scholarly journals Large Tank Evaluation of a GPS Wave Buoy for Wind Stress Measurements

2017 ◽  
Vol 34 (6) ◽  
pp. 1225-1234
Author(s):  
Naoya Suzuki ◽  
Takuji Waseda ◽  
Mark A. Donelan ◽  
Takeshi Kinoshita
Keyword(s):  
Wind Velocity ◽  
Wind Stress ◽  
Friction Velocity ◽  
Wave Spectrum ◽  
Sonic Anemometer ◽  
Eddy Correlation ◽  
Sensor Data ◽  
Motion Sensor ◽  
Ocean Engineering ◽  
Wind Speeds

AbstractThere exists considerable disagreement among the observed values of the drag coefficient CD. To develop a model of CD, the wind stress generally will be calculated from the eddy correlation method. A buoy is suitable to measure the wind stress in many sea surface conditions. However, the motion correction is very difficult because the anemometer measures the wind components, including the motion of the buoy. In this study, as a first approach, the motion of a prototype buoy system with a three-axis sonic anemometer and a six-axis motion sensor installed in the small-size GPS observation buoy was investigated. The wave tank is in the ocean engineering basin of the Institute of Industrial Science, University of Tokyo, Japan. The imposed conditions were wave periods from 1.1 to 2.5 s; wind speeds of 0, 2, and 5 m s−1; and the wave spectrum was either regular or irregular. The motion of the buoy was measured in 120 cases. For all the wave periods and without wind, the wind velocity measured by the sonic anemometer and the velocity of the anemometer motion calculated from the motion sensor data showed good agreement. Also, in the condition with wind speeds of 2 and 5 m s−1, the motion-corrected wind velocity, obtained by deducting the velocity of the anemometer motion from the wind velocity measured by the anemometer, yielded the true wind velocity with better-than-average (4.3%) accuracy. The friction velocity from corrected wind velocity components shows agreement with the friction velocity measured from a fixed sonic anemometer within expected intrinsic error. The buoy system is expected to be able to measure the wind stress in the field. The next stage is to do comprehensive field tests.

Ocean surface wave dynamics, energy and momentum air-sea transfer under a variety of wind and waves conditions

2020 ◽  
Author(s):  
Francisco J. Ocampo-Torres ◽  
Pedro Osuna ◽  
Nicolas Rascle ◽  
Hector Garcia-Nava ◽  
Carlos F. Herrera-Vazquez ◽  
...  
Keyword(s):  
Surface Wave ◽  
Momentum Transfer ◽  
Wave Spectrum ◽  
Sonic Anemometer ◽  
Correlation Method ◽  
Ocean Surface ◽  
Eddy Correlation ◽  
Wave Number ◽  
Directional Spectrum ◽  
Ocean Surface Wave

<p>Direct measurements have been conducted from a spar buoys deployed in the Gulf of Mexico, and in the vicinity of Todos Santos Island, offshore Ensenada BC, Mexico, in order to better understand ocean surface wave modulated processes under a variety of oceanographic and meteorological conditions. Full ocean surface wave directional spectrum is estimated from sea surface elevation data acquired with an array of capacitance wires, to represent directional spectrum as a function of frequency and direction, as well as a function of the wave number components Kx and Ky. Momentum transfer between ocean and the atmosphere is calculated directly through the eddy correlation method applied to wind velocity components acquired with a sonic anemometer. Momentum transfer variability is analysed to study its dependance on the surface wave conditions, with special emphasis on mixed sea states. Comparison between single peak spectra results with those cases where bi-modal spectra were present are performed in order to detect wind stress variability effects. Ocean-atmosphere transfer of momentum is studied and explained in terms of the shape and evolution of the surface wave spectrum. This research is funded by SENER-CONACYT 249795 and 201441 projects.</p>

Investigation of the effect of wind speed fluctuation on the drag coefficient

2020 ◽  
Author(s):  
Naoya Suzuki ◽  
Takuji Waseda ◽  
Naohisa Takagaki
Keyword(s):  
Wind Speed ◽  
Drag Coefficient ◽  
Friction Velocity ◽  
Sonic Anemometer ◽  
Correlation Method ◽  
Eddy Correlation ◽  
Large Fluctuation ◽  
Speed Fluctuation ◽  
Period Data ◽  
The University

<p>The drag coefficient is generally expressed as functions only of the wind speed U<sub>10</sub>. However, there exists considerable disagreement among the observed values of the drag coefficient. In this study, we observed the wind stress at the coastal tower of Hiratsuka Offshore Experimental Tower of the University of Tokyo in Japan. The 3-axis sonic anemometer was installed on the top of the tower, which was 20 m above mean sea level. The observation periods were from September 15, 2015 to December 31, 2019. The eddy correlation method was used to calculate the friction velocity every 10 minutes. The variation of the drag coefficient plotted against the wind speed U<sub>10</sub> has very large using the all period data. The variation of the drag coefficient was reduced by excluding large fluctuation of wind speed in time series within one hour. Furthermore, the sudden changes of the wind speed and direction was also found to affect the variation of the drag coefficient. These results show that the wind speed fluctuation influenced the variation of the drag coefficient. We also investigate the effect of waves on the drag coefficient.</p>

scholarly journals Improving Lidar-Derived Turbulence Estimates for Wind Energy

2016 ◽  
Author(s):  
Jennifer F. Newman ◽  
Andrew Clifton
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Wind Energy ◽  
Sonic Anemometer ◽  
Turbulence Models ◽  
Volume Averaging ◽  
Turbine Rotor ◽  
Error Reduction ◽  
Power Prediction ◽  
Wind Speeds

