Understanding complex wind fields with single Doppler Lidar techniques

Scanning Wind Doppler lidars are more and more used for operational applications for which the retrieval of wind vectors and the turbulence quantities at specific points or at high resolution in an area of interest can bring valuable information. For retrieving volume wind data, many techniques can be used to retrieve wind vectors from the combination of radial wind data at different distances and lines of sight, as the well-known Volume Velocity Processing (VVP) technique, developed formerly for Doppler Radars. Although More sophisticated techniques based on the coupling with computational fluid dynamic models exist. e, a processing based on the VVP technique, called the Volume Wind processing (VW) has been developed. This algorithm includes a Single Value Decomposition algorithm that ensures a robust filtering of the retrieved wind data, and then high accuracy and precision. In addition, radial velocity variance (RVV) algorithm has been developped to remove the constant wind speed over a scan to provide fields of turbulent radial wind speeds. Those fields can be averaged and their variances can be computed to determine the averaged turbulent radial wind speeds as well as its deviation.In this study, the principle and the validation of the VW algorithm will be presented at different sites (offshore, rural, and urban) against reference anemometers will be firstly presented. The bias and precision on 10 minutes averaged wind speed obtained are both about 0.5 m/s. The results reveal that the accuracy is significantly better for winds along lidar beam and precision better for larger samples of instantaneous wind speeds in the averaging time. Finally, the Volume Wind and the Radial Velocity Variance algorithms have then been applied to datasets of several months measured with Windcube Scanning Lidars at two different sites characterized by complex terrain and buildings. The data processed by the algorithms are analyzed to better understand the heterogeneities of the wind fields and its variations in time.

Download Full-text

Statistical Downscaling of Gridded Wind Speed Data Using Local Topography

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0054.1 ◽

2017 ◽

Vol 18 (2) ◽

pp. 335-348 ◽

Cited By ~ 12

Author(s):

Adam Winstral ◽

Tobias Jonas ◽

Nora Helbig

Keyword(s):

Wind Speed ◽

Statistical Downscaling ◽

Surface Wind ◽

Horizontal Resolution ◽

Wind Data ◽

Wind Fields ◽

Forecast Performance ◽

Wind Speeds ◽

Mountain Environments ◽

High Winds

Abstract Winds, particularly high winds, strongly affect snowmelt and snow redistribution. High winds during rain-on-snow events can lead to catastrophic flooding while strong redistribution events in mountain environments can generate dangerous avalanche conditions. To provide adequate warnings, accurate wind data are required. Yet, mountain wind fields exhibit a high degree of heterogeneity at small spatial lengths that are not resolved by currently available gridded forecast data. Wind data from over 200 stations across Switzerland were used to evaluate two forecast surface wind products (~2- and 7-km horizontal resolution) and develop a statistical downscaling technique to capture these finer-scaled heterogeneities. Wind exposure metrics derived from a 25-m horizontal resolution digital elevation model effectively segregated high, moderate, and low wind speed sites. Forecast performance was markedly compromised and biased low at the exposed sites and biased high at the sheltered, valley sites. It was also found that the variability of predicted wind speeds at these sites did not accurately represent the observed variability. A novel optimization scheme that accounted for local terrain structure while also nudging the forecasted distributions to better match the observed distributions and variability was developed. The resultant statistical downscaling technique notably decreased biases across a range of elevations and exposures and provided a better match to observed wind speed distributions.

Download Full-text

A spatial evaluation of high-resolution wind fields from empirical and dynamical modeling in hilly and mountainous terrain

10.5194/gmd-2018-238 ◽

2018 ◽

Author(s):

Christoph Schlager ◽

Gottfried Kirchengast ◽

Juergen Fuchsberger ◽

Alexander Kann ◽

Heimo Truhetz

Keyword(s):

