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Results for “wave forecast”

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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1975

Application of monochromatic ocean wave forecasts to prediction of wave-induced currents

The use of monochromatic wind-wave forecasts in prediction of wind-wave-induced currents was assessed. Currents were computed for selected combinations of wind conditions by using a spectrum approach which was developed by using the Bretschneider wave spectrum for partially developed wind seas. These currents were compared with currents computed by using the significant and average monochromatic wave parameters related to the Bretschneider spectrum. Results indicate that forecasts of significant wave parameters can be used to predict surface wind-wave-induced currents. Conversion of these parameters to average wave parameters can furnish reasonable estimates of subsurface current values.

Poole, L. R.↗
DOE OSTI2025

Uncertainty-Guided Prediction Horizon of Phase-Resolved Ocean Wave Forecasting Under Data Sparsity: Experimental and Numerical Evaluation

Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the τ-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the τ-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments.

42 ENGINEERING↗
DOE OSTI2020

Wave-by-Wave Forecasting via Assimilation of Marine Radar Data

Abstract This work describes a phase-resolving wave-forecasting algorithm that is based on the assimilation of marine radar image time series. The algorithm is tested against synthetic data and field observations. The algorithm couples X-band marine radar observations with a phase-resolving wave model that uses the linear mild slope equations for reconstruction of water surface elevations over a large domain of O (km) and a prescribed time window of O (min). The reconstruction also enables wave-by-wave forecasting through forward propagation in space and time. Marine radar image time series provide the input wave observations through a previously given relationship between backscatter intensity and the radial component of the sea surface slope. The algorithm assimilates the wave slope information into the model via a best-fit wave source function at the boundary that minimizes the slope reconstruction error over an annular region at the outer ranges of the radar images. The wave model is then able to propagate the waves across a polar domain to a location of interest at nearer ranges. The constraints on the method for achieving real-time forecasting are identified, and the algorithm is verified against synthetic data and tested using field observations.

Engineering↗
NASA NTRS1983

The potential of SAR directional spectra in operational wave forecasting

Digitally processed Seasat SAR ocean wave imagery, when digitally transformed, smoothed, and corrected for known instrument biases, can accurately track multiple (at least three) wave systems in the open ocean, across major currents, and into shallow water. Furthermore, the relative backscatterer of the digitally processed SAR image, when sampled within a single pass and along a constant range, responds to the local wind through a simple power law relationship. The results indicate that SAR spectra, if obtained from a low altitude satellite, could be an important supplement to global winds and non-directional wave heights obtained through other methods.

Beal, R. C.↗
DOE OSTI2021

Deep Learning with Quantized Neural Networks for Gravitational-wave Forecasting of Eccentric Compact Binary Coalescence

We present the first application of deep learning forecasting for binary neutron stars, neutron star–black hole systems, and binary black hole mergers that span an eccentricity range e ≤ 0.9. In this work, we train neural networks that describe these astrophysical populations, and then test their performance by injecting simulated eccentric signals in advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) noise available at the Gravitational Wave Open Science Center to (1) quantify how fast neural networks identify these signals before the binary components merge; (2) quantify how accurately neural networks estimate the time to merger once gravitational waves are identified; and (3) estimate the time-dependent sky localization of these events from early detection to merger. Our findings show that deep learning can identify eccentric signals from a few seconds (for binary black holes) up to tens of seconds (for binary neutron stars) prior to merger. A quantized version of our neural networks achieves 4× reduction in model size, and up to 2.5× inference speedup. These novel algorithms may be used to facilitate time-sensitive multimessenger astrophysics observations of compact binaries in dense stellar environments.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1986

Operational wave forecasting with spaceborne SAR: Prospects and pitfalls

Measurements collected in the Shuttle Imaging Radar (SIR-B) Extreme Waves Experiment confirm the ability of Synthetic Aperture Radar (SAR) to yield useful estimates of wave directional energy spectra over global scales, at least for shuttle altitudes. However, azimuth fall-off effects tend to become severe for wavelengths shorter than about 100 m in most sea states. Moreover, the azimuth fall-off problem becomes increasingly severe as the platform altitude increases beyond 300 km. The most viable solution to the global wave measurements problem may be a low altitude spacecraft containing a combination of both the SAR and the Radar Ocean Wave Spectrometry (ROWS). Such a combination could have a synergy which yield global spectral estimates superior to those of either instrument singly employed.

