scholarly journals Wind speed and direction estimation from wave spectra using deep learning

2022 ◽  
Vol 15 (1) ◽  
pp. 1-9
Author(s):  
Haoyu Jiang
Keyword(s):  
Wind Speed ◽  
High Frequency ◽  
Deep Neural Networks ◽  
Fourier Coefficients ◽  
Wave Spectrum ◽  
Ocean Wave ◽  
Wave Spectra ◽  
Strongly Coupled ◽  
Wave Measurements ◽  
Wind Measurements

Abstract. High-frequency parts of ocean wave spectra are strongly coupled to the local wind. Measurements of ocean wave spectra can be used to estimate sea surface winds. In this study, two deep neural networks (DNNs) were used to estimate the wind speed and direction from the first five Fourier coefficients from buoys. The DNNs were trained by wind and wave measurements from more than 100 meteorological buoys during 2014–2018. It is found that the wave measurements can best represent the wind information about 40 min previously because the high-frequency portion of the wave spectrum integrates preceding wind conditions. The overall root-mean-square error (RMSE) of estimated wind speed is ∼1.1 m s−1, and the RMSE of the wind direction is ∼ 14∘ when wind speed is 7–25 m s−1. This model can be used not only for the wind estimation for compact wave buoys but also for the quality control of wind and wave measurements from meteorological buoys.

scholarly journals Wind Speed and Direction Estimation from Wave Spectra using Deep Learning

2021 ◽  
Author(s):  
Haoyu Jiang
Keyword(s):  
Wind Speed ◽  
High Frequency ◽  
Deep Neural Networks ◽  
Fourier Coefficients ◽  
Ocean Wave ◽  
Wave Spectra ◽  
Strongly Coupled ◽  
Wave Measurements ◽  
Short Period ◽  
Wind Measurements

Abstract. High-frequency parts of ocean wave spectra are strongly coupled to the local wind. Measurements of ocean wave spectra can be used to estimate sea surface winds. In this study, two deep neural networks (DNNs) were used to estimate the wind speed and direction from the first five Fourier coefficients from buoys. The DNNs were trained by wind and wave measurements from more than 100 meteorological buoys during 2014–2018. It is found that the wave measurements can best represent the wind information ~1 h ago, because the wave spectra contain wind information a short period before. The overall root-mean-square error (RMSE) of estimated wind speed is ~1.1 m/s, and the RMSE of wind direction is ~14° when wind speed is 7~25 m/s. This model can not only be used for the wind estimation for compact wave buoys but also for the quality control of wind and wave measurements from meteorological buoys.

scholarly journals A Multisensor Comparison of Ocean Wave Frequency Spectra from a Research Vessel during the Southern Ocean Gas Exchange Experiment

2013 ◽  
Vol 30 (12) ◽  
pp. 2907-2925 ◽  
Author(s):  
Alejandro Cifuentes-Lorenzen ◽  
James B. Edson ◽  
Christopher J. Zappa ◽  
Ludovic Bariteau
Keyword(s):  
High Frequency ◽  
Doppler Radar ◽  
Inertial Sensors ◽  
Wave Spectrum ◽  
Ocean Wave ◽  
Modulation Transfer ◽  
Laser Altimeter ◽  
Frequency Spectra ◽  
Wave Statistics ◽  
Wave Measurements

Abstract Obtaining accurate measurements of wave statistics from research vessels remains a challenge due to the platform motion. One principal correction is the removal of ship heave and Doppler effects from point measurements. Here, open-ocean wave measurements were collected using a laser altimeter, a Doppler radar microwave sensor, a radar-based system, and inertial measurement units. Multiple instruments were deployed to capture the low- and high-frequency sea surface displacements. Doppler and motion correction algorithms were applied to obtain a full 1D (0.035–1.3 ± 0.2 Hz) wave spectrum. The radar-based system combined with the laser altimeter provided the optimal low- and high-frequency combination, producing a frequency spectrum in the range from 0.035 to 1.2 Hz for cruising speeds ≤3 m s−1 with a spectral rolloff of f−4 Hz and noise floor of −20/−30 dB. While on station, the significant wave height estimates were comparable within 10%–15% among instrumentation. Discrepancies in the total energy and in the spectral shape between instruments arise when the ship is in motion. These differences can be quantified using the spectral behavior of the measurements, accounting for aliasing and Doppler corrections. The inertial sensors provided information on the amplitude of the ship’s modulation transfer function, which was estimated to be ~1.3 ± 0.2 while on station and increased while underway [2.1 at ship-over-ground (SOG) speed; 4.3 m s−1]. The correction scheme presented here is adequate for measurements collected at cruising speeds of 3 m s−1 or less. At speeds greater than 5 m s−1, the motion and Doppler corrections are not sufficient to correct the observed spectral degradation.

