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 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 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 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 Real-Time Inverse Estimation of Ocean Wave Spectra from Vessel-Motion Sensors Using Adaptive Kalman Filter

2019 ◽  
Vol 9 (14) ◽  
pp. 2797 ◽  
Author(s):  
HanSung Kim ◽  
HeonYong Kang ◽  
Moo-Hyun Kim
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
High Frequency ◽  
Practical Importance ◽  
Ocean Wave ◽  
Sensor Data ◽  
Wave Spectra ◽  
Inverse Estimation ◽  
High Frequency Waves ◽  
Vessel Motion

The real-time inverse estimation of the ocean wave spectrum and elevation from a vessel-motion sensor is of significant practical importance, but it is still in the developing stage. The Kalman-filter method has the advantages of real-time estimation, cost reduction, and easy installation than other methods. Reasonable estimation of high-frequency waves is important in view of covering various sea states. However, if the vessel is less responsive for high-frequency waves, amplified noise may occur and cause overestimation problem there. In this paper, a configuration of Kalman filter with applying the principle of Wiener filter is proposed to suppress those over-estimations. Over-estimation is significantly reduced at high frequencies when the method is applied, and reliable real-time wave spectra and elevations can be obtained. The simulated sensor data was used, but the proposed algorithm has been proved to perform well for various sea states and different vessels. In addition, the proposed Kalman-filter technique is robust when it is applied to time-varying sea states.

scholarly journals Factors of boreal summer latent heat flux variations over the tropical western North Pacific

2021 ◽  
Author(s):  
Yuqi Wang ◽  
Renguang Wu
Keyword(s):  
Heat Flux ◽  
Wind Speed ◽  
North Pacific ◽  
High Frequency ◽  
Western North Pacific ◽  
Surface Wind ◽  
Low Frequency ◽  
Boreal Summer ◽  
Wind Intensity ◽  
Tropical Western North Pacific

AbstractSurface latent heat flux (LHF) is an important component in the heat exchange between the ocean and atmosphere over the tropical western North Pacific (WNP). The present study investigates the factors of seasonal mean LHF variations in boreal summer over the tropical WNP. Seasonal mean LHF is separated into two parts that are associated with low-frequency (> 90-day) and high-frequency (≤ 90-day) atmospheric variability, respectively. It is shown that low-frequency LHF variations are attributed to low-frequency surface wind and sea-air humidity difference, whereas high-frequency LHF variations are associated with both low-frequency surface wind speed and high-frequency wind intensity. A series of conceptual cases are constructed using different combinations of low- and high-frequency winds to inspect the respective effects of low-frequency wind and high-frequency wind amplitude to seasonal mean LHF variations. It is illustrated that high-frequency wind fluctuations contribute to seasonal high-frequency LHF only when their intensity exceeds the low-frequency wind speed under which there is seasonal accumulation of high-frequency LHF. When high-frequency wind intensity is smaller than the low-frequency wind speed, seasonal mean high-frequency LHF is negligible. Total seasonal mean LHF anomalies depend on relative contributions of low- and high-frequency atmospheric variations and have weak interannual variance over the tropical WNP due to cancellation of low- and high-frequency LHF anomalies.

Toward global measurements of ocean wave spectra

1982 ◽  
Author(s):  
F. Jackson ◽  
W. Walton ◽  
P. Baker
Keyword(s):  
Ocean Wave ◽  
Wave Spectra

Refraction of Continuous Ocean Wave Spectra

1969 ◽  
Vol 95 (4) ◽  
pp. 437-448
Author(s):  
Thorbjorn Karlsson
Keyword(s):  
Ocean Wave ◽  
Wave Spectra
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