Long-term Wind Speed Research Articles

The Assessment of Wind Energy Potential within the Carpathian Region of Ukraine

Since the limited and incomplete information about wind energy potential is a serious obstacle to the development of renewable energy, it is necessary to assess wind resources in order to obtain the data about the spatial and temporal distribution characteristics of wind and its energy value to determine the feasibility of placing wind turbines (windmills). The aim of the study is to provide the assessment of wind energy potential of Carpathian region of Ukraine. The main materials for the studies have become observation data of the wind characteristics at 26 weather stations at administrative regions of Carpathian region of Ukraine (Lviv, Ivano-Frankivsk, Zakarpathian region and Chernivtsy) and 5 meteorological stations in neighbouring regions (Ternopil, Khmelnytsky) since 1899. The given comparison was made in order to assess the current state of the wind characteristics of climate change and their analysis of data from 2005-2015. We have obtained the following results. First of all, we have got the important characteristic of wind conditions on the territory which is wind speed. We established that long-term average wind speed in Carpathian region of Ukraine fluctuates within 1,0-3,7 m / s. We have built the map of the average annual wind speed in Carpathian region at the height of 100 m. We also provide the classification of promising areas for installation of wind turbines. Our conclusions are as follows. The results of research is based on getting the most favorable conditions for the development of industrial wind power are in the highlands of Carpathian region in the Ivano-Frankivsk and Zakarpathian regions and Roztocze in Lviv region. The medium power wind turbines are advisable to locate Podolsky hill in Lviv region and the sky Khotyn in Chernivtsy region. Favorable wind conditions for the wind turbines is small in most of Lviv region (especially the north-western and central areas), northern and south-western districts of Ivano-Frankivsk, Chernivtsy central and northern Transcarpathian regions.

Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm

An artificial neural network was used for forecasting of long-term wind speed data (24 and 48 hours ahead) in La Serena City (Chile). In order to obtain a more effective correlation and prediction, a particle swarm algorithm was implemented to update the weights of the network. 43800 data points of wind speed were used (years 2003- 2007), and the past values of wind speed, relative humidity, and air temperature were used as input parameters, considering that these meteorogical parameters are more readily available around the globe. Several neural network architectures were studied, and the optimum architecture was determined by adding neurons in systematic form and evaluating the root mean square error (RMSE) during the learning process. The results show that the meteorological variables used as input parameters, have influential effects on the good training and predicting capabilities of the chosen network, and that the hybrid neural network can forecast the hourly wind speed with acceptable accuracy, such as: RMSE=0.81 [m·s−1], MSE=0.65 [m·s−1] 2 and R2 =0.97 for 24-hours-ahead wind speed prediction, and RMSE=0.78, MSE=0.634 [m·s−1] 2 and R2 =0.97 for 48-hours-ahead wind speed prediction. Predicción a largo plazo de la velocidad de viento en la ciudad de La Serena (Chile) utilizando un algoritmo híbrido de red neuronal-enjambre de partículasResumenUna red neuronal artificial fue utilizada para la predicción de datos de la velocidad de viento a largo plazo (24 y 48 horas en adelanto) en la Ciudad de La Serena (Chile). Para obtener una efectiva correlación y predición, se implementó una optimización de enjambre de particulas para actualizar los pesos de la red. Se emplearon 43800 datos de velocidad de viento (años 2003-2007), y los valores pasados de velocidad del viento, humedad relativa y temperatura del aire fueron utilizados como parámetros de entrada, considerando que estos parámetros meteorológicos se encuentran fácilmente disponibles en todo el mundo. Se estudiaron varias arquitecturas de redes neuronales y la arquitectura optima se determine añadiendo neuronas de forma sistemática y evaluando la raíz del error cuadrático medio (RMSE) durante el proceso de aprendizaje. Los resultados muestran que las variables meteorológicas utilizadas como parámetros de entrada, tienen un efecto positivo sobre el correcto entrenamiento y capacidades predictivas de la red, y que la red neural híbrida puede pronosticar la velocidad del viento horaria con una precisión aceptable, como un RMSE=0.81 [m·s−1], MSE=0.65 [m·s−1] 2 y R2 =0.97 para la predicción de la velocidad del viento de 24 horas en adelanto, y un RMSE=0.78, MSE=0.634 [m·s−1] 2 and R2 =0.97 para la predicción de la velocidad del viento de 48 horas en adelanto

