Wave Data

GOW has been generated from the spectral model WaveWatch III to obtain homogeneous, continuous and long records of wave climate. The different versions of GOW encompasses results that have been obtained forcing the wave model with different atmospheric reanalysis, the wave parameters are provided at hourly temporal resolution, covering temporal coverage of more than 40 years. All our GOW datasets have global coverage and the spatial resolution are between 5 and 25 km depending on the zone, an example is shown in figure left

An exhaustive validation procedure have been applied to each version of the GOW hindcast using instrumental information. For this particular issue, we have compared wave model results with measurements from deep-water buoys at different locations and altimeter data.

After of the validation procedure we can conclude that GOW provides high accuracy information on wave climate worldwide (figure right). The example for the selected GOW version shows high correlation values, generally above 0.9 in the tropics and extra tropics, the Bias error can be considered to be negligible (less than 5 cm) in large areas over the ocean.

ROW datasets is focused on improve GOW dataset quality and provides information in zones where the spatial resolution of GOW datasets is not enough to take account the changes due to a complex bathymetry or coast line and the applied wind forcings have to be extracted from atmospheric hindcast to take account local wind seas. ROW methodology use SWAN (Simulation Waves Nearshore; Booij et al., 1999) wave mode to generate the regional wave data and the wave boundaries are from GOW.

SWAN model is configured and calibrated with the forcings to provide the best wave data in the zone. The model accounts for the following physics: wave generation by wind, wave propagation in time and space, shoaling, refraction, three- and four-wave interactions, whitecapping, bottom friction, depth-induced breaking, wave-induced set-up, wave generation and propagation at regional scales, transmission through and reflection against obstacles.

ROW datasets are available in different zones around the world, although the methodology could be applied in your project zone to generate a ROW dataset that covers all your requirements, we will advise on the most suitable methodology or wave database. An example of ROW wave outcomes, quality and domain is showed in figure 3.3. for the north coast of Spain.

DOW is a historical reconstruction of coastal waves. In order to obtain wave data in shallow waters and due to the scarcity of coastal observation measurements, ocean wave reanalysis databases ought to be downscaled to increase the spatial resolution and simulate the wave transformation process. Due to the computational cost of hindcasting, DOW  methodology is based on a hybrid downscaling combining a numerical wave model (dynamical downscaling) with mathematical tools (statistical downscaling).

The procedure developed to build the DOW products is described in scientific articles and can be summarized as follows:  a calibrated wave inputs dataset is used to select a representative subset of sea states in deep water areas. The subset guarantees that all possible sea states are represented even capturing extreme events. The selected sea states are propagated using a wave propagation model with high spatial resolution over a detailed bathymetry (i.e. figure 3.4). The time series of the propagated sea state parameters at each location are reconstructed using a non-linear interpolation technique.

The ability to reproduce hourly time series of coastal waves has been validated by comparing DOW and buoy records. The results confirm that the proposed methodology is able to reproduce the time series of wave parameters at coastal areas, even for energy estimation (see figure 3.5.).

DOW is available for the Spanish, Brazilian and European coast with a spatial resolution between 1km and 100

m depending on the zone. The DOW methodology could be applied to develop a downscaled wave dataset focused in your project area and provide all the required parameters for your purposes.

Camus, P., Mendez, F., Medina, R. (2011a). A hybrid efficient method to downscale wave climate to coastal areas. Coastal Engineering 58, pp 851-861.

Camus, P., Mendez, F., Medina, R., Cofiño, A. (2011b). Analysis of clustering and selection algorithms for the study of multivariate wave climate. Coastal Engineering 58, pp 453-452.

Camus, P., Mendez, F., Medina, R., Tomas A., Izaguirre C. (2013). High resolution downscaled ocean waves (DOW) reanalysis in coastal areas. Coastal Engineering, 72, pp 56–68.

Mínguez, R., Espejo, A., Tomás, A., Méndez, F. J., and Losada, I. J. (2011). Directional calibration of wave reanalysis databases using instrumental data. J. Atmos. Oceanic Technol. 28, pp 1466-1485.

Perez, J., Menendez, M., Camus, P., Mendez, F. J., & Losada, I. J. (2015). Statistical multi-model climate projections of surface ocean waves in Europe. Ocean Modelling, 96, 161–170.

Perez, J., Menendez, M., & Losada, I. J. (2017). GOW2: A global wave hindcast for coastal applications. Coastal Engineering, 124(March), 1–11.

Reguero, B.G., Méndez, F.J., Losada, I.J. (2013). Variability of multivariate wave climate in Latin America and the Caribbean. Global and Planetary Change 100 (2013) 70–84.

Reguero, B.G., Menéndez, M., Méndez, F.J., Mínguez, R., Losada, I.J. (2012a). A global Ocean Wave (GOW) calibrated reanalysis from 1948 onwards. Coastal Engineering, 65, pp 38–55.