Wave spectra partitioning and long term statistical distribution

2015 ◽  
Vol 96 ◽  
pp. 148-160 ◽  
Author(s):  
Jesús Portilla-Yandún ◽  
Luigi Cavaleri ◽  
Gerbrant Ph. Van Vledder
2016 ◽  
Vol 43 (24) ◽  
pp. 12,348-12,355 ◽  
Author(s):  
X.-J. Zhang ◽  
W. Li ◽  
R. M. Thorne ◽  
V. Angelopoulos ◽  
J. Bortnik ◽  
...  

2019 ◽  
Vol 49 (2) ◽  
pp. 543-559 ◽  
Author(s):  
Haoyu Jiang ◽  
Lin Mu

AbstractWind-generated waves can propagate over large distances. Therefore, wave spectra from a fixed point can record information about air–sea interactions in distant areas. In this study, the spectral wave climate for a point in the tropical eastern Pacific Ocean is computed. Several well-defined wave climate systems are observed in the mean wave spectrum. Significant seasonal cycling, long-term trends, and correlations with the Southern Oscillation, the Arctic Oscillation, and the Antarctic Oscillation are observed in the local wave spectra, showing abundant climatic information. Projections of wind vectors on the directions pointing to the target location are used to connect the spectral wave climate and basin-scale wind climate, because significant correlations are observed between the wave spectra and the wind projections of both local and remote wind systems. The origins of all the identified wave climate systems, including the westerlies and the trade winds in both hemispheres, are clearly shown in wind projection maps. Some of these origins are thousands of kilometers away from the target point, demonstrating the validity of this connection. Comparisons are made between wave spectra and the corresponding local and remote wind fields with respect to seasonal and interannual variability and long-term trends. The results show that each frequency and direction of ocean wave spectra at a certain location can be approximately linked to the wind field for a geographical area, implying that it is feasible to reconstruct spectral wave climates from observational wind field data and monitor wind climates from observational wave spectra geographically far away.


2018 ◽  
Vol 844 ◽  
pp. 766-795 ◽  
Author(s):  
Sergei Y. Annenkov ◽  
Victor I. Shrira

Kinetic equations are widely used in many branches of science to describe the evolution of random wave spectra. To examine the validity of these equations, we study numerically the long-term evolution of water wave spectra without wind input using three different models. The first model is the classical kinetic (Hasselmann) equation (KE). The second model is the generalised kinetic equation (gKE), derived employing the same statistical closure as the KE but without the assumption of quasistationarity. The third model, which we refer to as the DNS-ZE, is a direct numerical simulation algorithm based on the Zakharov integrodifferential equation, which plays the role of the primitive equation for a weakly nonlinear wave field. It does not employ any statistical assumptions. We perform a comparison of the spectral evolution of the same initial distributions without forcing, with/without a statistical closure and with/without the quasistationarity assumption. For the initial conditions, we choose two narrow-banded spectra with the same frequency distribution and different degrees of directionality. The short-term evolution ($O(10^{2})$ wave periods) of both spectra has been previously thoroughly studied experimentally and numerically using a variety of approaches. Our DNS-ZE results are validated both with existing short-term DNS by other methods and with available laboratory observations of higher-order moment (kurtosis) evolution. All three models demonstrate very close evolution of integral characteristics of the spectra, approaching with time the theoretical asymptotes of the self-similar stage of evolution. Both kinetic equations give almost identical spectral evolution, unless the spectrum is initially too narrow in angle. However, there are major differences between the DNS-ZE and gKE/KE predictions. First, the rate of angular broadening of initially narrow angular distributions is much larger for the gKE and KE than for the DNS-ZE, although the angular width does appear to tend to the same universal value at large times. Second, the shapes of the frequency spectra differ substantially (even when the nonlinearity is decreased), the DNS-ZE spectra being wider than the KE/gKE ones and having much lower spectral peaks. Third, the maximal rates of change of the spectra obtained with the DNS-ZE scale as the fourth power of nonlinearity, which corresponds to the dynamical time scale of evolution, rather than the sixth power of nonlinearity typical of the kinetic time scale exhibited by the KE. The gKE predictions fall in between. While the long-term DNS show excellent agreement with the KE predictions for integral characteristics of evolving wave spectra, the striking systematic discrepancies for a number of specific spectral characteristics call for revision of the fundamentals of the wave kinetic description.


2021 ◽  
Vol 66 (1) ◽  
pp. 19-33
Author(s):  
Malwina Kozek ◽  

The study presents the assessment of spatial distribution, including vertical variability of selected features of springs. For this aim, the data obtained during three hydrological mapping sessions in the upper Bystrzyca Dusznicka river catchment in the period of 1995–2018 was used. Thanks to the long-term observation series, comparative spatial analyzes of statistical distribution of spring’s selected features were possible. Furthermore, the variability of temperature, reaction and conductivity of rivers and springs were estimated, assessing these parameters along the tributaries course of the Bystrzyca Dusznicka river. Results for years of various precipitation conditions were compared. Presented analyzes allowed to identify and valorize the factors determining the variability and changes in springs characteristics during dry and moist years.


Author(s):  
K. A. Belibassakis ◽  
Ch. N. Stefanakos ◽  
Y. G. Georgiou

In the present work a weakly nonlinear wave model originally developed by Rebaudengo Lando` et al (1996) is applied to the transformation of wave spectra from offshore to nearshore, and subsequently, it has been systematically applied to the derivation of long-term time series of spectral wave parameters on decreasing depth from corresponding offshore wave data. The derived long-term series of nearshore parameters have been used as input to a new method, recently developed by Stefanakos & Athanassoulis (2006), for calculating return periods of various level values from nonstationary time series data. The latter method is based on a new definition of the return period, that uses the MEan Number of Upcrossings of the level x* (MENU method), and it has been shown to lead to predictions that are more realistic than traditional methods. To examine the effects of bottom topography on the nearshore extreme value predictions, Roseau (1976) bottom profiles have been used for which analytical expressions are available concerning the reflection and transmission coefficients. A parametric (JONSWAP) model is used to synthesize offshore spectra from integrated parameters, which are then linearly transformed based on the previous transmission coefficient to derive first-order nearshore wave spectra. Second-order random sea states have been simulated by following the approach of Hudspeth & Chen (1979) (see also Langley 1987, Lando et al 1996), exploiting the quadratic transfer functions on decreasing depth to calculate the second-order nearshore spectra. Finally, wave parameters are extracted from the nearshore spectra by calculating the first few moments.


2020 ◽  
Vol 33 (8) ◽  
pp. 3381-3393
Author(s):  
Haoyu Jiang

AbstractLong-term wave spectral statistics can provide a better description of wave climate than integrated wave parameters because several wave climate systems (WCSs) generated by different wind climate systems can coexist at the same location. In this study, global wave climate patterns are presented by spatially tracking point-wise long-term wave spectra (probability density distributions of wave spectral partitions) from a WAVEWATCH III hindcast, providing new insights into global wave climate. Tens of well-defined WCSs, which are generated in different source regions by different wind systems, including prevailing westerlies, polar easterlies, trade winds, and monsoons, were identified. These WCSs are independent of each other because wave systems from different origins travel independently. The spatial distributions of these WCSs can illustrate the entire life cycle of ocean waves, from being generated as dominant wind-seas to becoming less dominant swells in far fields, from a climatic point of view. The mean wave directions in WCS patterns, especially those in westerlies-generated WCSs, are generally in agreement with the great circles on Earth’s surface, which display the propagation routes of ocean swells.


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