Evaluation of Wind Energy Potential as a Power Generation Source in Chad

Long-term wind speed data for thirteen meteorological stations, measured over a five-year period, were statistically analyzed using the two-parameter Weibull distribution function. The purpose of this study is to reveal for the first time the wind power potentials in Chad and to provide a comprehensive wind map of the country. The results show that the values of the shape and scale parameters varied over a wide range. Analysis of the seasonal variations showed that higher wind speed values occur when the weather condition is generally dry and they drop considerably when the weather condition is wet. It was also observed that the wind speed increases as one moves from the southern zone to the Saharan zone. Although the wind power at each site varies significantly, however, the potentials of most of the sites were encouraging. Nevertheless, according to the PNNL classification system, they are favorable for small-scale applications only. A few stations in the middle of Sudanian and Sahel regions are found to be not feasible for wind energy generation due to their poor mean wind speed. The prevailing wind direction for both Saharan and Sahel regions is dominated by northeastern wind, while it diverged to different directions in the Sudanian zone.

A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction

Wind forecasting is critical in the wind power industry, yet forecasting errors often exist. In order to effectively correct the forecasting error, this study develops a weather adapted bias correction scheme on the basis of an average bias-correction method, which considers the deviation of estimated biases associated with the difference in weather type within each unit of the statistical sample. This method is tested by an ensemble forecasting system based on the Weather Research and Forecasting (WRF) model. This system provides high resolution wind speed deterministic forecasts using 40 members generated by initial perturbations and multi-physical schemes. The forecasting system outputs 28–52 h predictions with a temporal resolution of 15 min, and is evaluated against collocated anemometer towers observations at six wind fields located on the east coast of China. Results show that the information contained in weather types produces an improvement in the forecast bias correction.

LONG TERM WIND SPEED PREDICTION USING WAVELET COEFFICIENTS AND SOFT COMPUTING

In the past researches, scholars have carried out short-term prediction for wind speed. The present work deals with long-term wind speed prediction, required for hybrid power generation design and contract planning. As the total database is quite large for long-term prediction, feature extraction of data by application of Lifting wavelet coefficients are exploited, along with soft computing techniques for time series data, which is scholastic in nature.

Preliminary assessment of wind power potential over the coastal region of Bheemunipatnam in northern Andhra Pradesh, India

This paper presents a preliminary Wind Power Potential (WPP) assessment for coastal site Bheemunipatnam located in the northern region of Andhra Pradesh, India. The novelty and major contribution of this paper stems from the fact that till date WPP has been done only up to the height of 80 m and no one has attempted it up to the height of 150 m in the steps of 10 m for said site. Power law has been used to estimate WPP at these heights. The paper will be a guiding document for policy makers, power and energy engineers as well as for researchers working in this area for providing solution to the problem of burgeoning gap between demand and supply of energy. The Cubic Factor (CF) method and Weibull model have been subjected in the present analysis. The long-term daily wind speed data for the period of 32 years (1983–2014) at a 10 m height has been collected from NASA website. In addition, Weibull and cumulative distribution functions have been determined at different heights. Finally, comparison of mean Wind Power Density (WPD) obtained from both the models has been statistically investigated, tested, and presented in section 5 and 6, respectively.

Long-Term Wind Speed Forecasting using Spiking Neural Network Optimized by Improved Modified Grey Wolf Optimization Algorithm.

26Jul 2016 Long-Term Wind Speed Forecasting using Spiking Neural Network Optimized by Improved Modified Grey Wolf Optimization Algorithm. M. Madhiarasan , and S. N. Deepa. Research Scholar (Ph.D), Department of Electrical and Electronics Engineering, Anna University Regional Campus, Coimbatore, Coimbatore – 641046, Tamil Nadu, India. Associate Professor, Department of Electrical and Electronics Engineering, Anna University Regional Campus, Coimbatore, Coimbatore – 641046, Tamil Nadu, India.

