Water Power Technologies Office Wave Hindcast Dataset
This database, produced by NREL and other national lab researchers for the U.S. Department of Energy's Water Power Technologies Office (WPTO), provides wave data spanning 1979–2020.
The WPTO Wave Hindcast Dataset, which feeds into two NREL water power tools and can be accessed directly online or downloaded, contains the highest-resolution time-series data on wave attributes in U.S. waters. Using the UnSWAN and WaveWatch III models, a team from NREL, in collaboration with researchers at Pacific Northwest National Laboratory and Sandia National Laboratories, generated a hindcast of wave data that spans 1979–2020. The U.S. Department of Energy and WPTO supported the production and dissemination of this data.
The dataset is published in two major forms:
- The spatial dataset—an unstructured grid of data with a spatial resolution as fine
as 100 to 200 meters in shallow water and a 3-hour time step. There are 15 million
grid points in this dataset. Variables in this dataset, defined in the International Electrotechnical Commission Technical Commission 114
wave resource assessment technical specification, are:
- Significant wave height
- Energy period
- Omnidirectional wave power
- Maximum energy direction
- Directionality coefficient
- Spectral width
- Mean absolute period
- Mean wave direction
- Peak period
- Water depth.
- The "spectral" or "virtual buoy" dataset—a much smaller dataset with fewer points (219) and a higher (1-hour) temporal resolution that also contains the directional wave spectrum. These points were selected to co-locate with active National Oceanic and Atmospheric Administration buoys, with several other points added to provide more comprehensive spatial coverage. All of the variables above are included in this data, plus the directional wave spectrum.
Technology developers can use these data to design the next generation of robust and efficient marine energy devices. Project developers can identify optimal project sites, and the public can understand how much marine energy might be available locally or at a national level. The data is also potentially valuable to other sectors (e.g., offshore wind energy, marine transportation) that have interest in a high-resolution history of the nation’s wave conditions.
The Wave Hindcast Webinar, hosted by Pacific Northwest National Laboratory, discusses the high-resolution regional hindcast datasets for wave energy resource characterization in U.S. coastal waters. View the Wave Hindcast webinar recording and the Wave Hindcast webinar slides.
Using the Dataset
The full dataset is several hundred terabytes (TB) organized into a collection of .h5 files. Files range from 2.5 to 455.2 gigabytes (GB). Therefore, to support the efficient dissemination of such a large dataset, the team partnered with Amazon Web Services (AWS) to host the data. This makes the full dataset publicly available but also provides infrastructure for users to access specific portions of the data in a variety of ways.
Each of the methods to access the data (detailed in the following sections) have unique advantages and disadvantages.
|Data Access Method||Advantages||Disadvantages|
NREL's Marine Energy Atlas
|This is the most user-friendly method, it’s easy to subset data of interest, and users can visualize data before download with in-app processing tools.||
Users cannot download full dataset for all variables and time steps.
NREL’s Marine and Hydrokinetic Toolkit
|This method is user-friendly for Python and MATLAB users. The Wave Module contains tools to download and analyze the WPTO Wave Hindcast Dataset and allows users to access complimentary data for streamlined analytical and processing workflows.||
Python users will find that some functionality only works in Jupyter-notebooks. MATLAB users will need the MATLAB code shell to run Python functions, which requires MATLAB 2019b and the installation of Python components.
Download from AWS
|All of the data is available in this method.||
Downloading this large of a dataset requires significant data storage volume.
Access within AWS
|This method provides access to all of the data without downloading.||
Operating AWS computational resources requires money and expertise.
WPTO Wave Hindcast Dataset is the default data available on the Marine Energy Atlas, an interactive mapping tool to explore potential for marine energy resources.
The atlas allows users to:
- Visualize or preview data before downloading
- Subset data easily to an area of interest or a particular year for download
- Download data for entire regions or multiple years using the large-scale data option on the Data Downloader
- Leverage in-app processing through the Capacity Factor Tool: estimate the capacity factor (the ratio of time-averaged power generation to the maximum power generation), using wave height from the WPTO Wave Hindcast Dataset and user-uploaded power matrices of wave energy converters.
To spatially subset data for download from the Marine Energy Atlas to your local machine, use the Query Tools. The Query Tools allows one to select data for discrete locations or larger areas. To retrieve information on a small, discrete area, one can input coordinates or click a point on the map. To obtain data on larger areas, one can use the region query, custom shape query, or the radius query and select an area of interest by double clicking and dragging the polygon on the map. For the queries where users delineate spatial extent on the map, once a polygon is set, the area of the polygon and the coordinates of the polygon corners will appear. Similarly, for data within a selected radius, the area of the circle, the radius of the circle, and the coordinate of the center will appear. Users will then have the option to Request Query Data or download the data. Selecting “Request Query Data” generates a table in the window to browse the data. Clicking on the download button will take the user to the Data Downloader with most of the fields pre-filled.
Proceeding directly to the Data Downloader allows users to select multiple attributes. The data from the WPTO Wave Hindcast Dataset are only accessible through the “Large-Scale Data” download type. Users select the wave model of choice and then again spatially subset the data using the query tools. Once the area of interest has been selected, the number of sites within the polygon pops up. If no sites are found within the selected polygon, one should expand the polygon to capture a larger area. In the next step on the attributes page, users can select which wave data variables from the hindcast they would like to download, select a particular year in the dataset, and easily change the format of the data from UTC to local time. However, users are limited by the amount of data they can download in one instance, which is indicated by the download limit bar on the bottom of the page. Through the Marine Energy Atlas, users are only able to download data for a few years for one variable, or multiple variables for one year. We encourage users to use one of the other data download methods for downloading more data or the complete dataset.