scholarly journals WAVE MEASUREMENTS IN OPEN OCEAN

1976 ◽  
Vol 1 (15) ◽  
pp. 6
Author(s):  
Davidson T. Chen ◽  
Benjamin S. Yaplee ◽  
Donald L. Hammond ◽  
Paul Bey
Keyword(s):  
Water Pressure ◽  
Open Ocean ◽  
Ocean Wave ◽  
Naval Research ◽  
Wave Spectra ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Wave Measurements ◽  
Microwave Sensor ◽  
Low Pass

The ability to measure the wave spectra in the open ocean from a moving vessel has met with varying degrees of success. Each sensor to date has suffered in its performance due to environmental conditions or due to its physical placement aboard the vessel for measuring the unperturbed sea. This paper will discuss the utilization of a microwave sensor on a moving vessel for measuring the open ocean wave spectra. Employing microwaves, some of the limitations of other sensors are not experienced. Tucker [1] developed the Tuckermeter for measuring the wave spectra from a moving ship by sensing changes in water pressure due to surface wave conditions. The Tuckermeter is placed below the water line and thus requires calibration for each wave frequency, ship speed, and depth. Since the sensor operates on pressure, it performs as a low pass filter and will not sense the higher frequencies. A microwave shipboard wave height radar sensor for measuring the ocean wave spectra was developed by the Naval Research Laboratory (NRL) and was installed on the S.S. McLean in February 1975 and its performance, design, and analysis of data for one data run will be discussed.

scholarly journals Wave spectral shapes in the coastal waters based on measured data off Karwar, west coast of India

2017 ◽  
Author(s):  
M. Anjali Nair ◽  
V. Sanil Kumar
Keyword(s):  
Wave Height ◽  
Coastal Waters ◽  
High Frequency ◽  
Significant Wave Height ◽  
Wave Spectrum ◽  
Temporal Variations ◽  
Wave Spectra ◽  
Monsoon Period ◽  
Significant Wave ◽  
Spectral Shapes

Abstract. Understanding of the wave spectral shapes is of primary importance for the design of marine facilities. In this paper, the wave spectra collected from January 2011 to December 2015 in the coastal waters are examined to know the temporal variations in the wave spectral shape. For 31.15 % of the time, peak frequency is between 0.08 and 0.10 Hz and the significant wave height is also relatively high (~ 1.55 m) for waves in this class. The slope of the high-frequency tail of the monthly average wave spectra is high during the Indian summer monsoon period (June–September) compared to other months and it increases with increase in significant wave height. There is no much interannual variation in slope for swell dominated spectra during the monsoon, while in the non-monsoon period when wind-seas have much influence, the slope varies significantly. Since the high-frequency slope of the wave spectrum is within the range 3–4 during the monsoon period, Donelan spectrum shows better fit for the wave spectra in monsoon months compared to other months.

scholarly journals CONFIDENCE INTERVALS FOR OCEAN WAVE SPECTRA

1972 ◽  
Vol 1 (13) ◽  
pp. 10
Author(s):  
Leon E. Borgman
Keyword(s):  
Confidence Intervals ◽  
Wave Spectrum ◽  
Structural Response ◽  
Ocean Wave ◽  
Wave Spectra ◽  
Wave Direction ◽  
Chi Square ◽  
Simulation Procedure ◽  
Spectral Estimates ◽  
Chi Squared

The random nature of ocean wave records introduces statistical variability into the wave spectrum estimates based on these records. This may cause inaccuracy in subsequent calculations such as the prediction of the primary wave direction or the estimation of structural response. Confidence intervals on such estimates are needed to evaluate whether adequate estimate accuracy has been obtained. The chi-squared confidence interval commonly used for wave spectra is based on the assumption of a Gaussian sea surface. Its applicability for hurrican size waves has been open for question. Therefore, after a brief outline of the relevant statistical relations basic to the chi-squared procedure, wave data from Hurrican Carla is empirically analyzed and compared with the theoretical conclusions. A simulation procedure is used to proceed from the data to probability interval statements. A comparison of these with the correponding chi-squared statements shows the chi-squared relations to be fairly reasonable approximations for spectral estimates averaged over bands of at least eight values. The empirical simulation procedure can be extended to subsequent calculations based on the spectral estimates while the chi-square method encounters difficulty for such problems.

