Article

Phase Resolved Wave Prediction From Synthetic Radar Images

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Abstract

A method is presented for the inversion of images of the sea surface taken by nautical radar into wave elevation that is specifically suitable for the prediction of the wave elevation outside the observation domain covered by the radar. By means of a beamwise analysis of the image obtained by a scanning radar, the image information is translated into wave elevation. Subsequently a 2D FFT is applied in order to obtain the directional wave components required for a linear propagation of the wave field. Assuming knowledge of the significant wave height, a method to obtain the correct scaling of the wave prediction is proposed. The proposed method is verified using synthetic radar images which are modelled by applying shadowing and tilt effect to synthesised short crested linear waves.

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... Also, maritime operation tasks such as cargo and personnel transfer, helicopter landing, high speed navigation, and small craft recovery would benefit from the prediction of workable time windows and/or extreme waves. The short-term (30-90 s) phase-resolved prediction of ocean waves is often referred to as deterministic sea wave prediction (DSWP) and has gathered increasing attention in recent years (e.g., Belmont et al. 2014;Naaijen and Wijaya 2014). The various proposed approaches typically fall under two schema: prediction by mathematical tools that utilize single point measurements, and predictions by models that utilize multipoint measurements at a distance and subsequent reconstruction of the surrounding wave field. ...
... An alternate approach to DSWP uses measurements throughout the wave field to reconstruct the two-dimensional sea and propagate toward a location of forecast interest. In this case, the predict-ahead time is a balance between computation time and wave group (Wu 2004) or phase velocities (Edgar et al. 2000), which can be demonstrated using space-time diagrams (Abusedra and Belmont 2011;Naaijen and Wijaya 2014;Qi et al. 2018b). There are several approaches to this type of DSWP, each of which requires some present knowledge of the wave field. ...
... Since then, notable improvements have been made in computing wave spectra for retrieval of statistical wave parameters (Ziemer and Gunther 1994;Borge et al. 1999;Liu et al. 2015). It is only within recent years that radar has been investigated for phase-resolved wave measurements (Dankert and Rosenthal 2004;Belmont et al. 2014;Naaijen and Wijaya 2014;Wijaya et al. 2015;Qi et al. 2016). ...
Article
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... These deterministic wave models can classically be divided into linear and nonlinear methods. As frequently pointed out (Köllisch et al., 2018;Klein et al., 2020;Law et al., 2020), linear models benefit from a shorter computation time, a critical feature for real-time prediction, and are thus often preferred for operational purposes (Morris et al., 1998;Belmont et al., 2006;Naaijen and Huijsmans, 2008;Naaijen et al., 2009;Abusedra and Belmont, 2011;Kosleck, 2013;Naaijen et al., 2014b). They offer rather satisfying results for moderate sea states and short propagation distances, or for prediction requiring information on quiescent periods only (Belmont et al., 2014). ...
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A number of maritime operations can benefit from a short-term deterministic sea wave prediction (DSWP). Conventional X-band radars have recently been shown to provide a low-cost convenient source of two-dimensional wave profile information for DSWP purposes. However, such rotating radars typically introduce temporal smearing into the data, which introduces errors when traditional Fourier transform–based wave prediction methods are used. The authors report on a new approach for DSWP that avoids such errors. Furthermore, it is not susceptible to the condition number problems that arise with any form of direct or indirect inversion-based approaches. Extensive numerical analyses are conducted to illustrate the effect of the mixed space–time nature of the data on DSWP and the efficiency of the proposed technique to handle it.
... [13] used the 3D-FFT method combined with phase propagation to predict SSE, and found no significant correlation to a vessel mounted MRU. [25] describes a method for predicting SSE using a 2D-FFT. Simulations resulted in correlation values of R = 0.91 for real-time SSE, and R = 0.84 for T+135 second predictions. ...
... [29] used a neural network approach based on SNR, peak wave length, and mean wave period to improve significant wave height estimates. [25] proposes a scaling method using historical RMS ratios of the predicted sea surfaces to MRU measurements. ...
Conference Paper
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