High resolution downscaled ocean waves (DOW) reanalysis in coastal areas |
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| Institution: | 1. CSIRO Oceans and Atmosphere, Hobart, TAS, Australia;2. National Computational Infrastructure, Australian National University, Canberra, ACT, Australia;1. School of Environment, University of Auckland, 23 Symonds Street, Auckland, New Zealand;2. Departamento de Ciencias y Técnicas del Agua y del Medio Ambiente, E.T.S.I de Caminos, Canales y Puertos, Universidad de Cantábria, Avda. de los Castros, s/n, Santander 39005, Spain;1. Department of Ocean and Resources Engineering, University of Hawaii, Honolulu, HI 96822, USA;2. Laboratory of Ocean Physics and Satellite Oceanography, University of Brest, Plouzane, France;3. Department of Atmospheric Sciences, University of Hawaii, Honolulu, HI 96822, USA;4. Hawaii Natural Energy Institute, University of Hawaii, Honolulu, HI 96822, USA |
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| Abstract: | Large-scale wave reanalysis databases (0.1°–1° spatial resolution) provide valuable information for wave climate research and ocean applications which require long-term time series (> 20 years) of hourly sea state parameters. However, coastal studies need a more detailed spatial resolution (50–500 m) including wave transformation processes in shallow waters. This specific problem, called downscaling, is usually solved applying a dynamical approach by means of numerical wave propagation models requiring a high computational time effort. Besides, the use of atmospheric reanalysis and wave generation and propagation numerical models introduce some uncertainties and errors that must be dealt with. In this work, we present a global framework to downscale wave reanalysis to coastal areas, taking into account the correction of open sea significant wave height (directional calibration) and drastically reducing the CPU time effort (about 1000 ×) by using a hybrid methodology which combines numerical models (dynamical downscaling) and mathematical tools (statistical downscaling). The spatial wave variability along the boundaries of the propagation domain and the simultaneous wind fields are taking into account in the numerical propagations to performance similarly to the dynamical downscaling approach. The principal component analysis is applied to the model forcings to reduce the data dimension simplifying the selection of a subset of numerical simulations and the definition of the wave transfer function which incorporates the dependency of the wave spatial variability and the non-uniform wind forcings. The methodology has been tested in a case study on the northern coast of Spain and validated using shallow water buoys, confirming a good reproduction of the hourly time series structure and the different statistical parameters. |
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