA data platform meets the request of near-real-time access to heterogeneous data with different accuracy, resolution or degrees of aggregation providing efficient and secure data access and strong support to operational oceanographic high-level services.
Giovanni Coppini, Eric Jansen, Giuseppe Turrisi, Sergio Creti, Elena Yurievna Shchekinova, Nadia Pinardi, Rita Lecci, Ivano Carluccio, Yogesh Vittal Kumkar, Alessandro D'Anca, Gianandrea Mannarini, Sara Martinelli, Palmalisa Marra, Tommaso Capodiferro, and Tommaso Gismondi
Nat. Hazards Earth Syst. Sci., 16, 2713–2727, https://doi.org/10.5194/nhess-16-2713-2016, https://doi.org/10.5194/nhess-16-2713-2016, 2016
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A new web-based and mobile Decision Support System (DSS) for Search-And-Rescue (SAR) at sea is presented, and its performance is evaluated using real case scenarios. The system, named OCEAN-SAR, is accessible via the website http://www.ocean-sar.com. OCEAN-SAR simulates drifting objects at sea, using as input ocean currents and wind. The performance of the service is evaluated by comparing simulations to data from the Italian Coast Guard pertaining to actual incidents in the Mediterranean Sea.
Gianandrea Mannarini, Giuseppe Turrisi, Alessandro D'Anca, Mario Scalas, Nadia Pinardi, Giovanni Coppini, Francesco Palermo, Ivano Carluccio, Matteo Scuro, Sergio Cretì, Rita Lecci, Paola Nassisi, and Luca Tedesco
Nat. Hazards Earth Syst. Sci., 16, 1791–1806, https://doi.org/10.5194/nhess-16-1791-2016, https://doi.org/10.5194/nhess-16-1791-2016, 2016
Short summary
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Safety and efficiency of navigation can be enhanced through a better situational awareness at sea. We designed and realized an operational infrastructure for providing the navigators with optimal routes through various devices: PC, tablets, and smartphones. Sea-state and wind forecasts are used as inputs. Both motor- and sailboat routes are addressed by VISIR.
Gianandrea Mannarini, Nadia Pinardi, Giovanni Coppini, Paolo Oddo, and Alessandro Iafrati
Geosci. Model Dev., 9, 1597–1625, https://doi.org/10.5194/gmd-9-1597-2016, https://doi.org/10.5194/gmd-9-1597-2016, 2016
Short summary
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VISIR is a new numerical model for the computation of optimal ship routes from meteo-marine forecasts. VISIR offers the scientific community an open platform whereby various ideas and methods for ship route optimization can be shared, tested, and compared to each other.
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Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
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Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
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The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
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Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
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David Meyer, Thomas Nagler, and Robin J. Hogan
Geosci. Model Dev., 14, 5205–5215, https://doi.org/10.5194/gmd-14-5205-2021, https://doi.org/10.5194/gmd-14-5205-2021, 2021
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Mark Jessell, Vitaliy Ogarko, Yohan de Rose, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, https://doi.org/10.5194/gmd-14-5063-2021, 2021
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We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to use to automatically build 3D geological models. By automating the process we are able to remove human bias from the procedure, which makes the workflow reproducible.
Martí Bosch, Maxence Locatelli, Perrine Hamel, Roy P. Remme, Jérôme Chenal, and Stéphane Joost
Geosci. Model Dev., 14, 3521–3537, https://doi.org/10.5194/gmd-14-3521-2021, https://doi.org/10.5194/gmd-14-3521-2021, 2021
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The article presents a novel approach to simulate urban heat mitigation from land use/land cover data based on three biophysical mechanisms: tree shade, evapotranspiration and albedo. An automated procedure is proposed to calibrate the model parameters to best fit temperature observations from monitoring stations. A case study in Lausanne, Switzerland, shows that the approach outperforms regressions based on satellite data and provides valuable insights into design heat mitigation policies.
Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen
Geosci. Model Dev., 14, 2097–2111, https://doi.org/10.5194/gmd-14-2097-2021, https://doi.org/10.5194/gmd-14-2097-2021, 2021
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This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training and thus improve the clustering quality compared to traditional SOM algorithms.
Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel
Geosci. Model Dev., 14, 1493–1510, https://doi.org/10.5194/gmd-14-1493-2021, https://doi.org/10.5194/gmd-14-1493-2021, 2021
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Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (SHAP) can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.
Juan A. Añel, Michael García-Rodríguez, and Javier Rodeiro
Geosci. Model Dev., 14, 923–934, https://doi.org/10.5194/gmd-14-923-2021, https://doi.org/10.5194/gmd-14-923-2021, 2021
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This work shows that it continues to be hard, if not impossible, to obtain some of the most used climate models worldwide. We reach this conclusion through a systematic study and encourage all development teams and research centres to make public the models they use to produce scientific results.
