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DOE OSTI2026

Performance Evaluation of an Offshore Wave Measurement Buoy in Monochromatic Waves

The accurate measurement of waves underpins marine energy resource characterization, device design, and project development. Datawell wave buoys are widely deployed and have long served as a trusted standard for wave measurements. We quantify the measurement performance, including wave elevation and energy flux estimation, of a Datawell DWR-MkIII buoy using prescribed monochromatic heave motions on a large-amplitude six-degree-of-freedom motion platform at the National Laboratory of the Rockies, assuming the buoy behaves as an ideal wave follower. Commanded motions were validated with an optical motion tracking system while buoy elevation and raw acceleration were recorded. Wave elevations were propagated to wave energy flux estimation using four methods, including one frequency-domain method and three time-domain methods. The Bayesian optimization was applied for design of experiments, and records from three test sites were also applied and evaluated in the present study. Results show two error regions within the nominal period range of 1.6 s to 30 s. For wave periods between 5 s and 25 s, the buoy provides accurate wave height measurements. For short periods less than 5 s, the 1.28 Hz sampling frequency induces sub-Nyquist artifacts that bias elevation and can drive maximum energy flux estimation errors above 100%. For long periods exceeding 25 s, the buoy reported elevation is underpredicted with error depending on period but relatively independent of wave height, with maximum wave height and wave energy flux errors reaching 64% and 87%, respectively. Furthermore, analysis of three field-derived cases shows that frequency-domain estimates at 1.28 Hz agree within 2% of the corresponding 100 Hz estimates, while larger method-dependent differences are observed for the Hilbert method.

16 TIDAL AND WAVE POWER↗

NASA NTRS2000

Comparison Between Sea Surface Wind Speed Estimates From Reflected GPS Signals and Buoy Measurements

Reflected signals from the Global Positioning System (GPS) have been collected from an aircraft at approximately 3.7 km altitude on 5 different days. Estimation of surface wind speed by matching the shape of the reflected signal correlation function against analytical models was demonstrated. Wind speed obtained from this method agreed with that recorded from buoys to with a bias of less than 0.1 m/s, and with a standard derivation of 1.3 meters per second.

Garrison, James L.↗

NASA NTRS1985

Theory and application of calibration techniques for an NDBC directional wave measurements buoy

The National Data Buoy Center (NDBC) of the National Oceanic and Atmospheric Administration (NOAA) deployed a 10-m-diameter discus-type hull in the Pacific Ocean some 185 km southwest of Los Angeles, CA, in April 1984. Aboard this hull was an electronic system capable of acquiring, processing, and transmitting to shore directional wave measurements. For this system to produce accurate data, a number of factors had to be taken into account. These factors included noise, amplitude and phase alterations due to mechanical and electrical components, and magnetic fields arising from the hull. Comprehensive calibration and verification techniques were developed and applied to ensure data quality. The system configuration is described with emphasis on the methods used in the data processing to correct for the various factors. Examples of the resulting corrected data are given.

Steele, K. E.↗

DOE OSTI2024

Characterizing Interference in a Wave-Powered Buoy

Oceanographic measurement buoys are power constrained by the capacity of their batteries and the available solar power. Many buoys are in locations with abundant wave energy, but wave energy converters are not being used to power them. One reason wave energy converters may not have not been developed for wave measurement buoys is that many buoys measure waves by precisely following the wave motion. Wave energy converters may inhibit this wave-following ability by altering a buoy's response to incoming waves. In this report, we present the results of field tests that measured the extent to which buoy motion was changed by modifications to the buoy for wave energy harvesting. The motion of the buoy was significantly modified by the addition of the hardware components necessary for harvesting wave energy. The ratio of the mass of the wave energy converter to the mass of the buoy was high for the system tested, which strongly affected buoy motion. A lower value for this ratio would decrease the effect of the wave energy converter on the motion of the buoy, thereby decreasing the impact on the buoy's ability to measure waves but also decreasing the power production capabilities. Therefore, the use of wave energy converters to power wave measurement buoys may be feasible for large buoys that measure low-frequency waves and have low power requirements.

