Coordinate Rotation–Amplification in the Uncertainty and Bias in Non-orthogonal Sonic Anemometer Vertical Wind Speeds

2020 ◽  
Vol 175 (2) ◽  
pp. 203-235 ◽  
Author(s):  
John M. Frank ◽  
William J. Massman ◽  
W. Stephen Chan ◽  
Keith Nowicki ◽  
Scot C. R. Rafkin
Keyword(s):  
Sonic Anemometer ◽  
Vertical Wind ◽  
Wind Speeds ◽  
Coordinate Rotation

scholarly journals Improved Wind and Turbulence Measurements Using a Low-Cost 3-D Sonic Anemometer at a Low-Wind Site

2008 ◽  
Vol 2 (1) ◽  
pp. 131-138 ◽  
Author(s):  
Brent M. Bowen
Keyword(s):  
Standard Deviation ◽  
Wind Direction ◽  
Sonic Anemometer ◽  
Vertical Wind ◽  
Wind Speeds ◽  
Low Wind Speeds ◽  
Mechanical Sensors ◽  
Wind Vane ◽  
Cup Anemometer ◽  
Propeller Anemometer

A year of data from sonic anemometer and mechanical wind sensors was analyzed and compared at a low-wind site. Results indicate that 15-minute average and peak 1-second wind speeds (u) from the sonic agree well with data derived from a co-located cup anemometer over a wide range of speeds. Wind direction data derived from the sonic also agree closely with those from a wind vane except for very low wind speeds. Values of standard deviation of longitudinal wind speed (σu) and wind direction fluctuations (σø) from the sonic and mechanical sensors agree well for times with u > 2 ms-1 but show significant differences with lower u values. The most significant differences are associated with the standard deviation of vertical wind fluctuations (σw): the co-located vertical propeller anemometer yields values increasingly less than those measured by the sonic anemometer as u decreases from 2.5 approaching 0 ms-1. The combination of u over-estimation and under-estimation of σw from the mechanical sensors at low wind speeds causes considerable underestimation of the standard deviation of vertical wind angle fluctuations (σø), an indicator of vertical dispersion. Calculations of σø from sonic anemometer measurements are typically 5° to 10° greater than from the mechanical sensors when the mechanical instruments indicate that σø < 5° or so. The errors with the propeller anemometer, cup anemometer and wind vane, caused by their inability to respond to higher frequency (smaller scale) turbulent fluctuations, can therefore lead to large (factors of 2 to 10 or more) errors in both the vertical and horizontal dispersion during stable conditions with light winds. The sonic anemometer clearly provides more accurate and reliable wind data than the mechanical wind sensor with u < 2.5 ms-1

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.

Correlated Vertical Wind Speeds in a Spruce Canopy

1985 ◽  
pp. 543-561 ◽  
Author(s):  
J. M. Crowther ◽  
N. J. Hutchings
Keyword(s):  
Vertical Wind ◽  
Wind Speeds

scholarly journals Coastal Wind Measurements Using a Single Scanning LiDAR

2020 ◽  
Vol 12 (8) ◽  
pp. 1347 ◽  
Author(s):  
Susumu Shimada ◽  
Jay Prakash Goit ◽  
Teruo Ohsawa ◽  
Tetsuya Kogaki ◽  
Satoshi Nakamura
Keyword(s):  
Wind Speed ◽  
Goodness Of Fit ◽  
Sonic Anemometer ◽  
Data Availability ◽  
Horizontal Wind ◽  
Research Station ◽  
Lidar Measurement ◽  
Wind Speeds ◽  
Wind Measurements ◽  
Scanning Lidar

A wind measurement campaign using a single scanning light detection and ranging (LiDAR) device was conducted at the Hazaki Oceanographical Research Station (HORS) on the Hazaki coast of Japan to evaluate the performance of the device for coastal wind measurements. The scanning LiDAR was deployed on the landward end of the HORS pier. We compared the wind speed and direction data recorded by the scanning LiDAR to the observations obtained from a vertical profiling LiDAR installed at the opposite end of the pier, 400 m from the scanning LiDAR. The best practice for offshore wind measurements using a single scanning LiDAR was evaluated by comparing results from a total of nine experiments using several different scanning settings. A two-parameter velocity volume processing (VVP) method was employed to retrieve the horizontal wind speed and direction from the radial wind speed. Our experiment showed that, at the current offshore site with a negligibly small vertical wind speed component, the accuracy of the scanning LiDAR wind speeds and directions was sensitive to the azimuth angle setting, but not to the elevation angle setting. In addition to the validations for the 10-minute mean wind speeds and directions, the application of LiDARs for the measurement of the turbulence intensity (TI) was also discussed by comparing the results with observations obtained from a sonic anemometer, mounted at the seaward end of the HORS pier, 400 m from the scanning LiDAR. The standard deviation obtained from the scanning LiDAR measurement showed a greater fluctuation than that obtained from the sonic anemometer measurement. However, the difference between the scanning LiDAR and sonic measurements appeared to be within an acceptable range for the wind turbine design. We discuss the variations in data availability and accuracy based on an analysis of the carrier-to-noise ratio (CNR) distribution and the goodness of fit for curve fitting via the VVP method.