You can also watch an NREL-hosted webinar New Functionality and WPTO Wave Hindcast Data in the Marine Energy Atlas, which provides an overview on the Marine Energy Atlas, how to access the data, and how to use the in-app processing tools.
The Marine and Hydrokinetic Toolkit (MHKiT) is a massive, searchable, open-source knowledge hub that provides marine energy developers with the code needed to analyze how well their technology might perform in various ocean and river sites. MHKiT is divided into various modules depending on the resource type and enables simple access to several marine data sources. Among these is the Wave Module, which includes tools to access the WPTO Wave Hindcast Dataset.
In addition to the WPTO Wave Hindcast Dataset, the Wave Module includes convenience functions to access the data and tools to calculate quantities of interest and visualize data for wave energy converters. MHKiT software enables simple access to the several marine data sources to compliment the WPTO Wave Hindcast Dataset and can be used to create or be incorporated into processing workflows.
A detailed overview of how to access data from the WPTO Wave Hindcast Dataset within MHKiT can be found on the software’s documentation webpage; API documentation for the WPTO Wave Hindcast Dataset in Python and for MATLAB. For access, use the example code for data through Python and the example code using MATLAB.
The data can also be accessed or downloaded using tools within the AWS ecosystem.
Download the Entire Dataset
If you have downloaded the AWS command line interface, the WPTO Wave Hindcast Dataset can easily be accessed through the AWS registry of open data. By copying a line of code into the terminal on your local machine, you can download the entire dataset. The data, arranged by year, currently occupy approximately 100 TB and are expected to grow to 200 TB.
Download a Subset of the Dataset
Users that want to download data for certain years from a particular region can use the web browser interface from the registry of open data. On the right panel, by clicking Browse Dataset under Explore and through Object v1.0.0, users can choose the region of interest and then download the files from their browser. These files contain all variables from the hindcast, and each file is between 81.9 GB and 455.2 GB for the spatial data and 2.5 GB and 3.6 GB for the virtual buoy data.
Users can also select data of interest through the user-friendly application programming interface through NREL’s Developer Network. However, this is highly rate-limited due to the complexity of the data. Users are limited by the amount of requests they can make within a 24-hour period.
Accessing the Data Within the AWS Ecosystem
Users can also access the data directly within the AWS ecosystem by either setting up a highly scalable data service (also known as HSDS) server or by reading the .h5 files directly. This approach has the advantage of not needing to download the data, but it does require require owning and managing your own AWS computational resources.
Citing the Data
The following indicates which publication should be cited for each region of the dataset. If you use more than one region, please cite each source.
Ahn, Seongho, V.S. Neary, M.N. Allahdadi, and R. He. 2021. “Nearshore Wave Energy Resource Characterization Along the East Coast of the United States.” Renewable Energy 172 (July 2021): 1212–1224. https://doi.org/10.1016/j.renene.2021.03.037.
Ahn, Seongho, V.S. Neary, M.N. Allahdadi, and R. He. 2022. “A Framework for Feasibility-Level Validation of High-Resolution Wave Hindcast Models.” Ocean Engineering 263 (1 November 2022): 112193. https://doi.org/10.1016/j.oceaneng.2022.112193.
Wu, Wei-Cheng, T. Wang, Z. Yang, and G. García-Medina. 2020. "Development and Validation of a High-Resolution Regional Wave Hindcast Model for US West Coast Wave Resource Characterization." Renewable Energy 152 (June 2020): 736–753. https://doi.org/10.1016/j.renene.2020.01.077.
García-Medina, Gabriel, Yang, Z., Wu, W.-C., and Wang T. 2021. “Wave Resource Characterization at Regional and Nearshore Scales for the U.S. Alaska Coast Based on a 32-Year High-Resolution Hindcast.” Renewable Energy 170 (June 2021): 595–612. https://doi.org/10.1016/j.renene.2021.02.005.
Li, Ning, G. García-Medina, K. Fai Cheung, and Z. Yang. 2021. “Wave Energy Resources Assessment for the Multi-Modal Sea State of Hawaii.” Renewable Energy 174 (August 2021): 1036–1055. https://doi.org/10.1016/j.renene.2021.03.116.
García Medina, Gabriel, Zhaoqing Yang, Ning Li, Kwok Fai Cheung, and Ellinor Luto-McMoore. 2022. “Wave Climate and Energy Resources in American Samoa From a 42-Year High-Resolution Hindcast.” Renewable Energy.
García Medina, Gabriel, Z. Yang, N. Li, K. Fai Cheung, H. Wang, and F.M. Ticona Rollano. 2021. High-Resolution Regional Wave Hindcast for U.S. Pacific Island Territories. Richland, WA: Pacific Northwest National Laboratory. PNNL-31208. https://doi.org/10.2172/1824210.
A Development and Calibration of a High-Resolution Model for the Gulf of Mexico, Puerto Rico, and the US Virgin Islands: Implication for Wave Energy Resource Characterization, Ocean Engineering (2021)
Characteristics and Variability of the Nearshore Wave Resource on the U.S. West Coast, Energy (2020)
High-Resolution Hindcasts for U.S. Wave Energy Resource Characterization, International Marine Energy Journal (2020)
Development and Validation of a Regional-Scale High-Resolution Unstructured Model for Wave Energy Resource Characterization Along the US East Coast, Renewable Energy (2019)
Predicting Ocean Waves Along the US East Coast During Energetic Winter Storms: Sensitivity to Whitecapping Parameterizations, Ocean Science (2019)
Please contact Katie Peterson with questions.