Ocean Wave Measurements From ENVISAT ASAR Data Using a Parametric Inversion Scheme

2004 ◽  
Author(s):  
J. Schulz-Stellenfleth ◽  
S. Lehner ◽  
D. Hoja ◽  
J. C. Nieto-Borge
Keyword(s):  
A Priori ◽  
Wave Spectrum ◽  
Global Scale ◽  
Wave Mode ◽  
Ocean Wave ◽  
Wave System ◽  
Wave Spectra ◽  
Data Set ◽  
Envisat Asar ◽  
Sar Imaging

A parametric algorithm is presented to estimate two-dimensional ocean wave spectra from ENVISAT ASAR wave mode data on a global scale. The retrieval scheme makes use of prior information taken from numerical wave models. The Partition Rescale and Shift algorithm (PARSA) is based on a partitioning technique, which splits an a priori wave spectrum into its wave system components. Integral parameters of these systems, such as mean direction, mean wavelength, waveheight, and directional spreading are then adjusted iteratively to improve the consistency with the SAR observation. The method takes into account the full nonlinear SAR imaging process and uses a maximum a posteriori approach, which is based on statistical model quantifying the errors of the SAR imaging model, the SAR measurement, and the prior wave spectra. The method is applied to a global data set of ENVISAT ASAR data acquired during the CAL/VAL phase. The benefit of cross spectra compared to conventional symmetric image spectra is demonstrated.

scholarly journals Apply GNSS-Reflectometry Technique for wind retrieval in typhoon condition

2019 ◽  
Vol 94 ◽  
pp. 05003
Author(s):  
Hwa Chien ◽  
Quang-Huy Lu ◽  
Wen-Hao Yeh
Keyword(s):  
Wind Speed ◽  
Gravity Wave ◽  
Wave Spectrum ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Data Uncertainty ◽  
Wave Spectra ◽  
Mean Square ◽  
Time Data ◽  
Wave Directionality

Global Navigation Satellite System-Reflectometry (GNSS-R) is an innovative Earth observation technique that exploits signal from satellite constellations after reflection on the Eart h surface. The GNSS-R techniques is also known to have the potential of mapping the surface wind speed up to 70 m/s, and thus provide a promising solution. Abundant real-time data of the typhoon surface wind speed will play the crucial role on the improvement of typhoon intensification forecasting. The aim of present study is to investigate the influences of high wind speed and the corresponding giant waves during typhoon to the GNSS-R wind speed retrieval algorithms. The Mean Square Slope that altered by the complex wave directionality is discussed. Finally, the uncertainties of wind speed retrieval with respect to the influences of wave directionality is assessed. The level 1 product from the GNSS-R receiver is a map of GPS signal power scattered from the sea surface, as a 2D function of delay and Doppler frequency, which is known as a Delay-Doppler Map, or DDM. Based on Zavorotny-Voronovich model, the DDMs are simulated from the Directional Mean Square Slope (DMSS) that obtained from the combination of capillary wave spectra and gravity wave spectra. The gravity wave spectra were calculated using a 3rd generation numerical wave model that driven by the Dujuan typhoon (2015) wind fields with super-fine resolution. The complexity of directional wave spectrum, such as extreme spatial heterogeneity, bimodal spectra and varying directional spreading alter the DMSS and DDM. Various observables, e.g. DDM Average (DDMA), and Leading Edge Slope (LES) are then applied to the simulated DDM. Regression-based wind retrievals are developed for each individual observable using empirical geophysical model functions. The wind speed retrieval in case of Dujuan typhoon are compared with the target data uncertainty assessment.

