On limiting the influence of serial correlation in metocean data for prediction of extreme return levels and environmental contours
Not Available
Coe, Ryan G. (ORCID:0000000307383772)↗
SEARCH · Search NASA
Results for “Metocean data”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Lidar Buoy Data Dictionary: For the 2020 – 2021 California Deployments
Pacific Northwest National Laboratory (PNNL) manages two AXYS WindSentinel™ buoys (Buoys #120 and #130) on behalf of the U.S. Department of Energy (DOE) that collect a comprehensive set of meteorological and oceanographic (metocean) data to support resource characterization for wind energy offshore. The buoys have been deployed off the California coast in partnership with the Bureau of Ocean Energy Management (BOEM) from September 2020 through October 2021. One buoy was deployed within the Morro Bay Call Area offshore central California; the other buoy was deployed within the Humboldt Call Area off the coast of northern California. The measurements from the buoys are used to characterize the metocean conditions near potential locations for offshore wind lease areas and are uploaded to DOE’s Data Archive and Portal (DAP). Plots are updated on the DAP webpage to visualize the recent metocean measurements. This document serves as a data dictionary – or reference guide – for understanding and interpreting the data available from the buoys. This document includes: (1) specifications for the buoy instrumentation (Section 2.0) (2) description of each plot and definition of measured parameters (Section 3.0) (3) description of data files and naming convention (Appendix A) (4) reference guide of measurements and variables (Appendix B).
17 WIND ENERGY↗
Gulf of Mexico Risk Analysis Database (GoMRAD)
The Gulf of Mexico Risk Analysis Database is comprehensive Esri geodatabase of vector layers, raster layers, and tables curated for risk analysis within the offshore Gulf of Mexico. Datasets include bathymetry, seafloor characteristics (channels, anomalies, faults, etc.), MetOcean data (wind speed, wave height, etc.), ocean current data, sediment data, and machine learning training regions used in NETL's Ocean & Geohazard Analysis (OGA) tool. This database serves as a compliment to the OGA tool by providing many of the datasets used in the design of the OGA tool, including regions used for machine learning. This database also serves as a valuable resource for risk analysis studies within the offshore Gulf of Mexico. This work was completed under the Advanced Offshore Research Portfolio, FWP Number: 1022476.
BOEM,Bathymetry,Gulf Of Mexico,Machine Learning,Me↗
Comprehensive GOM Federal Waters Platform, Incident, Metocean, and Geohazard Dataset
The dataset contains integrated data from an array of disparate data sources, all spatially and temporally linked to platforms in the federal waters of the Gulf of Mexico (platform data from BSEE, 2020). Integrated data includes past reported incidents dating back to 1956 (BSEE, BOEM, MMS), metocean data (see Nelson et al. in review for source information), and geohazard data (see Nelson et al. in review for source information). Proprietary well production information was redacted from this dataset, but was used in resulting analytics.
Full System↗
Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer
Abstract. With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air–sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
17 WIND ENERGY↗
Reference Site Condition Datasets for Floating Wind Arrays in the United States
Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. Further information on the datasets and how they were created can be found in: Biglu, M., M. Hall, E. Lozon, S. Housner. 2024. Reference Site Conditions for Floating Wind Arrays in the United States. Golden, CO: National Renewable Energy Laboratory (NREL). NREL/TP-5000-89897. The data are also available at: https://github.com/FloatingArrayDesign/SiteConditions The content of each dataset is as follows: _NOW23_wind.txt: Hourly NOW-23 wind data up to a height of 400 meter. _metocean_1hr.txt: Hourly time series including wind, wave, surface current and temperature data. _Summary.xlsx: Metocean data, including extreme values, joint probability distributions and monthly statistics. _usSEABED_soil.csv: Extract of the usSEABED database for this specific site. _bathymetry_200m.txt (and 500m, 1000m): Gridded seabed depth data.
16 TIDAL AND WAVE POWER↗
Metocean Reference Station Best Practices: MORS-1 Case Study
The goal of a Metocean Reference Station should be to aid both near-term development of the offshore wind energy industry as well as long-term climate and energy research. Capitalizing on existing shared-use facilities wherever possible, reference stations should provide data valued by industry users as well as research users in a cost-effective way. Cost-effectiveness is a critical component of developing a reference site, as the real value of the site's data collection efforts is the length of the time series it is able to sustain. This report seeks to lay out the best practices toward developing and maintaining metocean reference stations in the United States. The best practices described here focus on suitable platforms, sensor integration, and long-term operations of the reference station itself, as best practices of operations for individual sensors for the research community or validation enterprises focused on industrial use of metocean data are well described in the literature. This work focuses on potential stations in the United States because the market for reference data and validation facilities is less well defined in the United States, given the young age of the rapidly emerging offshore wind energy industry here.