Lifetime estimation for IGBT modules in wind turbine power converter system considering ambient temperature

Power semiconductors in the wind turbine power converter system suffer from two-scale thermal loadings, the fundamental frequency thermal cycling caused by the output frequency of converter and the low frequency thermal cycling due to the variation of long-term wind speed. These two-scale thermal loadings introduce different consumed lifetimes. Accurate lifetime estimation in the wind power application is desired for reliability prediction and health management. This paper adopts the Bayerer lifetime model to evaluate the consumed lifetime of power semiconductors in wind power converter systems based on a numerical junction temperature calculation method. Lifetime estimation can be improved by taking into account the ambient temperature. Studies show that fluctuations of the ambient temperature increase the consumed lifetime due to the low frequency thermal cycling, but have little effect on the consumed lifetime due to the fundamental frequency thermal cycling. Our results also show that the consumed lifetime due to fundamental frequency thermal cycling mainly falls on the high wind speed area, whereas the consumed lifetime due to low frequency thermal cycling is clustered in the area due to large low frequency junction temperature fluctuations. The resulting distribution characteristics can be used in the thermal management for reliability improvement.

Evaluation of FAO-56 Penman-Monteith Model with Limited Data and the Valiantzas Models for Estimating Grass-Reference Evapotranspiration in Sahelian Conditions

Accurate determination of grass-reference evapotranspiration (ETo) is very important for agricultural, hydrological, and environmental studies, especially under increasing water scarcity and climate mitigation conditions. The FAO-56 Penman-Monteith (FAO-PM) model is renowned for being the most accurate model; however, it requires a full climate data set that is not always available at all weather stations, especially in most of the developing countries. Thus, using ETo models that require a reduced set of climate data continues to be an important alternative in such cases. The objectives of this study were to (1) evaluate the FAO-56 Penman-Monteith equation to estimate ETo with limited climate data, and (2) evaluate two of the latest ETo equations developed by Valiantzas. Climatic variables, including maximum and minimum air temperature, maximum and minimum relative humidity, solar radiation, and wind speed were collected from eight weather stations across Burkina Faso. The results showed that when solar radiation data were missing, solar radiation estimation from maximum and minimum daily air temperatures generated accurate estimates of ETo, with regression slope from 0.98 to 1.04, root mean squared error (RMSE) less than 0.60 mm/day, and low mean biased error (MBE) within the range of −0.18–0.02 mm/day. In the case of lacking relative humidity data, the RMSE and MBE varied from 0.07 to 0.47 mm/day and from −0.45 to 0.22 mm/day, respectively, and the regression slope varied from 0.90 to 1.14, and the coefficient of determination (R2) ranged from 0.77 to 0.92. In the case of missing wind speed, the method that used long-term average wind speed (ETo-um) resulted in the best estimates, especially for lower ETo rates (ETo<6 mm/day) as compared to the method (ETo-w2) that adopted the default wind speed of 2 m/s. Indeed the ETo-w2 method overestimated ETo from 4 to 35%. With the ETo-um method, RMSE varied from 0.43 to 0.57 mm/day and the MBE varied from −0.05 to 0.04 mm/day whereas the (ETo-w2) method had the RMSE ranging from 0.59 to 2.11 mm/day and the MBE ranging from 0.26 to 1.90 mm/day. The FAO-PM equation had the least accurate performance when only temperature data is available. The Valiantzas 1 equation that uses only air temperature and relative humidity data was not suitable and is not recommended for use in Burkina Faso climatic conditions. The Valiantzas 2 equation with full climatic data resulted in good ETo estimates relative to the FAO-PM ETo with full data under the Burkina Faso climate conditions; however, the FAO-PM equation is recommended because of limitations of the Valiantzas 2 equation under limited data conditions.