High Resolution ◽

Wind Speed ◽

Climate Model ◽

Strong Wind ◽

Wind Fields ◽

Dynamical Modeling ◽

Thermally Induced ◽

Wind Speeds ◽

Error Measures ◽

Wind Events

Abstract. Empirical high-resolution surface wind fields, automatically generated by a weather diagnostic application, the WegenerNet Wind Product Generator (WPG), were intercompared with wind field analysis data from the Integrated Nowcasting through Comprehensive Analysis (INCA) system and with dynamical climate model wind field data from the non-hydrostatic climate model COSMO-CLM. The INCA analysis fields are available at a horizontal grid spacing of 1 km x 1 km, whereas the COSMO model fields are from simulations at a 3 km x 3 km grid. The WPG, developed by Schlager et al. (2017, 2018), generates diagnostic fields at a high resolution grid of 100 m x 100 m, using observations from two dense meteorological station networks: The WegenerNet Feldbach Region (FBR) and its alpine sister network, the WegenerNet Johnsbachtal (JBT). The high-density WegenerNet FBR is located in southeastern Styria, Austria, a region predominated by a hilly terrain and small differences in altitude. The network consists of more than 150 meteorological stations. The WegenerNet JBT contains eleven meteorological stations at elevations ranging from about 600 m to 2200 m in a mountainous region in northern Styria. The wind fields of these different empirical/dynamical modeling approaches were intercompared for thermally induced and strong wind events, using hourly temporal resolutions as supplied by the WPG, with the focus on evaluating spatial differences and displacements between the different datasets. For this comparison, a novel neighborhood-based spatial wind verification methodology based on fractions skill socres (FSS) is used to estimate the modeling performances. All comparisons show an increasing FSS with increasing neighborhood size. In general, the spatial verification indicates a better statistical agreement for the hilly WegenerNet FBR than for the mountainous WegenerNet JBT. The results for the WegenerNet FBR show a better agreement between INCA and WegenerNet than between COSMO and WegenerNet wind fields, especially for large scales (neighborhoods). In particular, COSMO-CLM clearly underperforms in case of thermally induced wind events. For the JBT region, all spatial comparisons indicate little overlap at small neighborhood sizes and in general large biases of wind vectors occur between the dynamical (COSMO) and analysis (INCA) fields and the diagnostic (WegenerNet) reference dataset. Furthermore, gridpoint-based error measures were calculated for the same evaluation cases. The statistical agreement, estimated for the vector-mean wind speed and wind directions show again a better agreement for the WegenerNet FBR than for the WegenerNet JBT region. In general, the difference between modeled and observed wind directions is smaller for strong wind speed events than for thermally induced ones. A combined examination of all spatial and gridpoint-based error measures shows that COSMO-CLM with its limited horizontal resolution of 3 km x 3 km and hence, a too smoothed orography, is not able to represent small-scale wind patterns. The results for the JBT region indicate that the INCA analysis fields generally overestimate wind speeds in the summit regions. For strong wind speed events the wind speed in the valleys is underestimated by INCA, however. Regarding the WegenerNet diagnostic wind fields, the statistics show decent performance in the FBR and somewhat overestimated wind speeds for strong wind speed events in the Enns valley of the JBT region.

Download Full-text

Alat Ukur Kecepatan Angin Permukaan Air Laut Berbasis Mikrokontroler

Barakuda 45: Jurnal Ilmu Perikanan dan Kelautan ◽

10.47685/barakuda45.v2i2.90 ◽

2020 ◽

Vol 2 (2) ◽

pp. 80-88

Author(s):

Waluyo Waluyo ◽

Meli Ruslinar

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

Wind Data ◽

Measuring Instrument ◽

Ocean Engineering ◽

Sea Surface Wind Speed ◽

Wind Speeds ◽

Sea Surface Wind ◽

Engineering Modeling

The microcontroller is one technology that is developing so rapidly with various types and functions, one of which is Arduino Uno which can be used as a microcontroller for various functions in the field of electronics technology. This research was conducted at the Laboratory of Ocean Engineering Modeling, Marine and Fisheries Polytechnic of Karawang in March-June 2020. The purpose of this study was to create a microcontroller-based sea surface wind speed measuring instrument. Based on the results of the acquisition of wind data using a fan simulation and natural wind gusts with different wind speeds in the field show a significant tool response. The results of the comparison of data recording between the results of research with the existing wind speed measuring instrument show that there is an average tool error of 3.24%, a relative error of 3.78%, and an instrument accuracy rate of 96.76%. Thus it can be said that the ability of the tool is able to record wind data with high accuracy.