17 WIND ENERGY↗
Reference Site Conditions for Floating Wind Arrays in the United States
Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. In addition to providing curated reference site condition datasets for four U.S. areas, our assessment highlights the need for more publicly available metocean and soil condition data.
17 WIND ENERGY↗
Infrastructure and Metocean Technology
<strong>Research Problem: </strong>Changes in the ocean environment (i.e., mudslides or burial from subsea currents, strong weather events or natural fluctuations) have been linked to <strong>billions of dollars of impacts</strong>. These events can have a significant effect on the <strong>success and longevity </strong>of offshore infrastructure, as well as affect <strong>safety and cost </strong>during exploration and production activities.<strong>Research Approach: (1) </strong>Determine current state of knowledge regarding hazardous metocean and bathymetric conditions, and data availability regarding these conditions and historic events. (2) EY19 -Evaluate if Ml/AI model can be developed to better identify current hazardous metocean and bathymetric conditions. (3) EY 20 + -develop, train, and test ML/AI model to identify current conditions and forecast changes and vulnerability that may impact offshore infrastructure and operations. <strong>Benefit: </strong>Improved characterization of seabed related hazards in the offshore environment will help to manage and minimize costs and risks during drilling and production operations, and help prevent catastrophic incidents.
Duran, Rodrigo↗
High-fidelity wave data from MarineLabs CoastScout™ buoys supporting WEC demonstration at Scripps Pier
Two MarineLabs CoastScout™ buoys were deployed off La Jolla, California in February 2020 to provide high-resolution wave elevation time series data in support of CalWave Power Technologies’ XWave WEC deployment. The buoys are positioned approximately 300m from a longterm wave monitoring station operated by the Coastal Data Information Program. Both CoastScout hull types showed excellent agreement with the reference Datawell Directional Waverider MkIII (Hs r 2 =0.94, m0 r 2 =0.88). The CoastScout buoys maintained 100% uptime throughout an initial 6- month intercomparison period. CalWave used 5-Hz wave train data from this period to drive physical hardware-in-the-loop tests of the XWave power take-off. Throughout the deployment, the CoastScout buoys will provide near-real-time bulk wave parameters to help CalWave operators safely and efficiently operate the WEC. In post-processing, high-fidelity wave resolution will be used to associate individual waves with onboard measurements from Fiber Bragg Grating strain gauges and external pressure sensors.
16 TIDAL AND WAVE POWER↗
Integrating Applied Energy and BER Smart Data Capabilities to Develop a DOE Data Fabric for Energy-Water R&D
Focal Area(s): 1) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Science Challenge: DOE R&D, including DOE’s Basic Energy Research (BER)’s Environmental Systems Science Division (EESSD) program and DOE’s applied energy research (AER) programs (EERE, FE, and NE) are producers and consumers of Earth systems datasets. This white paper focuses on the first topic area from the call in relation to how crosscutting resources and innovations from DOE’s EESSD and AER can be brought to bear to mutual benefit and more efficient energy-water, Earth system data resources through improved. The overarching challenge posed by this call focuses on how DOE can directly leverage artificial intelligence (AI) to engineer a substantial (paradigm-changing) improvement in Earth System Predictability? While stemming from DOE BER’s EESSD program, this is a challenge that is faced and also being addressed by DOE’s AER programs. Over the past decade plus, FE, EERE, and NE programs have made important strides towards addressing this need. These strides are in many ways highly complementary to EESSD’s MODEX efforts. Energy water systems spanning metocean to groundwater to surface water systems all are data driven whether for basic energy or applied energy. These are remote, multi-variate, complex natural, and in many cases engineered, systems. Key needs and challenges of both EESSD and AER include developing data-focused tools to enhance data search and discovery to fill in knowledge gaps (address sparse data challenge), and rapidly transform datasets, including disparate and multi-source data. Leveraging DOE on-premise computing (HPC, exascale) infrastructure supports the computing-intensive algorithms required to execute these data acquisition and transformation processes to derive enriched knowledge and data, driving AI/ML and big data analytics for these systems. The opportunity lies in combining BER and AER efforts to provide a more robust, advanced, efficient and complete computing data fabric to address energy-water data acquisition and assimilation needs which currently pose significant impediments to AI/ML predictions and research.