16 TIDAL AND WAVE POWER↗

DOE OSTI2023

Sea ice heat and mass balance measurements from four autonomous buoys during the MOSAiC drift campaign

As part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), four autonomous seasonal ice mass balance buoys were deployed in first- and second-year ice. These buoys measured position, barometric pressure, snow depth, ice thickness, ice growth, surface melt, bottom melt, and vertical profiles of temperature from the air, through the snow and ice, and into the upper ocean. Observed air temperatures were similar at all four sites; however, snow–ice interface temperatures varied by as much as 10°C, primarily due to differences in snow depth. Observed winter ice growth rates (November to May) were <1 cm day -1 , with summer melt rates (June to July) as large as 5 cm day -1 . Air temperatures changed as much as 2°C hour -1 but were dampened to <0.3°C hour -1 at the snow–ice interface. Initial October ice thicknesses ranged from 0.3 m in first-year ice to 1.2 m in second-year ice. By February, this range was only 1.20–1.46 m, due in part to differences in the onset of basal freezing. In second-year ice, this delay was due to large brine-filled voids in the ice; propagating the cold front through this ice required freezing the brine. Mass balance results were similar to those measured by autonomous buoys deployed at the North Pole from 2000 to 2013. Winter average estimates of the ocean heat flux ranged from 0 to 3 W m -2 , with a large increase in June 2020 as the floe moved into warmer water. Estimates of average snow thermal conductivity measured at two buoys during periods of linear temperature profiles were 0.41 and 0.42 W m -1 °C -1 , higher than previously published estimates. Results from these ice mass balance buoys can contribute to efforts to close the MOSAiC heat budget.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1981

A global atlas of GEOS-3 significant waveheight data and comparison of the data with national buoy data

The accuracy of the GEOS-3 significant waveheight estimates compared with buoy measurements of significant waveheight were determined. A global atlas of the GEOS-3 significant waveheight estimates gathered is presented. The GEOS-3 significant waveheight estimation algorithm is derived by analyzing the return waveform characteristics of the altimeter. Convergence considerations are examined, the rationale for a smoothing technique is presented and the convergence characteristics of the smoothed estimate are discussed. The GEOS-3 data are selected for comparison with buoy measurements. The GEOS-3 significant waveheight estimates are assembled in the form of a global atlas of contour maps. Both high and low sea state contour maps are presented, and the data are displayed both by seasons and for the entire duration of the GEOS-3 mission.

Mcmillan, J. D.↗

NASA NTRSDate not supplied

Assessing Uncertainties and Variability 34 Years of Surface Radiative Fluxes Using GEWEX SRB Release4-IP

The NASA/GEWEX Surface Radiation Budget (SRB) project produces 3-hrly shortwave and longwave surface and top of atmosphere radiative fluxes for the 1983-near present time period. The new Release 4 Integrated Product (IP) uses the newly recalibrated and processed ISCCP HXS product as its primary input for cloud and radiance data, replacing ISCCP DX with a ninefold increase in pixel count (10 km instead of 30 km). This first version retains a 1°x1° resolution enabling intercomparison against previous versions and other data sets such as CERES, but spans 34 years from July 1983 through June 2017 and was announced by Kummerow et al. 2019 (GEWEX News). This new IP product also uses an atmospheric temperature and moisture dataset known as nnHIRS and other parameters such as near surface and skin temperatures from SeaFlux and LandFlux data sets. In addition to the input data improvements, several important algorithm improvements have been made since Release 3. These include recalculated SW atmospheric transmissivities and reflectivities, updated ocean and snow/ice albedos, and variable total solar irradiance consistent with SORCE measurements. The LW code has been updated to improve the optical property treatment for clouds and aerosols are included in this version. Radiative treatment of ice clouds is also improved in the LW. The variable aerosol optical properties for the SW and LW are specified using a detailed aerosol history from the Max Planck Institute Aerosol Climatology (MAC). Here we present an assessment of the LW radiative fluxes and the uncertainty of those fluxes relative to the various inputs to surface SW/LW flux measurements from BSRN and PMEL buoys measurements. We review the validation of the SW and LW fluxes and then in terms of time series and then assess the products in terms of their long-term variability of the surface SW and LW net fluxes compared to multiple other data products including atmospheric reanalysis products. The comparisons of radiative estimates to observations are performed at various temporal scales and aimed at investigation of agreement at longer time averages but accessing potential change in diurnal magnitude and daily variability. Utilizing this uncertainty information, to access long-term variability of surface radiation components at selected region and global scales, considering satellite sampling/calibration “artifacts” as necessary. At the longer time scales, the net SW and net LW the TOA and surface have implications toward closure of the energy budgets at the surface, we assess these compared to other studies on energy budget closure for the same selected global and regional scales.