Evaluation on the Capability of Revealing Ocean Swells from Sentinel-1A Wave Spectra Measurements

2020 ◽  
Vol 37 (7) ◽  
pp. 1289-1304
Author(s):  
Xuan Wang ◽  
Romain Husson ◽  
Haoyu Jiang ◽  
Ge Chen ◽  
Guoping Gao
Keyword(s):  
Wave Energy ◽  
Wave Spectrum ◽  
Wave Mode ◽  
Wave Frequency ◽  
Wave Parameter ◽  
Wave System ◽  
Wave Spectra ◽  
Wave Measurements ◽  
The Difference ◽  
Level 2

AbstractWave measurements retrieved by Sentinel-1A level-2 ocean (OCN) products are sensitive to swells other than wind seas, and are considered to provide a finer resolution of ocean swells. To assess the capability of swell retrieval globally, OCN products are validated against WAVEWATCH III (WW3) wave spectra for two available incidence angles [“wave mode” (WV); WV1: 23°; WV2: 36°], focused on the integral wave parameters and most energetic wave system of Sentinel-1A. The wave parameter difference between Sentinel-1A and WW3 along antenna look angles for WV1 demonstrates the obvious impact of the nonlinearity influence in the azimuth direction, resulting in an unrealistically high wave height at the low wave frequency, and the spurious split of wave systems in the range direction, due to the vanishing of velocity bunching modulation. WV2 is less pronounced in these two aspects, but tends to shift wave energy to a higher wave frequency in the range direction. The inside discrepancy of wave energy has two noticeable features: the difference in peak wavelengths in the wave spectrum is positively clustered in the azimuth direction and negatively clustered in the range direction; some of the most energetic partitions derived from Sentinel-1A are difficult to assign to any wave systems in WW3. This phenomenon could be related to wind-wave coupling as the azimuth cutoff/WW3 peak wavelength is confined to a ratio below 0.5 for the negative difference between Sentinel-1A and WW3 peak wavelengths and the spectral distance of most energetic wave system in Sentinel-1A highly resembles “swell pools.”

scholarly journals Reflection and transmission of ocean wave spectra by a band of randomly distributed ice floes

2015 ◽  
Vol 56 (69) ◽  
pp. 315-322 ◽  
Author(s):  
Fabien Montiel ◽  
Vernon A. Squire ◽  
Luke G. Bennetts
Keyword(s):  
Wave Spectrum ◽  
Ocean Wave ◽  
Wave Spectra ◽  
Ensemble Averaging ◽  
Ice Dynamics ◽  
Reflection And Transmission ◽  
Directional Wave ◽  
Ice Edge ◽  
Ice Floes ◽  
Edge Band

AbstractA new ocean wave/sea-ice interaction model is proposed that simulates how a directional wave spectrum evolves as it travels through an arbitrary finite array of circular ice floes, where wave/ ice dynamics are entirely governed by wave-scattering effects. The model is applied to characterize the wave reflection and transmission properties of a strip of ice floes, such as an ice edge band. A method is devised to extract the reflected and transmitted directional wave spectra produced by the array. The method builds upon an integral mapping from polar to Cartesian coordinates of the scattered wave components. Sensitivity tests are conducted for a row of floes randomly perturbed from a regular arrangement. Results for random arrays are generated using ensemble averaging. A realistic ice edge band is then reconstructed from field experiment data. Simulations show good qualitative agreement with the data in terms of transmitted wave energy and directional spreading. In particular, it is observed that short waves become isotropic quickly after penetrating the ice field.

Estimation of Wind-Sea and Swell Components in a Bimodal Sea State

2004 ◽  
Vol 128 (4) ◽  
pp. 265-270 ◽  
Author(s):  
K. C. Ewans ◽  
E. M. Bitner-Gregersen ◽  
C. Guedes Soares
Keyword(s):  
Wave Spectrum ◽  
Low Frequency ◽  
Wave Spectra ◽  
Spectral Parameters ◽  
High Frequency Peak ◽  
Wave Measurements ◽  
Frequency Peak ◽  
Directional Wave ◽  
Wind Sea ◽  
Complete Frequency

Methods for separating the spectral components and describing bimodal wave spectra are evaluated with reference to wave spectra from directional wave measurements made at the Maui location off the west coast of New Zealand. Two methods involve partitioning bimodal wave spectra into wind-sea and swell components and then fitting a spectral function to each component, while the third assigns an average spectral shape based on the integrated spectral parameters. The partitioning methods involve separating the wave spectrum into two frequency bands: a low-frequency peak, the swell component, and a high-frequency peak, the wind-sea. One partitioning method uses only the frequency spectrum while the other analyzes the complete frequency-direction spectrum. Comparison of the spectral descriptions and derived parameters against the measured counterparts provides insight into the accuracy of the different approaches to describing actual bimodal sea states.

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