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). The data can be used to investigate the historical record of wave statistics at any U.S. site. As such, the dataset could also be of value to any entity with marine operations inside the U.S. EEZ.
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
- Spatial coverage: EEZ offshore of all U.S territories (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
The following U.S. regions will be added to this dataset under the given
domain names
- West Coast United States:
West_Coast - East Coast United States:
Atlantic - Alaskan Coast: TBD
- Hawaiian Islands:
Hawaii - Gulf of Mexico, Puerto Rico, and U.S. Virgin Islands: TBD
- U.S. Pacific Island Territories: TBD
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 SWAN 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 models were 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 definitions of the variables found in the dataset, the SWAN and WaveWatch III model configurations and model validation are available in technical report and peer-reviewed publications (Wu et al. 2020, Yang et al. 2020, Yang et al. 2018). 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 3 hourly .h5 files located on AWS S3 for the domains discussed above:
s3://wpto-pds-US_wave/v1.0.0/${domain}
Hourly virtual bouy data is also available in hourly .h5 files on AWS S3:
s3://wpto-pds-US_wave/v1.0.0/virtual_buoy/${domain}
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
To use rex with HSDS you will need
to install h5pyd:
pip install h5pyd
Next you'll need to configure HSDS:
hsconfigure
and enter at the prompt:
hs_endpoint = https://developer.nrel.gov/api/hsds
hs_username =
hs_password =
hs_api_key = 3K3JQbjZmWctY0xmIfSYvYgtIcM3CN0cb1Y2w9bf
IMPORTANT: The example API key here is for demonstation and is rate-limited per IP. To get your own API key, visit https://developer.nrel.gov/signup/
You can also add the above contents to a configuration file at ~/.hscfg
from rex import ResourceX
wave_file = '/nrel/US_wave/West_Coast/West_Coast_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/West_Coast/West_Coast_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/West_Coast/West_Coast_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/West_Coast/West_Coast_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/West_Coast/West_Coast_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 relevant 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, Z., G. García-Medina, W. Wu, and T. Wang, 2020. Characteristics and variability of the Nearshore Wave Resource on the U.S. West Coast. Energy.
- 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.
- Ahn, S. V.S. Neary, Allahdadi, N. and R. He, Nearshore wave energy resource characterization along the East Coast of the United States, Renewable Energy, 2021, 172
- Yang, Z. and V.S. Neary, High-resolution hindcasts for U.S. wave energy resource characterization. International Marine Energy Journal, 2020, 3, 65-71
- Allahdadi, M.N., He, R., and Neary, V.S.: Predicting ocean waves along the US East Coast during energetic winter storms: sensitivity to whitecapping parameterizations, Ocean Sci., 2019, 15, 691-715
- Allahdadi, M.N., Gunawan, J. Lai, R. He, V.S. Neary, Development and validation of a regional-scale high-resolution unstructured model for wave energy resource characterization along the US East Coast, Renewable Energy, 2019, 136, 500-511
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:
This data arose from worked performed under funding provided by the United States Government. Access to or use of this data ("Data") denotes consent with the fact that this data is provided "AS IS," “WHEREIS” AND SPECIFICALLY FREE FROM ANY EXPRESS OR IMPLIED WARRANTY OF ANY KIND, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES SUCH AS MERCHANTABILITY AND/OR FITNESS FOR ANY PARTICULAR PURPOSE. Furthermore, NEITHER THE UNITED STATES GOVERNMENT NOR ANY OF ITS ASSOCITED ENTITES OR CONTRACTORS INCLUDING BUT NOT LIMITED TO THE DOE/PNNL/NREL/BATTELLE/ALLIANCE ASSUME ANY LEGAL LIABILITY OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR USEFULNESS OF THE DATA, OR REPRESENT THAT ITS USE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS. NO ENDORSEMENT OF THE DATA OR ANY REPRESENTATIONS MADE IN CONNECTION WITH THE DATA IS PROVIDED. IN NO EVENT SHALL ANY PARTY BE LIABLE FOR ANY DAMAGES, INCLUDING BUT NOT LIMITED TO SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES ARISING FROM THE PROVISION OF THIS DATA; TO THE EXTENT PERMITTED BY LAW USER AGREES TO INDEMNIFY DOE/PNNL/NREL/BATTELLE/ALLIANCE AND ITS SUBSIDIARIES, AFFILIATES, OFFICERS, AGENTS, AND EMPLOYEES AGAINST ANY CLAIM OR DEMAND RELATED TO USER'S USE OF THE DATA, INCLUDING ANY REASONABLE ATTORNEYS FEES INCURRED.
The user is granted the right, without any fee or cost, to use or copy the Data, provided that this entire notice appears in all copies of the Data. In the event that user engages in any scientific or technical publication utilizing this data user agrees to credit DOE/PNNL/NREL/BATTELLE/ALLIANCE in any such publication consistent with respective professional practice.