Beal, R. C.↗
NASA NTRS1970

The integration of remote sensing data into global weather prediction, wave forecasting, and ocean circulation computer based systems

Data from infrared imaging systems and satellite infrared spectrometer (SIRS) for determining sea surface temperature and the atmospheric structure in cloudless areas over the oceans are discussed. Although some interpretations differ, it is clear that simultaneous measurements of radar sea return and passive microwave temperature will provide estimates of the wind speed, and perhaps wind direction, over the oceans, especially in cloudless areas, for a wide range of wind speeds. The problem of integrating the data that would be obtained by a spacecraft, especially one with a combination radar-radiometer, into global analysis procedures for meteorological, wave, and oceanographic predictions is described.

Pierson, W. J., Jr.↗
DOE OSTI2023

Planck constraints and gravitational wave forecasts for primordial black hole dark matter seeded by multifield inflation

Here, we perform a Markov Chain Monte Carlo analysis of a simple yet generic multifield inflation model characterized by two scalar fields coupled to each other and nonminimally coupled to gravity, fit to Planck 2018 cosmic microwave background (CMB) data. In particular, model parameters are constrained by data on the amplitude of the primordial power spectrum of scalar curvature perturbations on CMB scales A s , the spectral index n s , and the ratio of power in tensor to scalar modes r, with a prior that the primordial power spectrum should also lead to primordial black hole (PBH) production sufficient to account for the observed dark matter abundance. We find that n s in particular controls the constraints on our model. Whereas previous studies of PBH formation from an ultraslow-roll phase of inflation have highlighted the need for at least one model parameter to be highly fine-tuned, we identify a degeneracy direction in parameter space such that shifts by ~10% of one parameter can be compensated by comparable shifts in other parameters while preserving a close fit between model predictions and observations. Furthermore, we find this allowed parameter region produces observable gravitational wave signals in the frequency ranges to which upcoming experiments are projected to be sensitive, including Advanced LIGO and Virgo, the Einstein Telescope, Cosmic Explorer, DECIGO, and LISA.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1981

The potential impact of scatterometry on oceanography - A wave forecasting case

A series of observing system simulation experiments have been performed in order to assess the potential impact of marine surface wind data on numerical weather prediction. In addition to conventional data, the experiments simulated the time-continuous assimilation of remotely sensed marine surface wind or temperature sounding data. The wind data were fabricated directly for model grid points intercepted by a Seasat-1 scatterometer swath and were assimilated into the lowest active level (945 mb) of the model using a localized successive correction method. It is shown that Seasat wind data can greatly improve numerical weather forecasts due to better definition of specific features. The case of the QE II storm is examined.

Cane, M. A.↗
NASA NTRS1993

An Algorithm for Forecasting Mountain Wave Related Turbulence in the Stratosphere

A global mountain wave parameterization for prediction of wave related displacements and turbulence is described. The parameterization is used with input from NMC analyses of wind and temperature to examine small-scale disturbances encountered by the NASA high-altitude ER-2 during the Second Airborne Arctic Stratosphere Experiment (AASE-II). The magnitude and location of observed large wave events are well reproduced. A strong correlation is suggested between patches of moderate turbulence encountered by the ER-2 and locations where breaking mountain waves are predicted by the parameterization. These facts suggest that useful forecasts of global mountain wave activity, including wave related CAT, can be made quickly and inexpensively using our mountain wave parameterization with input from current numerical forecast models.