High Spatial Resolution Simulation of Annual Wind Energy Yield Using Near-Surface Wind Speed Time Series

In this paper a methodology is presented that can be used to model the annual wind energy yield (AEYmod) on a high spatial resolution (50 m × 50 m) grid based on long-term (1979–2010) near-surface wind speed (US) time series measured at 58 stations of the German Weather Service (DWD). The study area for which AEYmod is quantified is the German federal state of Baden-Wuerttemberg. Comparability of the wind speed time series was ensured by gap filling, homogenization and detrending. The US values were extrapolated to the height 100 m (U100m,emp) above ground level (AGL) by the Hellman power law. All U100m,emp time series were then converted to empirical cumulative distribution functions (CDFemp). 67 theoretical cumulative distribution functions (CDF) were fitted to all CDFemp and their goodness of fit (GoF) was evaluated. It turned out that the five-parameter Wakeby distribution (WK5) is universally applicable in the study area. Prior to the least squares boosting (LSBoost)-based modeling of WK5 parameters, 92 predictor variables were obtained from: (i) a digital terrain model (DTM), (ii) the European Centre for Medium-Range Weather Forecasts re-analysis (ERA)-Interim reanalysis wind speed data available at the 850 hPa pressure level (U850hPa), and (iii) the Coordination of Information on the Environment (CORINE) Land Cover (CLC) data. On the basis of predictor importance (PI) and the evaluation of model accuracy, the combination of predictor variables that provides the best discrimination between U100m,emp and the modeled wind speed at 100 m AGL (U100m,mod), was identified. Results from relative PI-evaluation demonstrate that the most important predictor variables are relative elevation (Φ) and topographic exposure (τ) in the main wind direction. Since all WK5 parameters are available, any manufacturer power curve can easily be applied to quantify AEYmod.

Investigation of wind resources in Timimoun region, Algeria

This article presents an analysis of wind power resources in Timimoun region using the Wind Atlas Analysis and Application Program. This application takes in consideration the effects of sheltering obstacles, surface roughness changes, and the terrain variations on the wind speed variability and power output. The analysis part is composed of generating the wind speed and direction data for developing the wind atlas. Hourly wind data records over a period of 3 years from 2001 to 2003 were obtained from Société Nationale de l’Electricité et du Gaz R&amp;D Office. The wind data measurements were made at an elevation of 17 m above the ground level. The mean values of wind speeds, wind power density, the predominant wind directions, the frequency distribution, and the Weibull distribution parameters (c and k) were determined. Finally, the energy yield was estimated using the VESTAS V90-2.0-MW wind turbine as the reference turbine in the southwestern region of Algeria, Timimoun. RETScreen model was also used to estimate the wind power potential employing the long-term annual mean wind speed data over a period of almost 22 years between 1984 and 2005.

Assessing uncertainty and sensor biases in passive microwave data across High Mountain Asia

Abstract Snowfall comprises a significant percentage of the annual water budget in High Mountain Asia (HMA), but snow-water equivalent (SWE) is poorly constrained due to lack of in-situ measurements and complex terrain that limits the efficacy of modeling and observations. Over the past few decades, SWE has been estimated with passive microwave (PM) sensors with generally good results in wide, flat, terrain, and lower reliability in densely forested, complex, or high-elevation areas. In this study, we use raw swath data from five satellite sensors — the Special Sensor Microwave/Imager (SSMI) and Special Sensor Microwave Imager/Sounder (SSMIS) (1987–2015, F08, F11, F13, F17), Advanced Microwave Scanning Radiometer — Earth Observing System (AMSR-E, 2002–2011), AMSR2 (2012–2015), and the Global Precipitation Measurement (GPM, 2014–2015) — in order to understand the spatial and temporal structure of native sensor, topographic, and land cover biases in SWE estimates in HMA. We develop a thorough understanding of the uncertainties in our SWE estimates by examining the impacts of topographic parameters (aspect, relief, hillslope angle, and elevation), land cover, native sensor biases, and climate parameters (precipitation, temperature, and wind speed). HMA, with its high seasonality, large topographic gradients and low relief at high elevations provides an excellent context to examine a wide range of climatic, land-cover, and topographic settings to better constrain SWE uncertainties and potential sensor bias. Using a multi-parameter regression, we compare long-term SWE variability to forest fraction, maximal multi-year snow depth, topographic parameters, and long-term average wind speed across both individual sensor time series and a merged multi-sensor dataset. In regions where forest cover is extensive, it is the strongest control on SWE variability. In those regions where forest density is low ( While forest cover and elevation have been integrated into many SWE algorithms, wind speed and long-term maximal snow depth have not. Our results show that wind redistribution of snow can have impacts on SWE, especially over large, flat, areas. Using our regression results, we have developed an understanding of sensor-specific SWE uncertainties and their spatial patterns. The uncertainty maps developed in this study provide a first-order approximation of SWE-estimate reliability for much of HMA, and imply that high-fidelity SWE estimates can be produced for many high-elevation areas.