Download Full-text

Quantification of NOX Emissions Based on Car MAX-DOAS Measurements over Beijing and Impacts of Wind Field

10.5194/egusphere-egu2020-12459 ◽

2020 ◽

Author(s):

Xinghong Cheng

Keyword(s):

Spatial Distribution ◽

Wind Speed ◽

Wind Field ◽

Meteorological Condition ◽

Emission Rates ◽

Wind Fields ◽

Vertical Column ◽

Wind Speeds ◽

South Central ◽

The Mean

We carried out 14 days of Car MAX-DOAS experiments on the 6th Ring Rd of Beijing in January, September and October, 2014. The tropospheric vertical column densities (VCD) of NO2 are retrieved and used to estimate the emissions of NOx. The offline LAPS-WRF-CMAQ model system is used to simulate wind fields by assimilation of observational data and calculate the NO2 to NOx concentration ratios. The NOX emissions in Beijing for different seasons derived from Car MAX-DOAS measurements are compared with the multi-resolution emission inventory in China for 2012 (MEIC 2012), and impacts of wind field on estimated emissions and its uncertainties are also investigated. Results show that the NO2 VCD is higher in January than other two months and it is typically larger at the southern parts of the 6th Ring Road than the northern parts of it. Wind field has obvious impacts on the spatial distribution of NO2 VCD, and the mean NO2 VCD with south wind at most sampling points along the 6th Ring Rd is higher than north wind. The journey-to-journey variation pattern of estimated NOX emissions rates (ENOX) is consistent with that of the NO2 VCD, and ENOX is mainly determined by the NO2 VCD. In addition, the journey-to-journey ENOX in the same month is different and it is affected by wind speed, the ratio of NO2 and NOx concentration and the decay rate of NOX from the emission sources to measured positions under different meteorological condition. The ENOX ranges between 6.46&#215;1025 and 50.05&#215;1025 molec s-1. The averaged ENOX during every journey in January, September and October are respectively 35.87&#215;1025, 20.34&#215;1025, 8.96&#215;1025 molec s-1. The estimated ENOX after removing the simulated error of wind speed and observed deviation of NO2 VCD are found to be mostly closer to the MEIC 2012, but sometimes ENOX is lower or higher and it indicates that the MEIC 2012 might be overestimate or underestimate the true emissions. The estimated ENOX on January 27 and September 19 are obviously higher than other journeys in the same month because the mean NO2 VCD and Leighton ratio during these two periods are larger, and corresponding wind speeds are smaller. Additionally, because south wind may affect the spatial distribution of mean NO2 VCD in Beijing which is downwind of south-central&#160;regions of Hebei province with high source emission rates, the uncertainty of the estimated ENOX with south wind will be increased.

Download Full-text

Height Extrapolation of Wind Data

Journal of Solar Energy Engineering ◽

10.1115/1.3267645 ◽

1985 ◽

Vol 107 (1) ◽

pp. 10-14 ◽

Cited By ~ 17

Author(s):

A. S. Mikhail

Keyword(s):

Wind Speed ◽

Power Law ◽

Wind Data ◽

Power Law Model ◽

Short Term ◽

Shape Factors ◽

Wind Speeds ◽

Average Wind ◽

Modified Power Law

Various models that are used for height extrapolation of short and long-term averaged wind speeds are discussed. Hourly averaged data from three tall meteorological towers (the NOAA Erie Tower in Colorado, the Battelle Goodnoe Hills Tower in Washington, and the WKY-TV Tower in Oklahoma), together with data from 17 candidate sites (selected for possible installation of large WECS), were used to analyze the variability of short-term average wind shear with atmospheric and surface parameters and the variability of the long-term Weibull distribution parameter with height. The exponents of a power-law model, fit to the wind speed profiles at the three meteorological towers, showed the same variability with anemometer level wind speed, stability, and surface roughness as the similarity law model. Of the four models representing short-term wind data extrapolation with height (1/7 power law, logarithmic law, power law, and modified power law), the modified power law gives the minimum rms for all candidate sites for short-term average wind speeds and the mean cube of the speed. The modified power-law model was also able to predict the upper-level scale factor for the WKY-TV and Goodnoe Hills Tower data with greater accuracy. All models were not successful in extrapolation of the Weibull shape factors.