54 ENVIRONMENTAL SCIENCES↗
Ocean & Geohazard Analysis Tool
The Ocean & Geohazard Analysis (OGA) software tool is designed to summarize insights into key offshore hazards drawing from a diverse set of approaches, including artificial intelligence, machine learning, probabilistic and statistical, and offshore data sources. The offshore hazards that can be analyzed include submarine landslides, extreme wind/wave/current event probabilities, earthquakes, and metocean pathways (CIAM Climatological Isolation and Attraction Model–Climatological Lagrangian Coherent Structures - Submissions - EDX (doe.gov)). Currently, the tool is developed for use in the Gulf of Mexico. The data underlying the offshore hazard analyses can be found here: https://edx.netl.doe.gov/dataset/gulf-of-mexico-risk-analysis-database-gomrad This work was conducted under the Advanced Offshore Research Portfolio, FWP Number 1022409 at National Energy Technology Laboratory, U.S. Dept. of Energy. Disclaimer This project was funded by the United States Department of Energy, National Energy Technology Laboratory, in part, through a site support contract. Neither the United States Government nor any agency thereof, nor any of their employees, nor the support contractor, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
AIML↗
Development of a Metocean Reference Site near the Massachusetts and Rhode Island Wind Energy Areas
This project developed the first long-term U.S.-based offshore MetOcean Reference Site (MORS-1) by capitalizing on a unique combination of one of the few existing publicly available offshore wind energy metocean observational campaigns in the United States and the only existing research-grade offshore fixed tower. Data collected at MORS-1 has facilitated improved wind resource assessments, improved short-term power production estimates, and reduced costs for sensor validation and calibration efforts, which translate into reduced overall wind energy project risk and cost for developers. Now operational, MORS-1 serves the needs of both industry and researchers using a nonprofit, joint industry-academic partnership model. Led by the Woods Hole Oceanographic Institution, the MORS-1 development effort focused on creating both a recognized organizational structure that will ensure support of the MORS-1 by the wider wind energy industry and research community, and a highly validated data collection and sensor validation facility that will serve as the premier location for cost- and uncertainty-reducing resource characterization and research efforts.
17 WIND ENERGY↗
Reducing Uncertainty in Offshore Wind Energy Yield Estimates via a Metocean Reference Site
The offshore wind industry is burgeoning in the coastal waters of the United States, specifically along the Atlantic. For wind energy to be successful, reliable observations and model simulations are needed for resource assessment and forecasting. While many of these activities have already begun, there is currently an absence of observations at hub-height in these waters, with the closest available hub-height measurements usually taken onshore. Deployment of floating lidars has occurred through various federally funded projects, but only encapsulates time periods of a couple of years at best. Private industry is also beginning to leverage floating lidars, but this data is often proprietary, and not shared with the general public. In this work, we make the case for a metocean reference site for long-term offshore wind energy. Specifically, we quantify the impact of having a metocean reference site compared to other methods of determining hub-height winds and energy production. We use an offshore floating lidar to directly measure the wind resource, and compare these measurements to predictions derived from other widely-available surface meteorological variables. These prediction methods (vertical extrapolation, machine learning, and NWP output) produce a variety of vertical wind speed profiles, of which produce different energy yield estimates for a reference offshore turbine (Figure 1). While some methods perform reasonably well against the lidar, the uncertainty in these energy yield estimates has financial implications, further illustrating the need for long-term measurements in coastal waters.
machine learning↗
U.S. Offshore Pipeline and Reported Incident Datasets
The U.S. Offshore Pipeline and Reported Incident Datasets provide a compilation of data from a variety of credible resources, spatially-temporally integrated into multivariate resources. This spatial resource includes more than 80,000 points along existing and abandoned pipelines in the Gulf with matched incidents based on similar lease blocks and temporal timelines (e.g., the incident date occurs within reported pipeline lifespan), structural characteristics, geologic and seafloor data, and meteorological, oceanographic, and biochemical statistics spatially and temporally matched to each point. This is provided as both a feature class in a file geodatabase, as well as a CSV file for ease of use. The pipeline incidents table is a CSV file containing more than 900 reported incidents from 1986 to 2021, including incident date, area (Outer Continental Shelf (OCS) lease block and area code), reported causes, reported incident information, and results (i.e., cost, repairs, inspections), along with quantitative severity metrics. Field dictionaries are included for both the pipeline locations and incidents datasets, which detail field definitions. The pipeline locations field dictionary includes original resource reference information.