Paul W Stackhouse↗

NASA NTRS1996

Equivalent Neutral Wind

The definition of equivalent neutral wind and the rationale for using it as the geophysical product of a spaceborne scatterometer are reviewed. The differences between equivalent neutral wind and actual wind, which are caused by atmospheric density stratification, are demonstrated with measurements at selected locations. A method of computing this parameter from ship and buoy measurements is described and some common fallacies in accounting for the effects of atmospheric stratification on wind shear are discussed. The computer code for the model to derive equivalent neutral wind is provided.

Liu, W. Timothy↗

NASA NTRS1982

Water Quality Monitor

An automated water quality monitoring system was developed by Langley Research Center to meet a need of the Environmental Protection Agency (EPA). Designed for unattended operation in water depths up to 100 feet, the system consists of a subsurface buoy anchored in the water, a surface control unit (SCU) and a hydrophone link for acoustic communication between buoy and SCU. Primary functional unit is the subsurface buoy. It incorporates 16 cells for water sampling, plus sensors for eight water quality measurements. Buoy contains all the electronic equipment needed for collecting and storing sensor data, including a microcomputer and a memory unit. Power for the electronics is supplied by a rechargeable nickel cadmium battery that is designed to operate for about two weeks. Through hydrophone link the subsurface buoy reports its data to the SCU, which relays it to land stations. Link allows two-way communications. If system encounters a problem, it automatically shuts down and sends alert signal. Sequence of commands sent via hydrophone link causes buoy to release from anchor and float to the surface for recovery.

Source record↗

DOE OSTI2021

Wave Measurements Using Multi-Frame Processing of Marine Radar Data

Marine radars have proven to be useful for measuring ocean waves, but the accuracy of the measurements is limited by several factors including the look-angle dependence of the radar signals as well as noise in the radar data. The look-angle dependence introduces a systematic error or bias in the measurements, and noise causes a random error. This paper describes a method of combining data from multiple radar frames that is optimal in the sense of minimizing the error for a set of biased measurements with random additive noise. The results are shown experimentally to increase the correlation of the radar estimates with buoy measurements.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Wave Prediction using X-Band Radar (Final Summary Report)

Model Predictive Control (MPC) is the best controls framework available to wave energy today. It allows for the maximization of energy yield from a WEC system while respecting various constraints and losses in the system. This type of constrained optimization is a critical capability for the techno-economic optimization of WEC devices and is difficult to do effectively with causal controls frameworks. MPC requires a future prediction of wave excitation forces, requiring wave prediction on a future time horizon of up to 30 seconds. The future horizon required is highly dependent on the WEC and PTO type available. To our knowledge no-one has been able to implement MPC on a WEC device at sea, due to the fact that phase-resolved wave-prediction is not a capability that has been sufficiently developed to date. The key objectives of this project are focused on developing “industry-ready” wave prediction technology building blocks that can be applied to a wide range of different wave energy conversion topologies. Our collaborative work with device developers has demonstrated that the level of improvement attainable for any particular device is heavily dependent on the device and PTO topology chosen. The range of annual average power capture improvements is on the order of 10% to over 100%. However, the important aspect is that we can get within about 10% of theoretical upper limits even when considering errors in the wave prediction and more importantly, we can optimize performance while respecting various constraints such as motion amplitude or peak structural loads. The combination of improved performance while limiting structural loads has a net effect of reducing the levelized cost of energy from these emerging technologies and allows us to design control laws that meet an economic optimum. Efforts under a previous project on controls has focused on developing a set of wave-prediction algorithms that work by leveraging a network of wave sensing measurement buoys that are equipped with real-time telemetry. The present work is focusing primarily on using X-band radar to measure the wave-field around the WEC to predict the future excitation forces on the structure and combining the measurements of these different sources to create improvements in the wave-prediction accuracy.