Download Full-text

Wind tunnel tests on the characteristics of wind fields over a simplified gorge

Advances in Structural Engineering ◽

10.1177/1369433216680635 ◽

2016 ◽

Vol 20 (10) ◽

pp. 1599-1611 ◽

Cited By ~ 1

Author(s):

Peng Hu ◽

Yongle Li ◽

Yan Han ◽

CS Cai ◽

Guoji Xu

Keyword(s):

Wind Speed ◽

Wind Tunnel ◽

Wind Direction ◽

Wind Field ◽

Small Range ◽

Wind Fields ◽

Wind Speeds ◽

Mean Wind Speed ◽

Wind Characteristics ◽

The Mean

Characteristics of wind fields over the gorge or valley terrains are becoming more and more important to the structural wind engineering. However, the studies on this topic are very limited. To obtain the fundamental characteristics information about the wind fields over a typical gorge terrain, a V-shaped simplified gorge, which was abstracted from some real deep-cutting gorges where long-span bridges usually straddle, was introduced in the present wind tunnel studies. Then, the wind characteristics including the mean wind speed, turbulence intensity, integral length scale, and the wind power spectrum over the simplified gorge were studied in a simulated atmospheric boundary layer. Furthermore, the effects of the oncoming wind field type and oncoming wind direction on these wind characteristics were also investigated. The results show that compared with the oncoming wind, the wind speeds at the gorge center become larger, but the turbulence intensities and the longitudinal integral length scales become smaller. Generally, the wind fields over the gorge terrain can be approximately divided into two layers, that is, the gorge inner layer and the gorge outer layer. The different oncoming wind field types have remarkable effects on the mean wind speed ratios near the ground. When the angle between the oncoming wind and the axis of the gorge is in a certain small range, such as smaller than 10°, the wind fields are very close to those associated with the wind direction of 0°. However, when the angle is in a larger range, such as larger than 20°, the wind fields in the gorge will significantly change. The research conclusions can provide some references for civil engineering practices regarding the characteristics of wind fields over the real gorge terrains.

Download Full-text

Multipoint Reconstruction of Wind Speeds

10.5194/wes-2020-5 ◽

2020 ◽

Author(s):

Christian Behnken ◽

Matthias Wächter ◽

Joachim Peinke

Keyword(s):

Wind Speed ◽

Time Scales ◽

Numerical Solutions ◽

Wind Data ◽

Conditional Probability Density ◽

Wind Speeds ◽

Fokker Planck Equations ◽

Short Time ◽

Speed Fluctuations ◽

Fokker Planck

Abstract. The most intermittent behavior of atmospheric turbulence is found for very short time scales. Based on a concatenation of conditional probability density functions (cpdfs) of nested wind speeds increments, inspired by a Markov process in scale, we derive a short-time predictor for wind speed fluctuations around a non-stationary mean value and with a corresponding non-stationary variance. As a new quality this short time predictor enables a multipoint reconstruction of wind data. The used cpdfs are (1) directly estimated from historical data from the offshore research platform FINO1 and (2) obtained from numerical solutions of a family of Fokker-Planck equations in the scale domain. The explicit forms of the Fokker-Planck equations are estimated from the given wind data. A good agreement between the statistics of the generated synthetic wind speed fluctuations and the measured is found even on time scales below 1 s. This shows that our approach captures the short-time dynamics of real wind speed fluctuations very well. Our method is extended by taking the non-stationarity of the mean wind speed and its non-stationary variance into account.

Download Full-text

Performance evaluation of Weibull function for wind data analysis in two selected locations in North-Western, Nigeria

International Journal of Physical Research ◽

10.14419/ijpr.v6i1.9053 ◽

2018 ◽

Vol 6 (1) ◽

pp. 18

Author(s):

Boluwaji Olomiyesan

Keyword(s):

Wind Speed ◽

Wind Power ◽

Predictive Ability ◽

Maximum Energy ◽

Wind Data ◽

Weibull Function ◽

Wind Speed Data ◽

Wind Speeds ◽

The North ◽

North Western

In this study, the predictive ability of two-parameter Weibull distribution function in analyzing wind speed data was assessed in two selected sites with different mean wind speeds in the North-Western region of Nigeria. Twenty-two years wind speed data spanning from 1984 to 2005 was used in the analysis. The data were obtained from the Nigerian Meteorological Agency (NIMET) in Lagos. The results of the analysis show that Weibull function is suitable for analyzing measured wind speed data and in predicting the wind-power density in both locations and that Weibull function is not discriminative between locations with high and low mean wind speeds in analyzing wind data. The annual mean wind speeds for the two sites (Sokoto and Yelwa) are 7.99 ms-1 and 2.59 ms-1 respectively, while the annual values of the most probable wind speed and the maximum, energy-carrying wind speeds are respectively:3.52 and 4.34 ms-1 for Yelwa and 8.33 and 9.02 ms-1 for Sokoto. The estimated annual wind power densities for Yelwa and Sokoto are respectively 36.91 and 359.96 Wm-2. Therefore, Sokoto has a better prospect for wind power generation.