Extrapolating Satellite Winds to Turbine Operating Heights

AbstractOcean wind retrievals from satellite sensors are typically performed for the standard level of 10 m. This restricts their full exploitation for wind energy planning, which requires wind information at much higher levels where wind turbines operate. A new method is presented for the vertical extrapolation of satellite-based wind maps. Winds near the sea surface are obtained from satellite data and used together with an adaptation of the Monin–Obukhov similarity theory to estimate the wind speed at higher levels. The thermal stratification of the atmosphere is taken into account through a long-term stability correction that is based on numerical weather prediction (NWP) model outputs. The effect of the long-term stability correction on the wind profile is significant. The method is applied to Envisat Advanced Synthetic Aperture Radar scenes acquired over the south Baltic Sea. This leads to maps of the long-term stability correction and wind speed at a height of 100 m with a spatial resolution of 0.02°. Calculations of the corresponding wind power density and Weibull parameters are shown. Comparisons with mast observations reveal that NWP model outputs can correct successfully for long-term stability effects and also, to some extent, for the limited number of satellite samples. The satellite-based and NWP-simulated wind profiles are almost equally accurate with respect to those from the mast. However, the satellite-based maps have a higher spatial resolution, which is particularly important in nearshore areas where most offshore wind farms are built.

Teleconnections and analysis of long-term wind speed variability in the UAE

Wind energy accounts for a small share of the global energy consumption in spite of its widespread availability. One of the obstacles hindering exploitation of wind energy is the lack of proper wind speed assessment models. The wind energy field credibility has occasionally suffered from wind power potential estimation studies that were conducted based on very short wind speed records and which did not give consideration to inter-annual wind variability. The objective of this paper is to examine the long-term variability of wind speed in the United Arab Emirates (UAE) and its teleconnections with various global climate indices by using wind speed collected from six ground stations and a reanalysis dataset. Linear correlation analysis and wavelet analysis were used to characterize the interaction. The modified Mann–Kendall test and linear regression indicated that half of the stations show a significant wind speed trend at the 5% level. The cumulative sum and Bayesian change detection methods indicated that five of the stations present change points. Continuous wavelet transform of wind speed showed biannual periodicity in some stations, in addition to the annual one. Wavelet coherence analysis demonstrated that wind speed in the UAE is mainly associated with the North Atlantic Oscillation, East Atlantic Oscillation, El Nino Southern Oscillation and the Indian Ocean Dipole indices. The first two indices simultaneously modulate wind speed in the summer while the last two influence winter and autumn wind speeds. Step-wise multiple linear regression models were developed to select appropriate predictors among the various climate indices.

Site assessment, turbine selection, and local feed-in tariffs through the wind energy index

Since wind energy is rapidly growing, new wind farms are installed worldwide and a discussion is going on concerning the optimal political framework to promote this development. In this paper, we present a wind energy index, which is supportive for wind farm planners, operators, and policy-makers. Based on long-term and low-scale reanalysis wind speed data from MERRA and true production data, it can predict the expected wind energy production for every location and turbine type. After an in-sample and out-of-sample evaluation of the index performance, it is applied to assess the wind energy potential of locations in Germany, to compare different turbine types, and to derive the required compensation in terms of locally different feed-in tariffs. We show that in many parts of South Germany, profitability of new wind farms cannot be achieved given the current legal situation.