16 TIDAL AND WAVE POWER↗

DOE OSTI2021

Critical Interference Mechanisms for Marine Renewable Energy-powered Ocean Observing Platforms

Marine renewable energy devices, such as wave energy converters or tidal turbines, not only have the potential to provide clean, reliable power to the grid but may also offer new opportunities for ocean exploration by providing power for oceanographic sensors where other sources cannot. However, an important consideration in making this vision a reality is understanding whether the marine energy devices may interfere with oceanographic measurements. Interference may occur through several pathways, including sound produced by the device and the motion of the device itself. In this report, we provide an overview of interference pathways and their implications before exploring two use cases for marine energy-powered ocean observing platforms: a tidal-powered passive acoustic monitoring system and wave-powered wave measurement buoys. For each use case, we review the relevant literature and underlying physics of the systems as they are relevant to the desired measurements before making recommendations for design of such systems.

16 TIDAL AND WAVE POWER↗

NASA NTRS1984

Remote sensing of weather and climate parameters from HIRS2/MSU on Tiros-N

At the Goddard Laboratory for Atmospheric Sciences (GLAS) a physically based satellite temperature sounding retrieval system, involving the simultaneous analysis of HIRS2 and MSU sounding data, has been developed for determining atmospheric and surface conditions which are consistent with the observed radiances. In addition to determining accurate atmospheric temperature profiles even in the presence of cloud contamination, the system provides global estimates of day and night sea or land surface temperatures, snow and ice cover, and parameters related to cloud cover. The inverse radiative transfer equation approach to the multi-spectral analysis of the data, and details of its implementation, are described. Ice, snow, and cloud fields derived for January 1979 are consistent with other measures of similar parameters obtained from AVHRR and SMMR. Monthly mean sea-surface temperature fields agree with those derived from ship and buoy measurements to 0.5 C.

Susskind, J.↗

NASA NTRS1986

Satellite scatterometer comparisons with surface measurements: Techniques and Seasat results

A method by which satellite and surface measurements can be compared in order to validate satellite wind observations is presented. The regression method requires knowledge of the expected differences (due to atmospheric variability and differing averaging) between perfect satellite and perfect buoy measurements. A model for estimating these differences is described. The regression method is applied to comparisons between Seasat Scatterometer (SASS) and U.S. National Data Buoy Office buoy data. Comparisons indicating a dependence of SASS accuracy on sea-surface temperature are summarized.

Freilich, Michael H.↗

NASA NTRS1995

Sea surface winds-A critical input to oceanic models, but are they accurately measured?

Wind, driving oceans, and the links between them to the atmosphere compose a critical parameter for the world circulation model as well as for the evaluation of climate changes. Traditionally, wind velocities have been reported by ships of oppurtunity and recorded on a network of buoys; they have also recently been generated by numerical weather prediction models and mapped with spaceborne remote sensors. Wind speeds from buoy measurements, ship observations, and model computations are compared, using the globally available altimeter returns that they have in common. Large, systematic deviations are found among the results obtained with these techniques, cautioning against use of these wind speeds.