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
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Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen
Geosci. Model Dev., 13, 6149–6164, https://doi.org/10.5194/gmd-13-6149-2020, https://doi.org/10.5194/gmd-13-6149-2020, 2020
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This paper presents a spatiotemporal weighted regression (STWR) model for exploring nonstationary spatiotemporal processes in nature and socioeconomics. A value change rate is introduced in the temporal kernel, which presents significant model fitting and accuracy in both simulated and real-world data. STWR fully incorporates observed data in the past and outperforms geographic temporal weighted regression (GTWR) and geographic weighted regression (GWR) models in several experiments.
Sheri Mickelson, Alice Bertini, Gary Strand, Kevin Paul, Eric Nienhouse, John Dennis, and Mariana Vertenstein
Geosci. Model Dev., 13, 5567–5581, https://doi.org/10.5194/gmd-13-5567-2020, https://doi.org/10.5194/gmd-13-5567-2020, 2020
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Every generation of MIP exercises introduces new layers of complexity and an exponential growth in the amount of data requested. CMIP6 required us to develop a new tool chain and forced us to change our methodologies. The new methods discussed in this paper provided us with an 18 times faster speedup over our existing methods. This allowed us to meet our deadlines and we were able to publish more than half a million data sets on the Earth System Grid Federation (ESGF) for the CMIP6 project.
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886, https://doi.org/10.5194/gmd-13-3863-2020, https://doi.org/10.5194/gmd-13-3863-2020, 2020
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Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Jorge Vicent, Jochem Verrelst, Neus Sabater, Luis Alonso, Juan Pablo Rivera-Caicedo, Luca Martino, Jordi Muñoz-Marí, and José Moreno
Geosci. Model Dev., 13, 1945–1957, https://doi.org/10.5194/gmd-13-1945-2020, https://doi.org/10.5194/gmd-13-1945-2020, 2020
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The modeling of light propagation through the atmosphere is key to process satellite images and to understand atmospheric processes. However, existing atmospheric models can be complex to use in practical applications. Here we aim at providing a new software tool to facilitate using advanced models and to generate large databases of simulated data. As a test case, we use this tool to analyze differences between several atmospheric models, showing the capabilities of this open-source tool.
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
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Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Grzegorz Muszynski, Karthik Kashinath, Vitaliy Kurlin, Michael Wehner, and Prabhat
Geosci. Model Dev., 12, 613–628, https://doi.org/10.5194/gmd-12-613-2019, https://doi.org/10.5194/gmd-12-613-2019, 2019
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We present the automated method for recognizing atmospheric rivers in climate data, i.e., climate model output and reanalysis product. The method is based on topological data analysis and machine learning, both of which are powerful tools that the climate science community often does not use. An advantage of the proposed method is that it is free of selection of subjective threshold conditions on a physical variable. This method is also suitable for rapidly analyzing large amounts of data.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
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Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Wendy Sharples, Ilya Zhukov, Markus Geimer, Klaus Goergen, Sebastian Luehrs, Thomas Breuer, Bibi Naz, Ketan Kulkarni, Slavko Brdar, and Stefan Kollet
Geosci. Model Dev., 11, 2875–2895, https://doi.org/10.5194/gmd-11-2875-2018, https://doi.org/10.5194/gmd-11-2875-2018, 2018
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Next-generation geoscientific models are based on complex model implementations and workflows. Next-generation HPC systems require new programming paradigms and code optimization. In order to meet the challenge of running complex simulations on new massively parallel HPC systems, we developed a run control framework that facilitates code portability, code profiling, and provenance tracking to reduce both the duration and the cost of code migration and development, while ensuring reproducibility.
Daojun Zhang, Na Ren, and Xianhui Hou
Geosci. Model Dev., 11, 2525–2539, https://doi.org/10.5194/gmd-11-2525-2018, https://doi.org/10.5194/gmd-11-2525-2018, 2018
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Geographically weighted regression is a widely used method to deal with spatial heterogeneity, which is common in geostatistics. However, most existing software does not support logistic regression and cannot deal with missing data, which exist extensively in mineral prospectivity mapping. This work generalized logistic regression to spatial statistics based on a spatially weighted technique. The new model also supports an anisotropic local window, which is another innovative point.
Thomas Block, Sabine Embacher, Christopher J. Merchant, and Craig Donlon
Geosci. Model Dev., 11, 2419–2427, https://doi.org/10.5194/gmd-11-2419-2018, https://doi.org/10.5194/gmd-11-2419-2018, 2018
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For calibration and validation purposes it is necessary to detect simultaneous data acquisitions from different spaceborne platforms. We present an algorithm and a software system which implements a general approach to resolve this problem. The multisensor matchup system (MMS) can detect simultaneous acquisitions in a large dataset (> 100 TB) and extract data for matching locations for further analysis. The MMS implements a flexible software infrastructure and allows for high parallelization.
