The development of this dataset was funded by the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, Water Power Technologies Office to improve our understanding of the U.S. wave energy resource and to provide critical information for wave energy project development and wave energy converter design.
This is the highest resolution publicly available long-term wave hindcast dataset that – when complete – will cover the entire U.S. Exclusive Economic Zone (EEZ). As such, the dataset could also be of value to any company with marine operations inside the U.S. EEZ. Specifically, the data can be used to investigate the historical record of wave statistics at any U.S. site. This level of detail could be of interest to the Oil and Gas industry for offshore platform engineering, to the offshore wind industry for turbine and array design, to offshore aquaculture production and blue economy development, to coastal communities for extreme hazards mitigation, to global shipping companies and fisherman for a better understanding of weather windows and seasonal wave climate patterns at a spatial resolution that does not exist elsewhere.
A technical summary of the dataset is as follows:
- 32 Year Wave Hindcast (1979-2010), 3-hour temporal resolution
- Unstructured grid spatial resolution ranges from 200 meters in shallow water to ~10 km in deep water (700,000 grid points in West Coast dataset)
- Current spatial coverage: EEZ offshore of U.S. West Coast (other regions coming soon, see below)
The following variables are included in the dataset:
- Mean Wave Direction: Direction normal to the wave crests
- Significant Wave Height: Calculated as the zeroth spectral moment (i.e., H_m0)
- Mean Absolute Period: Calculated as a ratio of spectral moments (m_0/m_1)
- Peak Period: The period associated with the maximum value of the wave energy spectrum
- Mean Zero-Crossing Period: Calculated as a ratio of spectral moments (sqrt(m_0/m_2))
- Energy Period: Calculated as a ratio of spectral moments (m_-1/m_0)
- Directionality Coefficient: Fraction of total wave energy travelling in the direction of maximum wave power
- Maximum Energy Direction: The direction from which the most wave energy is travelling
- Omni-Directional Wave Power: Total wave energy flux from all directions
- Spectral Width: Spectral width characterizes the relative spreading of energy in the wave spectrum
Currently the dataset only covers the EEZ offshore of the U.S. West Coast, but it will be updated to include all other U.S. regions by 2022. The timeline for extending the dataset is as follows:
- West Coast United States: Dataset Available
- East Coast United States: TBD
- Alaskan Coast: October 2020
- Hawaiian Islands: October 2020
- Gulf of Mexico, Puerto Rico, and U.S. Virgin Islands: TBD
- U.S. Pacific Island Territories: Dec 2021
The multi-scale, unstructured-grid modeling approach using WaveWatch III and SWAN enabled long-term (decades) high-resolution hindcasts in a large regional domain. In particular, the dataset was generated from the unstructured-grid SWAN model output that was driven by a WaveWatch III model with global-regional nested grids. The unstructured-grid (UnSWAN) model simulations were performed with a spatial resolution as fine as 200 meters in shallow waters. The dataset has a 3-hour timestep spanning 32 years from 1979 through 2010. The project team intends to extend this to 2020 (i.e., 1979-2020), pending DOE support to do so.
The model was extensively validated not only for the most common wave parameters, but also six IEC resource parameters and 2D spectra with high quality spectral data derived from publicly available buoys. Additional details on detailed definitions of the variables found in the dataset, the SWAN and WaveWatch III model configuration and model validation are available in a peer-review publication Development and validation of a high-resolution regional wave hindcast model for U.S. West Coast wave resource characterization and a PNNL technical report: High-Resolution Regional Wave Hindcast for the U.S.West Coast. This study was funded by the U.S. Department of Energy, Office of Energy Efficiency & Renewable Energy, Water Power Technologies Office under Contract DE-AC05-76RL01830 to Pacific Northwest National Laboratory (PNNL).
High Resolution Ocean Surface Wave Hindcast data is made available as a series of hourly .h5 located on AWS S3:
s3://WPTO-pds-US-wave/v1.0.0
The US wave data is also available via HSDS at /nrel/US_Wave For examples on setting up and using HSDS please see our examples repository
The data is provided in high density data file (.h5) separated by year. The
variables mentioned above are provided in 2 dimensional time-series arrays with
dimensions (time x location). The temporal axis is defined by the time_index
dataset, while the positional axis is defined by the coordinate dataset. The
units for the variable data is also provided as an attribute (units). The
SWAN and IEC valiable names are also provide under the attributes
(SWAWN_name) and (IEC_name) respectively.
Example scripts to extract wind resource data using python are provided below:
The easiest way to access and extract data from the Resource eXtraction tool
rex
from rex import ResourceX
wave_file = '/nrel/US_Wave/US_wave_2010.h5'
with ResourceX(wave_file, hsds=True) as f:
meta = f.meta
time_index = f.time_index
swh = f['significant_wave_height']rex also allows easy extraction of the nearest site to a desired (lat, lon)
location:
from rex import ResourceX
wave_file = '/nrel/US_Wave/US_wave_2010.h5'
lat_lon = (34.399408, -119.841181)
with ResourceX(wave_file, hsds=True) as f:
lat_lon_swh = f.get_lat_lon_df('significant_wave_height', nwtc)or to extract all sites in a given region:
from rex import ResourceX
wave_file = '/nrel/US_Wave/US_wave_2010.h5'
jurisdication='California'
with ResourceX(wave_file, hsds=True) as f:
ca_swh = f.get_region_df('significant_wave_height', jurisdiction,
region_col='jurisdiction')If you would rather access the US Wave data directly using h5pyd:
# Extract the average wave height
import h5pyd
import pandas as pd
# Open .h5 file
with h5pyd.File('/nrel/US_Wave/US_wave_2010.h5', mode='r') as f:
# Extract meta data and convert from records array to DataFrame
meta = pd.DataFrame(f['meta'][...])
# Significant Wave Height
swh = f['significant_wave_height']
# Extract scale factor
scale_factor = swh.attrs['scale_factor']
# Extract, average, and unscale wave height
mean_swh = swh[...].mean(axis=0) / scale_factor
# Add mean wave height to meta data
meta['Average Wave Height'] = mean_swh# Extract time-series data for a single site
import h5pyd
import pandas as pd
# Open .h5 file
with h5pyd.File('/nrel/US_Wave/US_wave_2010.h5', mode='r') as f:
# Extract time_index and convert to datetime
# NOTE: time_index is saved as byte-strings and must be decoded
time_index = pd.to_datetime(f['time_index'][...].astype(str))
# Initialize DataFrame to store time-series data
time_series = pd.DataFrame(index=time_index)
# Extract wave height, direction, and period
for var in ['significant_wave_height', 'mean_wave_direction',
'mean_absolute_period']:
# Get dataset
ds = f[var]
# Extract scale factor
scale_factor = ds.attrs['scale_factor']
# Extract site 100 and add to DataFrame
time_series[var] = ds[:, 100] / scale_factorPlease cite the most relavent publication below when referencing this dataset:
- Wu, Wei-Cheng, et al. "Development and validation of a high-resolution regional wave hindcast model for US West Coast wave resource characterization." Renewable Energy 152 (2020): 736-753.
- Yang, Zhaoqing, et al. High-Resolution Regional Wave Hindcast for the US West Coast. No. PNNL-28107. Pacific Northwest National Lab.(PNNL), Richland, WA (United States), 2018.
The National Renewable Energy Laboratory (“NREL”) is operated for the U.S. Department of Energy (“DOE”) by the Alliance for Sustainable Energy, LLC ("Alliance"). Pacific Northwest National Laboratory (PNNL) is managed and operated by Battelle Memorial Institute ("Battelle") for DOE. As such the following rules apply:
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