Wu, Jin↗

NASA NTRS1996

Sea Ice Floe Size Distribution in the Beaufort Sea Measured by ERS-1 SAR (abstract)

Model results indicate that understanding summer heat balance and freshwater balance in the polar oceans requires knowledge of how much goes into vertical and lateral sea ice melt. In addition to thickness, two of the key ice parameters that affect melt rate are ice concentration and floe size. Smaller ice floes and more open water enables more heat to go into lateral melt preferentially to vertical melt, thereby enhancing warming up the upper ocean and increasing stratification. Using ERS-1 SAR imagery along two areas, one in the Beaufort Sea and another in the Chukchi Sea, floe size distributions were obtained during the summer period in 1992. Comparisons will be made of floe distributions, together with meteorological and buoy measurements, to examine the differences between an ice sink region (Chukchi) and a multiyear ice region (Beaufort) in the summer melt process.

ERS-1 SAR Beaufort Sea Chukchi Sea heat balance ic↗

NASA NTRS2021

Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval

In this study, we compare eight recently developed snow depth products over Arctic sea ice, which use satellite observations, modeling, or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance observations and airborne measurements. Large mean snow depth discrepancies are observed over the Atlantic and Canadian Arctic sectors. The differences between climatology and the snow products early in winter could be in part a result of the delaying in Arctic ice formation that reduces early snow accumulation, leading to shallower snowpacks at the start of the freeze-up season. These differences persist through spring despite overall more winter snow accumulation in the reanalysis-based products than in the climatologies. Among the products evaluated, the University of Washington (UW) snow depth product produces the deepest spring (March–April) snowpacks, while the snow product from the Danish Meteorological Institute (DMI) provides the shallowest spring snow depths. Most snow products show significant correlation with snow depths retrieved from Operational IceBridge (OIB) while correlations are quite low against buoy measurements, with no correlation and very low variability from University of Bremen and DMI products. Inconsistencies in reconstructed snow depth among the products, as well as differences between these products and in situ and airborne observations, can be partially attributed to differences in effective footprint and spatial–temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in situ and remote sensing observations.

Lu Zhou↗

DOE OSTI2026

Performance of reanalysis and mesoscale models off the coast of Hawai'i

The eastern Hawai'i coast in the United States is characterized by considerable wind resource fuelled by persistent trade winds, making it an important area for energy research. The need is strong for reanalyses and higher-resolution regional simulations where observations have been historically limited, such as Hawai'i's offshore environments. However, studies using offshore observations in other parts of the world have shown that significant errors can occur in reanalyses and wind datasets, which can lead to inaccurate estimates of wind energy generation, payback periods, and extreme weather risks at project locations. The degree of such errors is influenced by a number of factors, including spatial resolution and the handling of processes within the planetary boundary layer (PBL). In this work, we provide a wind resource characterization from year-long lidar buoy measurements off the eastern coast of O'ahu, Hawai'i, an environment previously unobserved at the rotor level, and use the characterization to evaluate the performance of two simulation datasets. The O'ahu deployment location is meteorologically unique and less complex than land-based wind resource characterizations, being strongly characterized by trade winds with minimal land–atmosphere interaction influences. Despite the unique and fairly consistent meteorological conditions, we hypothesize that distinct simulation datasets will exhibit diverse ranges of errors similar to those that have been seen for other offshore locations. We find the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) to strongly underestimate observed wind speeds at the O'ahu location (bias = −1.54 m s −1 at a height of 140 m above sea level), while a regional Weather Research and Forecasting Model (WRF) simulation produced by the University of Hawai'i (UH-WRF) provides a significantly smaller wind speed bias (−0.25 m s −1 ), highlighting the value of running regional, higher-resolution simulations. The large bias noted for ERA5 is driven by significant underestimation of fast wind speeds (>9 m s −1 ), which the study site is largely characterized by, along with discontinuities in the ERA5 diurnal cycle. We also speculate that the relative sparsity of observations for data assimilation in this remote part of the world could influence the performance of ERA5 and that challenges with characterizing island effects could impact the performance of both datasets.

17 WIND ENERGY↗