David Hassell, Jonathan Gregory, Jon Blower, Bryan N. Lawrence, and Karl E. Taylor
Geosci. Model Dev., 10, 4619–4646, https://doi.org/10.5194/gmd-10-4619-2017, https://doi.org/10.5194/gmd-10-4619-2017, 2017
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We present a formal data model for version 1.6 of the CF (Climate and Forecast) metadata conventions that provide a description of the physical meaning of geoscientific data and their spatial and temporal properties. We describe the CF conventions and how they lead to our CF data model, and compare it other data models for storing data and metadata. We present cf-python version 2.1: a software implementation of the CF data model capable of manipulating any CF-compliant dataset.
Iulia Ilie, Peter Dittrich, Nuno Carvalhais, Martin Jung, Andreas Heinemeyer, Mirco Migliavacca, James I. L. Morison, Sebastian Sippel, Jens-Arne Subke, Matthew Wilkinson, and Miguel D. Mahecha
Geosci. Model Dev., 10, 3519–3545, https://doi.org/10.5194/gmd-10-3519-2017, https://doi.org/10.5194/gmd-10-3519-2017, 2017
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Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
Xinqiao Duan, Lin Li, Haihong Zhu, and Shen Ying
Geosci. Model Dev., 10, 239–253, https://doi.org/10.5194/gmd-10-239-2017, https://doi.org/10.5194/gmd-10-239-2017, 2017
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This article proposes an optimized transformation for topographic datasets. The resulting topographic grid exhibits good surface approximation and quasi-uniform high-quality. Both features of the processed topography build a concrete base from which improved endogenous or exogenous parameters can be derived, and makes it suitable for Earth and environmental simulations.
Venkatramani Balaji, Eric Maisonnave, Niki Zadeh, Bryan N. Lawrence, Joachim Biercamp, Uwe Fladrich, Giovanni Aloisio, Rusty Benson, Arnaud Caubel, Jeffrey Durachta, Marie-Alice Foujols, Grenville Lister, Silvia Mocavero, Seth Underwood, and Garrett Wright
Geosci. Model Dev., 10, 19–34, https://doi.org/10.5194/gmd-10-19-2017, https://doi.org/10.5194/gmd-10-19-2017, 2017
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Climate models are among the most computationally expensive scientific applications in the world. We present a set of measures of computational performance that can be used to compare models that are independent of underlying hardware and the model formulation. They are easy to collect and reflect performance actually achieved in practice. We are preparing a systematic effort to collect these metrics for the world's climate models during CMIP6, the next Climate Model Intercomparison Project.
Massimiliano Alvioli, Ivan Marchesini, Paola Reichenbach, Mauro Rossi, Francesca Ardizzone, Federica Fiorucci, and Fausto Guzzetti
Geosci. Model Dev., 9, 3975–3991, https://doi.org/10.5194/gmd-9-3975-2016, https://doi.org/10.5194/gmd-9-3975-2016, 2016
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Slope units are morphological mapping units bounded by drainage and divide lines that maximize within-unit homogeneity and between-unit heterogeneity. We use r.slopeunits, a software for the automatic delination of slope units. We outline an objective procedure to optimize the software input parameters for landslide susceptibility (LS) zonation. Optimization is achieved by maximizing an objective function that simultaneously evaluates terrain aspect segmentation quality and LS model performance.
Duncan Watson-Parris, Nick Schutgens, Nicholas Cook, Zak Kipling, Philip Kershaw, Edward Gryspeerdt, Bryan Lawrence, and Philip Stier
Geosci. Model Dev., 9, 3093–3110, https://doi.org/10.5194/gmd-9-3093-2016, https://doi.org/10.5194/gmd-9-3093-2016, 2016
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In this paper we describe CIS, a new command line tool for the easy visualization, analysis and comparison of a wide variety of gridded and ungridded data sets used in Earth sciences. Users can now use a single tool to not only view plots of satellite, aircraft, station or model data, but also bring them onto the same spatio-temporal sampling. This allows robust, quantitative comparisons to be made easily. CIS is an open-source project and welcomes input from the community.
Benjamin F. Jamroz and Robert Klöfkorn
Geosci. Model Dev., 9, 2881–2892, https://doi.org/10.5194/gmd-9-2881-2016, https://doi.org/10.5194/gmd-9-2881-2016, 2016
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The scalability of computational applications on current and next-generation supercomputers is increasingly limited by the cost of inter-process communication. We implement communication hiding data exchange in the High-Order Methods Modeling Environment (HOMME) for the time integration of the hydrostatic fluid equations using both the spectral-element and discontinuous Galerkin methods. The presented approach produces significant performance and scalability gains in large-scale simulations.
