Step 1: Select a model
Step 2: Select variables to exportFor an explanation of these variables, please click here
Step 3: Select data pointsTo select a subset of data points, use the polygon or rectangle tool. Making a new selection will replace the previous selection.
0 data points selected.
Step 4: Select a time rangeAdjust the time range here or by dragging the ends of the timeseries export range control at the bottom.
0 days selected.
0 total rows for export. Estimated (worst-case) time to export: about 1 minute
A high resolution 20-year partitioned hindcast for the New Zealand area
João Albuquerque, Jose A. A. Antolínez, Richard Gorman, Fernando J. Méndez, Giovanni Coco
Partitioned WAVEWATCH-III results from a 20-year (1993-2012) global wave model (Rascle and Ardhuin, 2013) were calibrated using a state-of-the-art directional wind-sea and swell wave height correction method based on satellite data (Albuquerque et al., 2018). The corrected partitions were then used to reconstruct the multimodal wave spectra along the boundaries of a SWAN grid that encompasses the whole New Zealand area. Waves were downscaled in non-stationary mode with CFSR wind forcings through 2 levels of nested grids, having both partitioned and integrated parameters stored at a 9Km resolution. Validation carried out against the available buoy data shows a good agreement between instrumental and hindcast data. Wave parameters estimated in areas where the water depth is lower than 30 metres were discarded as they might not represent physically sound values due to depth and grid resolution.
As the latest published hindcast of the New Zealand waters dates back from 2003, the presented database, providing extensible and reliable information of the wave characteristics of New Zealand, will be of great value to a number of future studies, such as beach erosion, coastal inundation, and risk assessment. The partitioned wave data can provide valuable insights on the regional wave climate and its main drivers. The full description of data and methods will be available shortly in Albuquerque et al., 2019 (in prep).
We acknowledge funding from a New Zealand GNS-Hazard Platform grant to Giovanni Coco. We also acknowledge NeSI for providing us the computing power necessary to perform the simulations and the Centre for eResearch for assisting us on the development of this hindcast web-interface.