Download Full-text

Comparison of Weibull and Gaussian Mixture Models for Wind Speed Data Analysis

International Journal of Economic and Environmental Geology ◽

10.46660/ijeeg.vol11.iss1.2020.405 ◽

2020 ◽

Vol 11 (1) ◽

pp. 10-16

Author(s):

Muhammad Shoaib ◽

Saif Ur Rehman ◽

Imran Siddiqui ◽

Shafiqur Rehman ◽

Shamim Khan ◽

...

Keyword(s):

Wind Speed ◽

Wind Power ◽

Gaussian Mixture ◽

Descriptive Statistics ◽

Wind Data ◽

Weibull Function ◽

Data Sets ◽

Data Set ◽

Wind Speeds ◽

Power Densities

In order to have a reliable estimate of wind energy potential of a site, high frequency wind speed and direction data recorded for an extended period of time is required. Weibull distribution function is commonly used to approximate the recorded data distribution for estimation of wind energy. In the present study a comparison of Weibull function and Gaussian mixture model (GMM) as theoretical functions are used. The data set used for the study consists of hourly wind speeds and wind directions of 54 years duration recorded at Ijmuiden wind site located in north of Holland. The entire hourly data set of 54 years is reduced to 12 sets of hourly averaged data corresponding to 12 months. Authenticity of data is assessed by computing descriptive statistics on the entire data set without average and on monthly 12 data sets. Additionally, descriptive statistics show that wind speeds are positively skewed and most of the wind data points are observed to be blowing in south-west direction. Cumulative distribution and probability density function for all data sets are determined for both Weibull function and GMM. Wind power densities on monthly as well as for the entire set are determined from both models using probability density functions of Weibull function and GMM. In order to assess the goodness-of-fit of the fitted Weibull function and GMM, coefficient of determination (R2) and Kolmogorov-Smirnov (K-S) tests are also determined. Although R2 test values for Weibull function are much closer to ‘1’ compared to its values for GMM. Nevertheless, overall performance of GMM is superior to Weibull function in terms of estimated wind power densities using GMM which are in good agreement with the power densities estimated using wind data for the same duration. It is reported that wind power densities for the entire wind data set are 307 W/m2 and 403.96 W/m2 estimated using GMM and Weibull function, respectively.

Download Full-text

Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India

Energies ◽

10.3390/en12112158 ◽

2019 ◽

Vol 12 (11) ◽

pp. 2158 ◽

Cited By ~ 5

Author(s):

Mekalathur B Hemanth Kumar ◽

Saravanan Balasubramaniyan ◽

Sanjeevikumar Padmanaban ◽

Jens Bo Holm-Nielsen

Keyword(s):

Wind Speed ◽

Frequency Distribution ◽

Wind Direction ◽

Optimization Method ◽

Wind Data ◽

Gray Wolf ◽

Energy Potential ◽

Weibull Parameters ◽

Wind Speeds ◽

Wind Potential

In this paper the multiverse optimization (MVO) was used for estimating Weibull parameters. These parameters were further used to analyze the wind data available at a particular location in the Tirumala region in India. An effort had been made to study the wind potential in this region (13°41′30.4″ N 79°21′34.4″ E) using the Weibull parameters. The wind data had been measured at this site for a period of six years from January 2012 to December 2017. The analysis was performed at two different hub heights of 10 m and 65 m. The frequency distribution of wind speed, wind direction and mean wind speeds were calculated for this region. To compare the performance of the MVO, gray wolf optimizer (GWO), moth flame optimization (MFO), particle swarm optimization (PSO) and other numerical methods were considered. From this study, the performance had been analyzed and the best results were obtained by using the MVO with an error less than one. Along with the Weibull frequency distribution for the selected region, wind direction and wind speed were also provided. From the analysis, wind speed from 2 m/s to 10 m/s was present in sector 260–280° and wind from 0–4 m/s were present in sector 170–180° of the Tirumala region in India.

Download Full-text