T. Fischer, D. Naumov, S. Sattler, O. Kolditz, and M. Walther
Geosci. Model Dev., 8, 3681–3694, https://doi.org/10.5194/gmd-8-3681-2015, https://doi.org/10.5194/gmd-8-3681-2015, 2015
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We present a workflow to convert geological models into the open-source VTU format for usage in numerical simulation models. Tackling relevant scientific questions or engineering tasks often involves multidisciplinary approaches. Conversion workflows are needed between the diverse tools of the various disciplines. Our approach offers an open-source, platform-independent, robust, and comprehensible method that is potentially useful for a multitude of similar environmental studies.
S. Moulds, W. Buytaert, and A. Mijic
Geosci. Model Dev., 8, 3215–3229, https://doi.org/10.5194/gmd-8-3215-2015, https://doi.org/10.5194/gmd-8-3215-2015, 2015
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The contribution of lulcc is to provide a free and open-source framework for land use change modelling. The software, which is provided as an R package, addresses problems associated with the current paradigm of closed-source, specialised land use change modelling software which disrupt the scientific process. It is an attempt to move the discipline towards open and transparent science and to ensure land use change models are accessible to scientists working across the geosciences.
B. Poulter, N. MacBean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann, P. Defourny, S. Hagemann, M. Herold, G. Kirches, C. Lamarche, D. Lederer, C. Ottlé, M. Peters, and P. Peylin
Geosci. Model Dev., 8, 2315–2328, https://doi.org/10.5194/gmd-8-2315-2015, https://doi.org/10.5194/gmd-8-2315-2015, 2015
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Land cover is an essential variable in earth system models and determines conditions driving biogeochemical, energy and water exchange between ecosystems and the atmosphere. A methodology is presented for mapping plant functional types used in global vegetation models from a updated land cover classification system and open-source conversion tool, resulting from a consultative process among map producers and modelers engaged in the European Space Agency’s Land Cover Climate Change Initiative.
J. Du, C. Chen, V. Lesur, and L. Wang
Geosci. Model Dev., 8, 1979–1990, https://doi.org/10.5194/gmd-8-1979-2015, https://doi.org/10.5194/gmd-8-1979-2015, 2015
D. C. Wong, C. E. Yang, J. S. Fu, K. Wong, and Y. Gao
Geosci. Model Dev., 8, 1033–1046, https://doi.org/10.5194/gmd-8-1033-2015, https://doi.org/10.5194/gmd-8-1033-2015, 2015
M. Mergili, I. Marchesini, M. Alvioli, M. Metz, B. Schneider-Muntau, M. Rossi, and F. Guzzetti
Geosci. Model Dev., 7, 2969–2982, https://doi.org/10.5194/gmd-7-2969-2014, https://doi.org/10.5194/gmd-7-2969-2014, 2014
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The article deals with strategies to (i) reduce computation time and to (ii) appropriately account for uncertain input parameters when applying an open source GIS sliding surface model to estimate landslide susceptibility for a 90km² study area in central Italy. For (i), the area is split into a large number of tiles, enabling the exploitation of multi-processor computing environments. For (ii), the model is run with various parameter combinations to compute the slope failure probability.
Cited articles
Aendekerk, F.: Weather Route Optimization for Oceanic Vessels, Master's thesis,
Delft University of Technology, 2018. a
Alexandersson, M.: A study of methods to predict added resistance in waves,
Master's thesis, KTH Centre for Naval Architecture, 2009. a
Almeida, J., Silvestre, C., and Pascoal, A.: Cooperative control of multiple
surface vessels in the presence of ocean currents and parametric model
uncertainty, Int. J. Robust Nonlin., 20,
1549–1565, 2010. a
Aouf, L. and Lefevre, J.-M.: On the assimilation of the ASAR L2 wave spectra
in the operational wave model MFWAM, in: SeaSAR 2012, vol. 709, 2013. a
Bazari, Z. and Longva, T.: MEPC 63/INF.2 (Annex) assessment of imo mandated energy efficiency measures for international shipping. Technical report, International Maritime Organization, London, UK, 2011. a
Bentley, J. L.: Multidimensional Binary Search Trees Used for Associative
Searching, Commun. ACM, 18, 509–517, https://doi.org/10.1145/361002.361007,
1975. a
Berger, M. J. and Colella, P.: Local adaptive mesh refinement for shock
hydrodynamics, J. Comput. Phys., 82, 64–84, 1989. a
Berline, L., Testut, C.-E., Brasseur, P., and Verron, J.: Variability of the
Gulf Stream position and transport between 1992 and 1999: a re-analysis based
on a data assimilation experiment, Int. J. Remote Sens.,
27, 417–432, 2006. a
Bertram, V.: Practical ship hydrodynamics, Elsevier, Woburn, MA, 2000. a
Bertram, V. and Couser, P.: Computational Methods for Seakeeping and Added
Resistance in Waves, in: 13th International Conference on Computer and IT
Applications in the Maritime Industries, Redworth, 12–14 May 2014, edited by:
Volker, B., Technische Universität Hamburg-Harburg, 8–16, 2014. a
