A method for computing calibrated ocean wave spectra from measurements with a nautical X-band radar

Abstract

The radar backscatter from the ocean surface, called sea clutter, is modulated due t o the surface wave field. The modulation introduces a spatio-temporal correlation of the sea clutter signal. A three-dimensional wavenumber frequency spectrum of the sea clutter is calculated from a time series of radar images with an FFT algorithm. Because of the non-linearity of the imaging process the image spectrum contains harmonics in addition t o the linear fundamental mode. These modes are localized at distinct surfaces in the wave number frequency space corresponding t o the scaled dispersion relation of surface gravity waves. Because of the localization of the spectral energy on dispersion shells, the aliasing effect due t o a temporal undersampling can be overcome. The dispersion shells are reconstructed over the Nyquist frequency barrier and for negative frequencies. The dispersion shells are used as spectral filters t o separate the modes from the clutter noise pedestal of radar image spectra. This pedestal shows the effect of the impulse response due t o the finite spatial resolution of the radar. The spectral energy of the wavenumber frequency spectrum is integrated over the frequency coordinate axis, with the positive solution of the dispersion relation of surface gravity waves as signal filter. With this spectral filter method an unambiguous wave number image spectrum is selected.An empirical calibration procedure is presented. This procedure is based on the correlation of the signal t o noise ratio with the significant wave height. The applicability of the method is shown using a radar and buoy data set. The spectral energies of fundamental mode, first harmonic, and the clutter component are compared. It was proved that the spectral energy of the clutter noise exceeds the spectral energy of the first harmonic mode by an order of magnitude, i.e. the physical background of the empirical calibration method is similar t o the method well established for wave spectra from single SAR images.The fundamental image spectrum is related t o the surface wave spectrum by an image transfer function.

Estimate of significant wave height from non-coherent marine radar images by multilayer perceptrons

“…Therefore, there is an overestimation of H s using Equation (9). It is known that, at grazing incidence, as marine radars work, shadowing is one of the most important modulation mechanisms of the radar imagery [7,21]. In addition, the wave slopes affect the backscatter mechanisms because they change the local angle of incidence of the electromagnetic field [16].…”

“…These components concern to the wave field, as well as other additional contributions, as depicted in Figure 4. These additional contributions are due to the radar imagery mechanisms [7], such as the background noise, BGN, caused by the sea surface roughness generated by the local wind [9,19,20], higher harmonics of the dispersion relation (see Equation (2)) due to non-linear radar imagery mechanisms at grazing incidence [19,21], etc. Hence, an inversion modeling technique is applied to the image spectrum I(k, ω) to estimate the wave spectrum F(k, ω) [7].…”

“…Figure 4 Example of a 2D image spectrum I(k, ω), and its different spectral components [7,9]. A detailed description of these components can be found in [19,21]. obtained by the inversion modeling technique and the noise of the total spectral energy of the BGN components [9].…”

Phase-Resolved Wave Field Simulation Calibration of Sea Surface Reconstruction Using Noncoherent Marine Radar

“…Among the modulation mechanisms, except possibly for the near range, where the tilt modulation may play an appreciable role, the shadowing modulation has the dominant impact on radar imaging, especially for grazing incidence and horizontal polarization (NietoBorge et al 2004;Lee et al 1995;Seemann et al 1997). By comparing the filtered radar spectrum and the in situ-measured buoy spectrum for select sea states, and using numerical simulation of shadowing (and tilt) modulation effects for select sea states, NietoBorge et al (2004) derived an empirical form M(k) 5 k 2q for the correction function, with a constant parameter q based on the mean value for the sea states they considered.…”

“…The main benefit of this approach is that in situ measurements and external calibration are not needed for scaling wave amplitude. However, for typical radars, much of the image domain is in the range where the incidence is grazing and shadowing modulation is more important than tilt modulation (Nieto-Borge et al 2004;Lee et al 1995;Seemann et al 1997). In this case, the existing established method is based on Nieto-Borge et al (2004).…”