A non-parametric, data-driven approach to despiking ocean surface wave time series

We propose a methodology for despiking ocean surface wave time series based on a Bayesian approach to data-driven learning known as Gaussian Process (GP) regression. We show that GP regression can be used for both robust detection of erroneous measurements and interpolation over missing values, while also obtaining a measure of the uncertainty associated with these operations. In comparison with a recent dynamical phase space-based despiking method, our data-driven approach is here shown to lead to improved wave signal correlation and spectral tail consistency, although at a significant increase in computational cost. Our results suggest that GP regression is thus especially suited for offline quality control requiring robust noise detection and replacement, where the subsequent analysis of the despiked data is sensitive to the accidental removal of extreme or rare events such as abnormal or rogue waves. We assess our methodology on measurements from an array of four co-located 5-Hz laser altimeters during a much-studied storm event the North Sea covering a wide range of sea states.

Simulation and Modelling for a Three-Dimensional Ocean Surface Wave using and Inverse Fourier Transform

Ocean surface waves have been utilized as fundamental information in various fields of oceanic research. In this paper, we suggest a simulation and modelling technique for generating an ocean surface wave using an inverse Fast Fourier Transform (iFFT), and we subsequently verify the wave’s accuracy. The conventional method, linear superposition, requires recursive calculation because of the double summation and the time variable; to circumvent this issue, the new algorithm is presented. The Joint North Sea Wave Project (JONSWAP) spectrum is utilized for the ocean surface wave simulation example, and the parameters are the significant wave height (HS) and the zero-crossing wave period (TZ). A coordinate transform for the wavenumber domain was used to apply the inverse FFT algorithm. To verify the accuracy of the simulation result, the relative error between the input condition and the analysis result was calculated. The result for TZ is below 4% relative error, and the maximum relative error for HS is 7%. To avoid the Nyquist frequency for wave-field analysis and simulation, the minimum grid size was calculated by twice applying the maximum wavenumber.

Sea ice fragmentation and its role in the evolution of the Arctic sea ice cover.

<p>The Arctic sea ice cover is not a continuous expanse of ice but is instead composed of individual sea ice floes. These floes can range in size from just a few metres to tens of kilometres. Floe size can influence a variety of processes, including lateral melt rates, momentum transfer within the sea ice-ocean-atmosphere system, surface moisture flux, and sea ice rheology. Sea ice models have traditionally defined floe size using a single parameter, if floe size is explicitly treated at all. There have been several recent efforts to incorporate models of the Floe Size Distribution (FSD) into sea ice models in order to explore both how the shape of the FSD emerges and evolves and its impact on the sea ice cover, including the seasonal retreat. Existing models have generally focused on ocean surface wave-floe interactions and thermodynamic melting and growth processes. However, in-situ observations have indicated the presence of mechanisms other than wave fracture involved in the fragmentation of floes, including brittle failure and melt-induced break up.</p><p>In this study we consider two alternative FSD models within the CICE sea ice model: the first assumes the FSD follows a power law with a fixed exponent, with parameterisations of individual processes characterised using a variable FSD tracer; the second uses a prognostic approach, with the shape of the FSD an emergent characteristic of the model rather than imposed. We firstly use case studies to understand how similarities and differences in the impacts of the two FSD models on the sea ice emerge, including the different spatial and temporal variability of these impacts. We also consider whether the inclusion of FSD processes in sea ice models can enhance seasonal predictability. We will also demonstrate the need to include in-plane brittle fracture processes in FSD models and discuss the requirements needed within any parameterisation of the brittle failure mechanism.</p>

Microwave Specular Measurements and Ocean Surface Wave Properties

Microwave reflectometers provide spectrally integrated information of ocean surface waves several times longer than the incident electromagnetic (EM) wavelengths. For high wind condition, it is necessary to consider the modification of relative permittivity by air in foam and whitecaps produced by wave breaking. This paper describes the application of these considerations to microwave specular returns from the ocean surface. Measurements from Ku and Ka band altimeters and L band reflectometers are used for illustration. The modeling yields a straightforward integration of a closed-form expression connecting the observed specular normalized radar cross section (NRCS) to the surface wave statistical and geometric properties. It remains a challenge to acquire sufficient number of high-wind collocated and simultaneous reference measurements for algorithm development or validation and verification effort. Solutions from accurate forward computation can supplement the sparse high wind databases. Modeled specular NRCSs are provided for L, C, X, Ku, and Ka bands with wind speeds up to 99 m/s.

Sea Echoes for Airborne HF/VHF Radar: Mathematical Model and Simulation

Currently, shore-based HF radars are widely used for coastal observations, and airborne radars are utilized for monitoring the ocean with a relatively large coverage offshore. In order to take the advantage of airborne radars, the theoretical mechanism of airborne HF/VHF radar for ocean surface observation has been studied in this paper. First, we describe the ocean surface wave height with the linear and nonlinear parts in a reasonable mathematical form and adopt the small perturbation method (SPM) to compute the HF/VHF radio scattered field induced by the sea surface. Second, the normalized radar cross section (NRCS) of the ocean surface is derived by tackling the field scattered from the random sea as a stochastic process. Third, the NRCS is simulated using the SPM under different sea states, at various radar operating frequencies and incident angles, and then the influences of these factors on radar sea echoes are investigated. At last, a comparison of NRCS using the SPM and the generalized function method (GFM) is done and analyzed. The mathematical model links the sea echoes and the ocean wave height spectrum, and it also offers a theoretical basis for designing a potential airborne HF/VHF radar for ocean surface remote sensing.

Ocean surface wave dynamics, energy and momentum air-sea transfer under a variety of wind and waves conditions

<p>Direct measurements have been conducted from a spar buoys deployed in the Gulf of Mexico, and in the vicinity of Todos Santos Island, offshore Ensenada BC, Mexico, in order to better understand ocean surface wave modulated processes under a variety of oceanographic and meteorological conditions. Full ocean surface wave directional spectrum is estimated from sea surface elevation data acquired with an array of capacitance wires, to represent directional spectrum as a function of frequency and direction, as well as a function of the wave number components Kx and Ky. Momentum transfer between ocean and the atmosphere is calculated directly through the eddy correlation method applied to wind velocity components acquired with a sonic anemometer. Momentum transfer variability is analysed to study its dependance on the surface wave conditions, with special emphasis on mixed sea states. Comparison between single peak spectra results with those cases where bi-modal spectra were present are performed in order to detect wind stress variability effects. Ocean-atmosphere transfer of momentum is studied and explained in terms of the shape and evolution of the surface wave spectrum. This research is funded by SENER-CONACYT 249795 and 201441 projects.</p>