Bos, M.: An Ensemble Prediction of Added Wave Resistance to Identify the
Effect of Spread of Wave Conditions on Ship Performance, in: 3rd Hull
Performance & Insight Conference, edited by: Bertram, V., 265–274,
available at: http://data.hullpic.info/hullpic2018_redworth.pdf (last access: 12 July 2019), 2018. a
Breivik, Ø. and Allen, A. A.: An operational search and rescue model for the
Norwegian Sea and the North Sea, J. Marine Syst., 69, 99–113,
2008. a
Broer, H. W.: Bernoulli's light ray solution of the brachistochrone problem
through Hamilton's eyes, International Journal of Bifurcation and Chaos, 24,
1440009, https://doi.org/10.1142/S0218127414400094, 2014. a
Cheung, J. C. H.: Flight planning: node-based trajectory prediction and
turbulence avoidance, Meteorol. Appl., 25, 78–85, https://doi.org/10.1002/met.1671,
2017. a
Clementi, E., Oddo, P., Drudi, M., Pinardi, N., Korres, G., and Grandi, A.:
Coupling hydrodynamic and wave models: first step and sensitivity experiments
in the Mediterranean Sea, Ocean Dynam., 67, 1293–1312, https://doi.org/10.1007/s10236-017-1087-7,
2017. a
De Berg, M., Van Kreveld, M., Overmars, M., and Schwarzkopf, O.: Computational
geometry, in: Computational geometry, Springer, 1–17, 1997. a
Diestel, R.: Graph Theory, Springer-Verlag,
available at: http://diestel-graph-theory.com/ (last access: 12 July 2019), 2005. a
Dijkstra, E. W.: A note on two problems in connexion with graphs, Numer.
Math., 1.1, 269–271, 1959. a
Dubins, L. E.: On curves of minimal length with a constraint on average
curvature, and with prescribed initial and terminal positions and tangents,
Am. J. Math., 79, 497–516, 1957. a
Foschini, L., Hershberger, J., and Suri, S.: On the complexity of
time-dependent shortest paths, Algorithmica, 68, 1075–1097, 2014. a
Fossen, T. I., Pettersen, K. Y., and Galeazzi, R.: Line-of-sight path following
for dubins paths with adaptive sideslip compensation of drift forces, IEEE
T. Contr. Syst. T., 23, 820–827, 2015. a
Fu, L.-L. and Smith, R. D.: Global ocean circulation from satellite altimetry
and high-resolution computer simulation, B. Am.
Meteorol. Soc., 77, 2625–2636, 1996. a
Fujii, M., Hashimoto, H., and Taniguchi, Y.: Analysis of satellite AIS Data to
derive weather judging criteria for voyage route selection, TransNav:
International Journal on Marine Navigation and Safety of Sea Transportation, 11, 271–277, https://doi.org/10.12716/1001.11.02.09,
2017. a
Fujiwara, T., Ueno, M., and Ikeda, Y.: Cruising performance of a large
passenger ship in heavy sea, in: The Sixteenth International Offshore and
Polar Engineering Conference, International Society of Offshore and Polar
Engineers, 2006. a
Gerritsma, J. and Beukelman, W.: Analysis of the resistance increase in waves
of a fast cargo ship, International Shipbuilding Progress, 19, 285–293,
1972. a
Harvald, S. A.: Resistance and propulsion of ships, Krieger Publishing Company,
1992. a
Hinwood, J. B., Blackman, D. R., and Lleonart, G. T.: Some properties of swell
in the Southern Ocean, in: 18th International Conference on Coastal
Engineering, 261–269, https://doi.org/10.1061/9780872623736.017, 1982. a
IMO: Resolution A.526(13) Performance Standards for Rate-Of-Turn
Indicators, Tech. rep., International Maritime Organization (IMO), London,
UK, 1983. a
IMO: MSC 76/23/Add.1 Resolution MSC.137(76), Annex 6 – Standards for ship
manoeuvrability, Tech. rep., International Maritime Organization, London,
UK, 2002. a
IMO: MSC.1/Circ.1281 Explanatory notes to the international code on intact
stability, Tech. rep., International Maritime Organization, London, UK, 2008. a
IMO: MEPC 59/65/5 Interpretations of, and amendments to, MARPOL and related
instruments, Tech. rep., International Maritime Organization, London, UK,
2009a. a
IMO: MEPC 59/INF.10 (Annex) “Second IMO GHG Study 2009”, Technical report, International Maritime Organization, London, UK, 2009c. a
IMO: MEPC 67/INF.3 (Annex) “Third IMO GHG Study 2014”, Technical report, International Maritime Organization, London, UK, 2014. a
IMO: MEPC.176(58) Amendments to MARPOL Annex VI, Tech. rep.,
International Maritime Organization, London, UK, 2016. a
IMO: MEPC 73/WP5 Report of the fourth meeting of the Intersessional Working
Group on Reduction of GHG emissions from ships (ISWG-GHG 4), Tech. rep.,
International Maritime Organization, London, UK, 2018a. a
IMO: MEPC.304(72) Initial IMO strategy on reduction of GHG emissions
from ships, Tech. rep., International Maritime Organization, London, UK,
2018b. a
IMO: SDC 5/J/7 Finalization of second generation intact stability criteria,
Tech. rep., International Maritime Organization, London, UK,
2018c. a
IPCC: Global Warming of 1.5 deg, Tech. rep., WMO, UNEP,
available at: http://ipcc.ch/report/sr15/ (last access: 12 July 2019), 2018. a
Jameson, A. and Vassberg, J. C.: Studies of alternative numerical optimization
methods applied to the brachistochrone problem, Computational Fluid Dynamics
Journal, 9, 281–296, 2000. a
JRC and PBL: Emission Database for Global Atmospheric Research (EDGAR), Tech.