Abstract. Remote sensing devices such as lidars are currently being investigated as alternatives to cup anemometers on meteorological towers. Although lidars can measure mean wind speeds at heights spanning an entire turbine rotor disk and can be easily moved from one location to another, they measure different values of turbulence than an instrument on a tower. Current methods for improving lidar turbulence estimates include the use of analytical turbulence models and expensive scanning lidars. While these methods provide accurate results in a research setting, they cannot be easily applied to smaller, commercially available lidars in locations where high-resolution sonic anemometer data are not available. Thus, there is clearly a need for a turbulence error reduction model that is simpler and more easily applicable to lidars that are used in the wind energy industry. In this work, a new turbulence error reduction algorithm for lidars is described. The algorithm, L-TERRA, can be applied using only data from a stand-alone commercially available lidar and requires minimal training with meteorological tower data. The basis of L-TERRA is a series of corrections that are applied to the lidar data to mitigate errors from instrument noise, volume averaging, and variance contamination. These corrections are applied in conjunction with a trained machine-learning model to improve turbulence estimates from a vertically profiling WINDCUBE v2 lidar. L-TERRA was tested on data from three sites – two in flat terrain and one in semicomplex terrain. L-TERRA significantly reduced errors in lidar turbulence at all three sites, even when the machine-learning portion of the model was trained on one site and applied to a different site. Errors in turbulence were then related to errors in power through the use of a power prediction model for a simulated 1.5 MW turbine. L-TERRA also reduced errors in power significantly at all three sites, although moderate power errors remained for periods when the mean wind speed was close to the rated wind speed of the turbine and periods when variance contamination had a large effect on the lidar turbulence error. Future work will include the use of a lidar simulator to better understand how different factors affect lidar turbulence error and to determine how these errors can be reduced using information from a stand-alone lidar.

scholarly journals Variation of diurnal Turbulence Kinetic Energy using sonic anemometer observation: a case study Ledeng, Bandung

2018 ◽  
Author(s):  
Sandy Hardian Susanto Herho ◽  
Dasapta Erwin Irawan
Keyword(s):  
Kinetic Energy ◽  
Diurnal Variation ◽  
Wind Velocity ◽  
Sonic Anemometer ◽  
Diurnal Variations ◽  
Turbulence Kinetic Energy ◽  
Atmospheric Conditions

Sonic anemometer observation was performed on 29 - 30 September 2014 in Ledeng, Bandung to see diurnal variations of Turbulence Kinetic Energy (TKE) that occurred in this area. The measured sonic anemometer was the wind velocity components u, v, and w. From the observation result, it can be seen that the diurnal variation that happened was quite significant. The maximum TKE occurs during the daytime when atmospheric conditions tend to be unstable. TKE values were small at night when atmospheric conditions are more stable than during the daytime.

Wind-generated gravity-capillary waves: laboratory measurements of temporal growth rates using microwave backscatter

1975 ◽  
Vol 70 (3) ◽  
pp. 417-436 ◽  
Author(s):  
T. R. Larson ◽  
J. W. Wright
Keyword(s):  
Growth Rates ◽  
Water Waves ◽  
Friction Velocity ◽  
Capillary Waves ◽  
Spectral Amplitude ◽  
Bragg Scattering ◽  
Laboratory Measurements ◽  
Wind Speeds ◽  
Tightly Coupled ◽  
Microwave Backscatter

The growth rates of wind-induced water waves at fixed fetch were measured in a laboratory wave tank using microwave backscatter. The technique strongly filters out all wavenumber component pairs except for a narrow window at the resonant Bragg scattering conditions. For these waves the spectral amplitude was measured as a function of the time after a fixed wind was abruptly started. The radars were aligned to respond to waves travelling in the downwind direction at wavelengths of 0·7-7 cm. Wind speeds ranged from 0·5 to 15 m/s. Fetches of 1·0, 3·0 and 8·4 m were used. In every case, the spectral amplitude initially grew at a single exponential rate β over several orders of magnitude, and then abruptly ceased growing. No dependence of the growth rate on fetch was observed. For all wavelengths and wind speeds the data can be fitted by \[ \beta (k,u_{*},{\rm fetch})=f(k)\,u^n_{*}, \] with n = 1·484 ± 0·027. Here u* is the friction velocity obtained from vertical profiles of mean horizontal velocity. For each wind speed, f(k) had a relative maximum near k = kn ≃ 3·6 cm−1. Rough estimates of β/2ω, where ω is the water wave frequency, and of the wind stress supported by short waves indicate that the observed growth rates are qualitatively very large. These waves are tightly coupled to the wind, and play a significant role in the transfer of momentum from wind to water.