scholarly journals A Bayesian model to correct underestimated 3-D wind speeds from sonic anemometers increases turbulent components of the surface energy balance

2016 ◽  
Vol 9 (12) ◽  
pp. 5933-5953 ◽  
Author(s):  
John M. Frank ◽  
William J. Massman ◽  
Brent E. Ewers
Keyword(s):  
Heat Flux ◽  
Energy Balance ◽  
Bayesian Model ◽  
Field Experiments ◽  
Sonic Anemometer ◽  
Vertical Wind ◽  
State Variables ◽  
Data Set ◽  
Sonic Anemometers ◽  
Posterior Correction

Abstract. Sonic anemometers are the principal instruments in micrometeorological studies of turbulence and ecosystem fluxes. Common designs underestimate vertical wind measurements because they lack a correction for transducer shadowing, with no consensus on a suitable correction. We reanalyze a subset of data collected during field experiments in 2011 and 2013 featuring two or four CSAT3 sonic anemometers. We introduce a Bayesian analysis to resolve the three-dimensional correction by optimizing differences between anemometers mounted both vertically and horizontally. A grid of 512 points (∼ ±5° resolution in wind location) is defined on a sphere around the sonic anemometer, from which the shadow correction for each transducer pair is derived from a set of 138 unique state variables describing the quadrants and borders. Using the Markov chain Monte Carlo (MCMC) method, the Bayesian model proposes new values for each state variable, recalculates the fast-response data set, summarizes the 5 min wind statistics, and accepts the proposed new values based on the probability that they make measurements from vertical and horizontal anemometers more equivalent. MCMC chains were constructed for three different prior distributions describing the state variables: no shadow correction, the Kaimal correction for transducer shadowing, and double the Kaimal correction, all initialized with 10 % uncertainty. The final posterior correction did not depend on the prior distribution and revealed both self- and cross-shadowing effects from all transducers. After correction, the vertical wind velocity and sensible heat flux increased  ∼ 10 % with  ∼ 2 % uncertainty, which was significantly higher than the Kaimal correction. We applied the posterior correction to eddy-covariance data from various sites across North America and found that the turbulent components of the energy balance (sensible plus latent heat flux) increased on average between 8 and 12 %, with an average 95 % credible interval between 6 and 14 %. Considering this is the most common sonic anemometer in the AmeriFlux network and is found widely within FLUXNET, these results provide a mechanistic explanation for much of the energy imbalance at these sites where all terrestrial/atmospheric fluxes of mass and energy are likely underestimated.

scholarly journals The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds

2020 ◽  
Author(s):  
Nicola Bodini ◽  
Mike Optis
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Power Law ◽  
Wind Farm ◽  
Sonic Anemometer ◽  
Model Performance ◽  
Learning Model ◽  
Round Robin ◽  
Wind Speeds ◽  
Machine Learning Model