rep., European Commission,
available at: http://edgar.jrc.ec.europa.eu/overview.php?v=CO2ts1990-2015 (last access: 12 July 2019),
2016. a
Kang, D., Curchitser, E. N., and Rosati, A.: Seasonal variability of the Gulf
Stream kinetic energy, J. Phys. Oceanogr., 46, 1189–1207,
2016. a
Komen, G. J., Cavaleri, L., Donelan, M., Hasselmann, K., Hasselmann, S., and
Janssen, P.: Dynamics and modelling of ocean waves, Dynamics and Modelling of
Ocean Waves, edited by: Komen, G. J., Cavaleri, L., Donelan, M., Hasselmann, K., Hasselmann, S., and Janssen, P. A. E. M., 554 pp., ISBN 0521577810, Cambridge, UK,
Cambridge University Press, August 1996, p. 554, 1996. a
Legrand, S., Legat, V., and Deleersnijder, E.: Delaunay mesh generation for an
unstructured-grid ocean general circulation model, Ocean Modell., 2,
17–28, 2000. a
Likhachev, M., Ferguson, D. I., Gordon, G. J., Stentz, A., and Thrun, S.:
Anytime Dynamic A*: An Anytime, Replanning Algorithm, in: ICAPS,
262–271, 2005. a
Lloyd's: Top 100 – Container Ports 2017, Tech. rep., Informa UK Ltd,
available at: https://maritimeintelligence.informa.com/content/top-100-success (last access: 12 July 2019),
2018. a
Lo, H. K. and McCord, M. R.: Adaptive ship routing through stochastic ocean
currents: General formulations and empirical results, Transport. Res.
A-Pol., 32, 547–561, 1998. a
Lolla, T., Lermusiaux, P. F., Ueckermann, M. P., and Haley Jr., P. J.:
Time-optimal path planning in dynamic flows using level set equations: theory
and schemes, Ocean Dynam., 64, 1373–1397, 2014. a
Loria, A., Fossen, T. I., and Panteley, E.: A separation principle for dynamic
positioning of ships: theoretical and experimental results, IEEE T.
Contr. Syst. T., 8, 332–343, 2000. a
Lu, L.-F., Sasa, K., Sasaki, W., Terada, D., Kano, T., and Mizojiri, T.: Rough
wave simulation and validation using onboard ship motion data in the Southern
Hemisphere to enhance ship weather routing, Ocean Eng., 144, 61–77,
2017. a
Lu, R., Turan, O., Boulougouris, E., Banks, C., and Incecik, A.: A
semi-empirical ship operational performance prediction model for voyage
optimization towards energy efficient shipping, Ocean Eng., 110, 18–28, https://doi.org/10.1016/j.oceaneng.2015.07.042, 2015. a
Lumpkin, R., Treguier, A.-M., and Speer, K.: Lagrangian eddy scales in the
northern Atlantic Ocean, J. Phys. Oceanogr., 32, 2425–2440,
2002. a
Madec, G.: NEMO reference manual, ocean dynamic component: NEMO-OPA, Note
du pôle de modélisation, Institut Pierre Simon Laplace, France,
2008. a
MANDieselTurbo: Basic Principles of Ship Propulsion, Tech. rep.,
MANDieselTurbo, Augsburg, Germany, 2011. a
Mannarini, G. and Carelli, L.: ISIR-1.b ship routing model (Version 1.00), Zenodo, https://doi.org/10.5281/zenodo.2563074, 2019a. a
Mannarini, G. and Carelli, L.:
Support Data Assets for Figures and Tables in gmd-2018-292 (Geosci. Model Dev.) (Version 1.05) [Data set], Zenodo, https://doi.org/10.5281/zenodo.3258177, 2019b. a
Mannarini, G. and Carelli, L.: Optimal tracks for USNFK-ESALG route in 2017, accounting for CMEMS waves, https://doi.org/10.5446/38218, 2019c. a