Advanced Infrastructure Integrity Model↗
IEA Wind Task 49: Reference Site Conditions for Floating Wind Arrays
The commercial-scale deployment of floating offshore wind (FOW) projects is expected to take place in a diverse range of sites that may differ significantly from existing fixed-bottom projects. FOW farms are particularly sensitive to the water depth and the meteorological and oceanographic (metocean) and geotechnical conditions at the project site due to the wave-induced system motions and loads as well as the anchoring system constraints imposed by the seafloor conditions. Uncertainty around the site conditions will permeate through all aspects of project design, leading to suboptimal and overly conservative designs, increased costs, and adversely affected performance. As FOW expands into a global industry, metocean and geotechnical conditions will increasingly vary for projects located in different geographic regions or in far-from-shore, deep-water sites. This study represents the outputs of work package 1 of International Energy Agency Wind Task 49, which focuses on the integrated design of floating wind arrays. The primary goal of this study is to establish the type of parameters and constraints required to characterize FOW array reference sites; provide a realistic and publicly available set of reference site conditions to the FOW community as a baseline set of data for individual research projects; identify and categorize any critical gaps in the existing data or methodologies required to define reference site characteristics; and inform and support the design of reference FOW arrays. A building block concept was developed for synthesizing reference sites for the design of FOW arrays. The building blocks include three classes of site conditions focusing on the techno-economic design of FOW projects: metocean conditions, seabed conditions, and coastal infrastructure. All reference site data produced and collected in this study are publicly available.
17 WIND ENERGY↗
Modeling and observations of North Atlantic cyclones: Implications for U.S. Offshore wind energy
To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power by 2030 and 110 GW by 2050, expansion of wind energy into U.S. territorial waters prone to tropical cyclones (TCs) and extratropical cyclones (ETCs) is essential. This requires a deeper understanding of cyclone-related risks and the development of robust, resilient offshore wind energy systems. Here, this paper provides a comprehensive review of state-of-the-science measurement and modeling capabilities for studying TCs and ETCs, and their impacts across various spatial and temporal scales. We explore measurement capabilities for environments influenced by TCs and ETCs, including near-surface and vertical profiles of critical variables that characterize these cyclones. The capabilities and limitations of Earth system and mesoscale models are assessed for their effectiveness in capturing atmosphere–ocean–wave interactions that influence TC/ETC-induced risks under a changing climate. Additionally, we discuss microscale modeling capabilities designed to bridge scale gaps from the weather scale (a few kilometers) to the turbine scale (dozens to a few meters). We also review machine learning (ML)-based, data-driven models for simulating TC/ETC events at both weather and wind turbine scales. Special attention is given to extreme metocean conditions like extreme wind gusts, rapid wind direction changes, and high waves, which pose threats to offshore wind energy infrastructure. Finally, the paper outlines the research challenges and future directions needed to enhance the resilience and design of next-generation offshore wind turbines against extreme weather conditions.
17 WIND ENERGY↗
Dashboard for Marine Energy Site Assessment and Monitoring
The marine energy (ME) industry presently relies upon fragmented site assessment solutions that require high resource expenditure for deployment at each site and do not leverage the wealth of readily available tools and information. A wave energy resource assessment dashboard, currently in development, will substantially improve siting, permitting, operations, and maintenance of ME projects by providing an integrated solution that is a one-stop-shop for a developer’s needs. The Site Energy Assessment and MOnitoring Dashboard (SEAMOD) will be of commercial interest to anyone seeking to deploy an ME project and is easily expandable to include tidal and wind energy site assessments. The integrated dashboard is being developed using state-of-the-art database and cloud computing methods and data-assimilative modeling tools that can be coupled with low-cost, rapidly deployable wave buoys and environmental sensing hardware. The combined software and hardware dashboard will reduce wave energy site characterization and wave climate monitoring costs by more than 60 percent and provide assessments that meet international industry standards. To realize a thriving global ME industry, the physical environment at a potential deployment site must be understood, not only for resource characterization, but also for optimization of device and power conversion performance. SEAMOD directly addresses these needs with a commercially marketable product. SEAMOD is a low-cost solution that provides comprehensive ME resource assessments, baseline environmental monitoring, and offshore characterizations required for successful ME development. The key technical objectives for Phase I were a series of software development goals, which when implemented with monitoring solutions, produced an initial proof-of-concept low-cost wave energy resources dashboard. In Phase II, the development of the prototype SEAMOD continued. The basic framework employed was the development of a revised dashboard and monitoring tool customized for ME applications by focusing on IEC site assessment and method requirements. Development was focused on the integration of full hindcast metocean products to provide hindcast resource characterization and environmental information. The final integrated dashboard provides a low-cost solution that delivers comprehensive, scalable, industry-standard energy resource assessments and offshore characterizations required for successful ME development. The integrated dashboard offers visibility of the most recent site modeling, measurements, and historical data. The application and integration of consensus-based standards for wave energy resource assessment, as determined by the International Electrotechnical Commission (IEC), are crucial for the impact and value of SEAMOD. SEAMOD includes monthly, seasonal, and yearly statistics, as well as the total 30-year record, offering temporal resolution of the IEC parameters to aid potential developers in determining the available wave energy resources in their area of interest.
16 TIDAL AND WAVE POWER↗