Abstract. The extrapolation of wind speeds measured at a meteorological mast to wind turbine hub heights is a key component in a bankable wind farm energy assessment and a significant source of uncertainty. Industry-standard methods for extrapolation include the power law and logarithmic profile. The emergence of machine-learning applications in wind energy has led to several studies demonstrating substantial improvements in vertical extrapolation accuracy in machine-learning methods over these conventional power law and logarithmic profile methods. In all cases, these studies assess relative model performance at a measurement site where, critically, the machine-learning algorithm requires knowledge of the hub-height wind speeds in order to train the model. This prior knowledge provides fundamental advantages to the site-specific machine-learning model over the power law and log profile, which, by contrast, are not highly tuned to hub-height measurements but rather can generalize to any site. Furthermore, there is no practical benefit in applying a machine-learning model at a site where hub-height winds are known; rather, its performance at nearby locations (i.e., across a wind farm site) without hub-height measurements is of most practical interest. To more fairly and practically compare machine-learning-based extrapolation to standard approaches, we implemented a round-robin extrapolation model comparison, in which a random forest machine-learning model is trained and evaluated at different sites and then compared against the power law and logarithmic profile. We consider 20 months of lidar and sonic anemometer data collected at four sites between 50–100 kilometers apart in the central United States. We find that the random forest outperforms the standard extrapolation approaches, especially when incorporating surface measurements as inputs to include the influence of atmospheric stability. When compared at a single site (the traditional comparison approach), the machine-learning improvement in mean absolute error was 28 % and 23 % over the power law and logarithmic profile, respectively. Using the round-robin approach proposed here, this improvement drops to 19 % and 14 %, respectively. These latter values better represent practical model performance, and we conclude that round-robin validation should be the standard for machine-learning-based, wind-speed extrapolation methods.

scholarly journals A relaxed eddy accumulation system for measuring vertical fluxes of nitrous acid

2011 ◽  
Vol 4 (10) ◽  
pp. 2093-2103 ◽  
Author(s):  
X. Ren ◽  
J. E. Sanders ◽  
A. Rajendran ◽  
R. J. Weber ◽  
A. H. Goldstein ◽  
...  
Keyword(s):  
Wind Velocity ◽  
Nitrous Acid ◽  
Sonic Anemometer ◽  
Field Studies ◽  
Fast Response ◽  
Vertical Wind ◽  
Measured Temperature ◽  
Relaxed Eddy Accumulation ◽  
Vertical Fluxes ◽  
Vertical Wind Velocity

Abstract. A relaxed eddy accumulation (REA) system combined with a nitrous acid (HONO) analyzer was developed to measure atmospheric HONO vertical fluxes. The system consists of three major components: (1) a fast-response sonic anemometer measuring both vertical wind velocity and air temperature, (2) a fast-response controlling unit separating air motions into updraft and downdraft samplers by the sign of vertical wind velocity, and (3) a highly sensitive HONO analyzer based on aqueous long path absorption photometry that measures HONO concentrations in the updrafts and downdrafts. A dynamic velocity threshold (±0.5σw, where σw is a standard deviation of the vertical wind velocity) was used for valve switching determined by the running means and standard deviations of the vertical wind velocity. Using measured temperature as a tracer and the average values from two field deployments, the flux proportionality coefficient, β, was determined to be 0.42 ± 0.02, in good agreement with the theoretical estimation. The REA system was deployed in two ground-based field studies. In the California Research at the Nexus of Air Quality and Climate Change (CalNex) study in Bakersfield, California in summer 2010, measured HONO fluxes appeared to be upward during the day and were close to zero at night. The upward HONO flux was highly correlated to the product of NO2 and solar radiation. During the Biosphere Effects on Aerosols and Photochemistry Experiment (BEARPEX 2009) at Blodgett Forest, California in July 2009, the overall HONO fluxes were small in magnitude and were close to zero. Causes for the different HONO fluxes in the two different environments are briefly discussed.

Vertical wind speed extrapolation: Modelling using a response surface methodology (RSM) based on unconventional designs

2021 ◽  
pp. 0309524X2110463
Author(s):  
Feriel Adli ◽  
Nawel Cheggaga ◽  
Farouk Hannane ◽  
Leila Ouzeri
Keyword(s):  
Response Surface Methodology ◽  
Wind Speed ◽  
Response Surface ◽  
Vertical Wind ◽  
Industrial Processes ◽  
Wind Speed Profile ◽  
Wind Speeds ◽  
Vertical Wind Speed ◽  
Mathematical Techniques ◽  
Rsm Model

The main objective of this paper is to develop a predictive model of vertical wind speed profile. Response surface methodology (RSM) is used for this purpose. RSM is a set of statistical and mathematical techniques useful for the development, improvement and optimisation of processes. It is mainly used in industrial processes and is successfully applied in this paper to model the wind speed at the hub height of the wind turbine. An unconventional model is adopted due to the nature of the input parameters which cannot be controlled or modified. The model validation indicators, namely correlation coefficient ([Formula: see text]) and root mean square error (RMSE = 1.02), give excellent results when comparing predicted and measured wind speeds. For the same data, the RSM model gives a better RMSE compared to the conventional power law and the artificial neural network.

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