scholarly journals Radar measurements of blowing snow off a mountain ridge

2020 ◽  
Vol 14 (6) ◽  
pp. 1779-1794
Author(s):  
Benjamin Walter ◽  
Hendrik Huwald ◽  
Josué Gehring ◽  
Yves Bühler ◽  
Michael Lehning
Keyword(s):  
Wind Velocity ◽  
Order Approximation ◽  
Sonic Anemometer ◽  
Snow Accumulation ◽  
Travel Distance ◽  
Storm Event ◽  
Height Distribution ◽  
Blowing Snow ◽  
Mountain Ridge ◽  
Mountainous Regions

Abstract. Modelling and forecasting wind-driven redistribution of snow in mountainous regions with its implications on avalanche danger, mountain hydrology or flood hazard is still a challenging task often lacking in essential details. Measurements of drifting and blowing snow for improving process understanding and model validation are typically limited to point measurements at meteorological stations, providing no information on the spatial variability of horizontal mass fluxes or even the vertically integrated mass flux. We present a promising application of a compact and low-cost radar system for measuring and characterizing larger-scale (hundreds of metres) snow redistribution processes, specifically blowing snow off a mountain ridge. These measurements provide valuable information of blowing snow velocities, frequency of occurrence, travel distances and turbulence characteristics. Three blowing snow events are investigated, two in the absence of precipitation and one with concurrent precipitation. Blowing snow velocities measured with the radar are validated by comparison against wind velocities measured with a 3D ultra-sonic anemometer. A minimal blowing snow travel distance of 60–120 m is reached 10–20 % of the time during a snow storm, depending on the strength of the storm event. The relative frequency of transport distances decreases exponentially above the minimal travel distance, with a maximum measured distance of 280 m. In a first-order approximation, the travel distance increases linearly with the wind velocity, allowing for an estimate of a threshold wind velocity for snow particle entrainment and transport of 7.5–8.8 m s−1, most likely depending on the prevailing snow cover properties. Turbulence statistics did not allow a conclusion to be drawn on whether low-level, low-turbulence jets or highly turbulent gusts are more effective in transporting blowing snow over longer distances, but highly turbulent flows are more likely to bring particles to greater heights and thus influence cloud processes. Drone-based photogrammetry measurements of the spatial snow height distribution revealed that increased snow accumulation in the lee of the ridge is the result of the measured local blowing snow conditions.

scholarly journals A Hierarchical Deep Fusion Framework for Egocentric Activity Recognition Using a Wearable Hybrid Sensor System

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 546 ◽  
Author(s):  
Haibin Yu ◽  
Guoxiong Pan ◽  
Mian Pan ◽  
Chong Li ◽  
Wenyan Jia ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Short Term Memory ◽  
Specific Activity ◽  
Negative Influence ◽  
Sensor System ◽  
Sensor Data ◽  
Motion Sensor ◽  
Motion Sensors ◽  
Motion State ◽  
Fusion Framework

Recently, egocentric activity recognition has attracted considerable attention in the pattern recognition and artificial intelligence communities because of its wide applicability in medical care, smart homes, and security monitoring. In this study, we developed and implemented a deep-learning-based hierarchical fusion framework for the recognition of egocentric activities of daily living (ADLs) in a wearable hybrid sensor system comprising motion sensors and cameras. Long short-term memory (LSTM) and a convolutional neural network are used to perform egocentric ADL recognition based on motion sensor data and photo streaming in different layers, respectively. The motion sensor data are used solely for activity classification according to motion state, while the photo stream is used for further specific activity recognition in the motion state groups. Thus, both motion sensor data and photo stream work in their most suitable classification mode to significantly reduce the negative influence of sensor differences on the fusion results. Experimental results show that the proposed method not only is more accurate than the existing direct fusion method (by up to 6%) but also avoids the time-consuming computation of optical flow in the existing method, which makes the proposed algorithm less complex and more suitable for practical application.

scholarly journals Turbulence fluxes and variances measured with a sonic anemometer mounted on a tethered balloon

2016 ◽  
Vol 9 (9) ◽  
pp. 4375-4386 ◽  
Author(s):  
Guylaine Canut ◽  
Fleur Couvreux ◽  
Marie Lothon ◽  
Dominique Legain ◽  
Bruno Piguet ◽  
...  
Keyword(s):  
Boundary Layer ◽  
High Frequency ◽  
Field Experiments ◽  
Sonic Anemometer ◽  
Motion Sensor ◽  
Tethered Balloon ◽  
Inertial Motion ◽  
Convective Boundary ◽  
Momentum Fluxes ◽  
Field Campaigns

Abstract. This study presents the first deployment in field campaigns of a balloon-borne turbulence probe, developed with a sonic anemometer and an inertial motion sensor suspended below a tethered balloon. This system measures temperature and horizontal and vertical wind at high frequency and allows the estimation of heat and momentum fluxes as well as turbulent kinetic energy in the lower part of the boundary layer. The system was validated during three field experiments with different convective boundary-layer conditions, based on turbulent measurements from instrumented towers and aircraft.

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