Mannarini, G. and Carelli, L.: Optimal tracks for USNFK-ESALG route in 2017, accounting for both CMEMS waves and ocean currents, https://doi.org/10.5446/38483, 2019d. a
Mannarini, G. and Carelli, L.: Optimal tracks for ESALG-USNFK route in 2017, accounting for CMEMS waves, https://doi.org/10.5446/38484, 2019e. a
Mannarini, G. and Carelli, L.: Optimal tracks for ESALG-USNFK route in 2017, accounting for both CMEMS waves and ocean currents, https://doi.org/10.5446/38482, 2019f. a
Mannarini, G., Lecci, R., and Coppini, G.: Introducing sailboats into ship
routing system VISIR, in: 6th International Conference on Information,
Intelligence, Systems and Applications (IISA 2015), IEEE-Xplore, 1–6,
https://doi.org/10.1109/IISA.2015.7387962,
2015. a
Mannarini, G., Turrisi, G., D'Anca, A., Scalas, M., Pinardi, N., Coppini, G., Palermo, F., Carluccio, I., Scuro, M., Cretì, S., Lecci, R., Nassisi, P., and Tedesco, L.: VISIR: technological infrastructure of an operational service for safe and efficient navigation in the Mediterranean Sea, Nat. Hazards Earth Syst. Sci., 16, 1791–1806, https://doi.org/10.5194/nhess-16-1791-2016, 2016b. a, b
Mannarini, G., Carelli, L., Zissis, D., Spiliopoulos, G., and Chatzikokolakis,
K.: Preliminary inter-comparison of AIS data and optimal ship tracks,
TransNav, 13, 53–61, https://doi.org/10.12716/1001.13.01.04, 2019. a
Maximenko, N., Hafner, J., and Niiler, P.: Pathways of marine debris derived
from trajectories of Lagrangian drifters, Mar. Pollut. Bull., 65,
51–62, 2012. a
Meehl, G. A.: Characteristics of surface current flow inferred from a global
ocean current data set, J. Phys. Oceanogr., 12, 538–555, 1982. a
Minobe, S., Miyashita, M., Kuwano-Yoshida, A., Tokinaga, H., and Xie, S.-P.:
Atmospheric response to the Gulf Stream: Seasonal variations, J.
Climate, 23, 3699–3719, 2010. a
Morin, D.: The Lagrangian Method, Tech. rep., Harvard,
available at: http://www.people.fas.harvard.edu/~djmorin/chap6.pdf (last access: 12 July 2019), 2007. a
Munk, W. H.: Origin and generation of waves, Tech. rep., SCRIPPS Institution of
Ocenography, La Jolla, 1951. a
Newman, J. N.: Marine hydrodynamics, MIT press, Cambridge, Massachusetts and
London, England, 1977. a
Pascual, A., Faugère, Y., Larnicol, G., and Traon, P. L.: Improved
description of the ocean mesoscale variability by combining four satellite
altimeters, Geophys. Res. Lett., 33, L02611, https://doi.org/10.1029/2005GL024633,
2006. a
Perakis, A. and Papadakis, N.: Minimal Time Vessel Routing in a Time-Dependent
Environment, Transportation Science, 23, 266–276, 1989. a
Pereira, A. A., Binney, J., Hollinger, G. A., and Sukhatme, G. S.: Risk-aware
Path Planning for Autonomous Underwater Vehicles using Predictive Ocean
Models, J. Field Robot., 30, 741–762, 2013. a
Pinardi, N., Zavatarelli, M., Adani, M., Coppini, G., Fratianni, C., Oddo, P.,
Simoncelli, S., Tonani, M., Lyubartsev, V., Dobricic, S., and Bonaduce, A.:
Mediterranean Sea large-scale low-frequency ocean variability and water mass
formation rates from 1987 to 2007: a retrospective analysis, Prog.