Chan, Roland↗
NASA NTRS1983

900 km of digital ocean wave spectra from the SEASAT SAR

One of the primary reasons for including the SAR in the complement of ocean microwave instruments on SEASAT was the global ocean wave spectra. An accurate, timely, and global knowledge of the full two dimensional or directional surface wave spectra has great value in both operational wave forecasting and in wave climatology, especially in the Southern Oceans. The sparse sampling obtainable from a single orbiting SAR would by itself be inadequate to reconstruct a global wave field, but when supplemented with auxiliary knowledge of the winds, the SAR might provide essential updates to a global wave model.

Beal, R. C.↗
NASA NTRS1987

Measuring ocean waves from space; Proceedings of the Symposium, Johns Hopkins University, Laurel, MD, Apr. 15-17, 1986

Papers are presented on ocean-wave prediction; the quasi-universal form of the spectra of wind-generated gravity waves at different stages of their development; the limitations of the spectral measurements and observations of the group structure of surface waves; the effect of swell on the growth of wind wave; operational wave forecasting; ocean-wave models, and seakeeping using directional wave spectra. Consideration is given to microwave measurements of the ocean-wave directional spectra; SIR research; estimating wave energy spectra from SAR imagery, with the radar ocean-wave spectrometer, and SIR-B; the wave-measurement capabilities of the surface contour radar and the airborne oceanographic lidar; and SIR-B ocean-wave enhancement with fast-Fourier transform techniques. Topics discussed include wave-current interaction; the design and applicability of Spectrasat; the need for a global wave monitoring system; the age and source of ocean swell observed in Hurricane Josephine; and the use of satellite technology for insulin treatment.

Beal, Robert C.↗
NASA NTRS1983

Potential impact of remote sensing data on sea-state analysis and prediction

The severe North Atlantic storm which damaged the ocean liner Queen Elizabeth 2 (QE2) was studied to assess the impact of remotely sensed marine surface wind data obtained by SEASAT-A, on sea state specifications and forecasts. Alternate representations of the surface wind field in the QE2 storm were produced from the SEASAT enhanced data base, and from operational analyses based upon conventional data. The wind fields were used to drive a high resolution spectral ocean surface wave prediction model. Results show that sea state analyses would have been vastly improved during the period of storm formation and explosive development had remote sensing wind data been available in real time. A modest improvement in operational 12 to 24 hour wave forecasts would have followed automatically from the improved initial state specification made possible by the remote sensing data in both numerical and sea state prediction models. Significantly improved 24 to 48 hour wave forecasts require in addition to remote sensing data, refinement in the numerical and physical aspects of weather prediction models.

Cardone, V. J.↗
NASA NTRS2004

Observational Evidence Against Mountain-Wave Generation of Ice Nuclei as a Prerequisite for the Formation of Three Solid Nitric Acid Polar Stratospheric Clouds Observed in the Arctic in Early December 1999

A number of recently published papers suggest that mountain-wave activity in the stratosphere, producing ice particles when temperatures drop below the ice frost point, may be the primary source of large NAT particles. In this paper we use measurements from the Advanced Very High Resolution Radiometer (AVHRR) instruments on board the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites to map out regions of ice clouds produced by stratospheric mountain-wave activity inside the Arctic vortex. Lidar observations from three DC-8 flights in early December 1999 show the presence of solid nitric acid (Type Ia or NAT) polar stratospheric clouds (PSCs). By using back trajectories and superimposing the position maps on the AVHRR cloud imagery products, we show that these observed NAT clouds could not have originated at locations of high-amplitude mountain-wave activity. We also show that mountain-wave PSC climatology data and Mountain Wave Forecast Model 2.0 (MWFM-2) raw hemispheric ray and grid box averaged hemispheric wave temperature amplitude hindcast data from the same time period are in agreement with the AVHRR data. Our results show that ice cloud formation in mountain waves cannot explain how at least three large scale NAT clouds were formed in the stratosphere in early December 1999.

MOUNTAIN WAVES↗