Boosting Spectral Partitioning by Sampling and Iteration

2005 ◽  
pp. 473-482
Author(s):  
Joachim Giesen ◽  
Dieter Mitsche
Keyword(s):  
Spectral Partitioning

Peakedness of Wave Systems Observed on the French Wave Energy Test Site (SEM-REV) Using a Spectral Partitioning Algorithm

2013 ◽  
Author(s):  
Jean-Baptiste Saulnier ◽  
Izan Le Crom
Keyword(s):  
Wave Energy ◽  
Test Site ◽  
Extreme Conditions ◽  
Multiple Wave ◽  
Wave Energy Converters ◽  
Marine Energy ◽  
Partitioning Algorithm ◽  
Spectral Partitioning ◽  
The Individual ◽  
Energy Converters

Located off the Guérande peninsula, SEM-REV is the French maritime facility dedicated to the testing of wave energy converters and related components. Lead by Ecole Centrale de Nantes through the LHEEA laboratory, its aim is to promote research alongside the development of new offshore technologies. To this end, the 1km2, grid-connected zone is equipped with a comprehensive instruments network sensing met-ocean processes and especially waves, with two identical directional Waverider buoys deployed on the site since 2009. For the design of moored floating structures and, a fortiori, floating marine energy converters, the knowledge of the main wave resource — for regular operation — but also extreme conditions — for moorings and device survivability — has to be as precise as possible. Also, the consideration of the multiple wave systems (swell, wind sea) making up the sea state is a key asset for the support of developers before and during the testing phase. To this end, a spectral partitioning algorithm has been implemented which enables the individual characterisation of wave systems, in particular that of their spectral peakedness which is especially addressed in this work. Peakedness has been shown to be strongly related to the groupiness of large waves and is defined here as the standard JONSWAP’s peak enhancement factor γ. Statistics related to this quantity are derived from the measurement network, with a particular focus on the extreme conditions reported on SEM-REV (Joachim storm).

scholarly journals Spectral Response of Water Under Different Concentrations of Suspended Sediment: Measurement and Simplified Modeling

2019 ◽  
Vol 11 (3) ◽  
pp. 327
Author(s):  
J. W. B. Lopes ◽  
F. B. Lopes ◽  
E. M. de Andrade ◽  
L. C. G. Chaves ◽  
M. G. R. Carneiro
Keyword(s):  
Organic Matter ◽  
Suspended Sediment ◽  
Spectral Response ◽  
Salt Content ◽  
Organic Matter Content ◽  
Textural Properties ◽  
Sediment Concentration ◽  
Matter Content ◽  
Irrigated Agriculture ◽  
Spectral Partitioning

Understanding the spectral behaviour of water is of the greatest importance to the quality management of water resources. Continuous monitoring by remote sensing is therefore essential for administrators seeking the efficient management of its many uses. The aim of this research was to characterise the spectral response of water submitted to different concentrations of sediments of varying textural properties, organic matter and salts, and to identify the implications of these characteristics using simplified modelling. The experiment was conducted at the Radiometry Laboratory of the Department of Agricultural Engineering of the Federal University of Ceará, Brazil. The soils used in the research came from two areas of irrigated agriculture in Ceará, one in Morada Nova and the other in Pentecoste. Both soils were classified as Fluvic Neosols; the first saline and the second saline-sodic, and presented significant differences in granulometric composition and organic matter content. From the results, it can be concluded that: (i) sediments added at different concentrations cause an increase and deformation of the reflectance curves, and that maximum spectral partitioning occurs at two reflectance peaks; (ii) derivative analysis favours the identification of wavelengths that best differentiate sediment concentration, allowing more-efficient modelling of the process; (iii) the characteristics of texture, organic matter and salt content have little effect on estimating suspended-sediment concentration in the water, making multiple linear regression modelling a viable option for this purpose.

scholarly journals Spatially Tracking Wave Events in Partitioned Numerical Wave Model Outputs

2019 ◽  
Vol 36 (10) ◽  
pp. 1933-1944 ◽  
Author(s):  
Haoyu Jiang
Keyword(s):  
Grid Point ◽  
Wave Model ◽  
Ocean Waves ◽  
Model Verification ◽  
Analysis Model ◽  
Wave Parameters ◽  
Numerical Wave ◽  
Wave Models ◽  
Spectral Partitioning ◽  
The Impact

AbstractNumerical wave models can output partitioned wave parameters at each grid point using a spectral partitioning technique. Because these wave partitions are usually organized according to the magnitude of their wave energy without considering the coherence of wave parameters in space, it can be difficult to observe the spatial distributions of wave field features from these outputs. In this study, an approach for spatially tracking coherent wave events (which means a cluster of partitions originating from the same meteorological event) from partitioned numerical wave model outputs is presented to solve this problem. First, an efficient traverse algorithm applicable for different types of grids, termed breadth-first search, is employed to track wave events using the continuity of wave parameters. Second, to reduce the impact of the garden sprinkler effect on tracking, tracked wave events are merged if their boundary outlines and wave parameters on these boundaries are both in good agreement. Partitioned wave information from the Integrated Ocean Waves for Geophysical and other Applications dataset is used to test the performance of this spatial tracking approach. The test results indicate that this approach is able to capture the primary features of partitioned wave fields, demonstrating its potential for wave data analysis, model verification, and data assimilation.

Covariate Extreme Value Analysis Using Wave Spectral Partitioning

2020 ◽  
Vol 37 (5) ◽  
pp. 873-888 ◽  
Author(s):  
Jesús Portilla-Yandún ◽  
Edwin Jácome
Keyword(s):  
Extreme Values ◽  
Wind Waves ◽  
Spectral Energy Distribution ◽  
Wave Spectrum ◽  
Wave Climate ◽  
Extreme Value ◽  
Extreme Value Analysis ◽  
Spectral Energy ◽  
Value Analysis ◽  
Spectral Partitioning

AbstractAn important requirement in extreme value analysis (EVA) is for the working variable to be identically distributed. However, this is typically not the case in wind waves, because energy components with different origins belong to separate data populations, with different statistical properties. Although this information is available in the wave spectrum, the working variable in EVA is typically the total significant wave height Hs, a parameter that does not contain information of the spectral energy distribution, and therefore does not fulfill this requirement. To gain insight in this aspect, we develop here a covariate EVA application based on spectral partitioning. We observe that in general the total Hs is inappropriate for EVA, leading to potential over- or underestimation of the projected extremes. This is illustrated with three representative cases under significantly different wave climate conditions. It is shown that the covariate analysis provides a meaningful understanding of the individual behavior of the wave components, in regard to the consequences for projecting extreme values.

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