Deterministic Sea Waves Prediction Using Mixed Space–Time Wave Radar Data

M. Al-Ani University of Exeter, Exeter, United Kingdom

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J. Christmas University of Exeter, Exeter, United Kingdom

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M. R. Belmont University of Exeter, Exeter, United Kingdom

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J. M. Duncan Defence Equipment and Support, Ministry of Defence, Bristol, United Kingdom

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J. Duncan Defence Equipment and Support, Ministry of Defence, Bristol, United Kingdom

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B. Ferrier Dynamic Interface Laboratory, Hoffman Engineering Corporation, Stamford, Connecticut

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Abstract

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.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: M. Al-Ani, m.t.a.al-ani@exeter.ac.uk

Abstract

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.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: M. Al-Ani, m.t.a.al-ani@exeter.ac.uk
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