Oceanogr., 132, 318–332, 2015. a
Pontryagin, L., Boltyianskii, V., Gamkrelidze, R., and Mishchenko, E.: The
Mathematical Theory of Optimal Processes, vol. 4, Interscience, New York,
London, Paris, Montreux, Tokyo, Gordon and breach science publishers Edn.,
1962. a
Robinson, A., Sellschopp, J., Warn-Varnas, A., Leslie, W., Lozano, C., Jr.,
P. H., Anderson, L., and Lermusiaux, P.: The Atlantic Ionian Stream, J. Marine Syst., 20, 129–156,
https://doi.org/10.1016/S0924-7963(98)00079-7, 1999. a
Roquet, F., Wunsch, C., Forget, G., Heimbach, P., Guinet, C., Reverdin, G.,
Charrassin, J.-B., Bailleul, F., Costa, D. P., Huckstadt, L. A., Goetz, K. T., Kovacs, K. M., Lydersen, C., Biuw, M., Nøst, O. A., Bornemann, H., Ploetz, J., Bester, M. N., McIntyre, T., Muelbert, M. C., Hindell, M. A., McMahon, C. R., Williams, G., Harcourt, R., Field, I. C., Chafik, L., Nicholls, K. W., Boehme, L., and Fedak, M. A.:
Estimates of the Southern Ocean general circulation improved by animal-borne
instruments, Geophys. Res. Lett., 40, 6176–6180, 2013. a
Sandery, P. A. and Sakov, P.: Ocean forecasting of mesoscale features can
deteriorate by increasing model resolution towards the submesoscale, Nat.
Commun., 8, 1566, https://doi.org/10.1038/s41467-017-01595-0, 2017. a
She, J., Allen, I., Buch, E., Crise, A., Johannessen, J. A., Le Traon, P.-Y., Lips, U., Nolan, G., Pinardi, N., Reißmann, J. H., Siddorn, J., Stanev, E., and Wehde, H.: Developing European operational oceanography for Blue Growth, climate change adaptation and mitigation, and ecosystem-based management, Ocean Sci., 12, 953–976, https://doi.org/10.5194/os-12-953-2016, 2016. a
Shewchuk, J. R.: Delaunay refinement algorithms for triangular mesh generation,
Computational Geometry, 22, 21–74, 2002. a
Stentz, A.: The focussed D* algorithm for real-time replanning, in:
Proceedings of the International Joint Conference on Artificial Intelligence, 95, 1652–1659, 1995. a
Subramani, D. N. and Lermusiaux, P. F.: Energy-optimal path planning by
stochastic dynamically orthogonal level-set optimization, Ocean Modell.,
100, 57–77, 2016. a
Szlapczynska, J.: Multi-objective weather routing with customised criteria and
constraints, J. Navigation, 68, 338–354, 2015. a
Techy, L., Woolsey, C. A., and Morgansen, K. A.: Planar path planning for
flight vehicles in wind with turn rate and acceleration bounds, in: 2010 IEEE International Conference on Robotics
and Automation (ICRA), 3240–3245,
2010. a
Tomczak, M. and Godfrey, J. S.: Regional oceanography: an introduction,
Pergamon, 1994. a
Triantafyllou, M. S. and Hover, F. S.: Maneuvering and control of marine
vehicles, Department of Ocean Engineering, Massachussets Institute of
Technology, Cambridge, USA, 2003. a
Tsou, M.-C. and Cheng, H.-C.: An Ant Colony Algorithm for efficient ship
routing, Pol. Marit. Res., 20, 28–38, 2013. a
Tsujimoto, M., Kuroda, M., and Sogihara, N.: Development of a calculation
method for fuel consumption of ships in actual seas with performance
evaluation, in: ASME 2013 32nd International Conference on Ocean, Offshore
and Arctic Engineering, American Society of
Mechanical Engineers, V009T12A047–V009T12A047, 2013. a
UNFCCC: Adoption of the Paris Agreement, Tech. Rep. s32, United Nations
Office, Geneva, 2015. a
Vratanar, B. and Saje, M.: On the analytical solution of the brachistochrone
problem in a non-conservative field, Int. J. Nonlin.
Mech., 33, 489–505, 1998. a
Weatherall, P., Marks, K. M., Jakobsson, M., Schmitt, T., Tani, S., Arndt,
J. E., Rovere, M., Chayes, D., Ferrini, V., and Wigley, R.: A new digital
bathymetric model of the world's oceans, Earth Space Sci., 2,
331–345, 2015. a
Wessel, P. and Smith, W. H.: A global, self-consistent, hierarchical,
high-resolution shoreline database, J. Geophys. Res.-Sol. Ea., 101, 8741–8743, 1996. a
WMO-Secretariat: Guide to Marine Meteorological Services, WMO-No.471, WMO,
available at: http://www.jcomm.info/index.php?option=com_oe&task=viewDocumentRecord&docID=19901 (last access: 12 July 2019),
2017. a
Zor, C. and Kittler, J.: Maritime anomaly detection in ferry tracks, in:
2017 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP), 2647–2651, 2017. a
Short summary
The VISIR ship-routing model is updated in order to deal with ocean currents.
The optimal tracks we computed through VISIR in the Atlantic ocean show great seasonal and regional variability, following a variable influence of surface gravity waves and currents. We assess how these tracks contribute to voyage energy-efficiency gains through a standard indicator (EEOI) of the International Maritime Organization. Also, the new model features are validated against an exact analytical benchmark.
The VISIR ship-routing model is updated in order to deal with ocean currents.
The optimal...