Aerosol Measurements at South Pole: Climatology and Impact of Local Contamination

The Atmospheric Research Observatory (ARO), part of the National Science Foundation's (NSF's) Amundsen-Scott South Pole Station, is located at one of the cleanest and most remote sites on earth. NOAA has been making atmospheric baseline measurements at South Pole since the mid-1970's. The pristine conditions and high elevation make the South Pole a desirable location for many types of research projects and since the early 2000's there have been multiple construction projects to accommodate both a major station renovation and additional research activities and their personnel. The larger population and increased human activity at the station, located in such close proximity to the global baseline measurements conducted at the ARO, calls into question the potential effects of local contamination of the long-term background measurements. In this work, the long-term wind and aerosol climatologies were updated and analyzed for trends. Winds blow toward the ARO from the Clean Air Sector ~88% of the time and while there is some year-to-year variability in this number, the long-term wind speed and direction measurements at South Pole have not changed appreciably in the last 35 years. Several human activity markers including station population, aircraft flights and fuel usage were used as surrogates for local aerosol emissions; peak human activity (and thus likely local emissions) occurred in the 2006 and 2007 austral summer seasons. The long-term aerosol measurements at ARO do not peak during these seasons, suggesting that the quality control procedures in place to identify and exclude continuous sources of local contamination are working and that the NSF's sector management plan for the Clean Air Sector is effective. No significant trends over time were observed in particle number concentration, aerosol light scattering coefficient, or any aerosol parameter except scattering Angstrom exponent, which showed a drop of ~0.02 yr^(-1) over the 36-year record. The effect of discrete local contamination events in the Clean Air Sector is discussed using one well-documented example.

Influence of Natural and Cost Factors on the Choice of Optimal Wind Power Plant Capacity as Part of a Wind-Diesel System in the East of Russia

The paper presents the results of testing the methods for determining optimal capacity of wind power plant for offgrid consumers. Analysis of hydrometeorological data on yearly variation in long-term average wind speed in the eastern regions of Russia allows us to identify three characteristic groups of annual distribution of this parameter depending on territory. For each of the groups the relationships between the ratio of an optimal wind power plant capacity to a maximum off-grid consumer load and wind resource indices and annual distribution are presented. The research substantiates that capital investment of the wind power plant construction project and prices of replaced diesel fuel do not affect the value of optimal wind power plant capacity but make a considerable impact on the costeffectiveness of the projects.

Weibull Wind-Speed Distribution Parameters Derived from a Combination of Wind-Lidar and Tall-Mast Measurements Over Land, Coastal and Marine Sites

Wind-speed observations from tall towers are used in combination with observations up to 600 m in altitude from a Doppler wind lidar to study the long-term conditions over suburban (Hamburg), rural coastal (Hovsore) and marine (FINO3) sites. The variability in the wind field among the sites is expressed in terms of mean wind speed and Weibull distribution shape-parameter profiles. The consequences of the carrier-to-noise-ratio (CNR) threshold-value choice on the wind-lidar observations are revealed as follows. When the wind-lidar CNR is lower than a prescribed threshold value, the observations are often filtered out as the uncertainty in the wind-speed measurements increases. For a pulsed heterodyne Doppler lidar, use of the traditional –22 dB CNR threshold value at all measuring levels up to 600 m results in a $$\approx $$ 7 % overestimation in the long-term mean wind speed over land, and a $$\approx $$ 12 % overestimation in coastal and marine environments. In addition, the height of the profile maximum of the shape parameter of the Weibull distribution (so-called reversal height) is found to depend on the applied CNR threshold; it is found to be lower at small CNR threshold values. The reversal height is greater in the suburban (high roughness) than in the rural (low roughness) area. In coastal areas the reversal height is lower than that over land and relates to the internal boundary layer that develops downwind from the coastline. Over the sea the shape parameter increases towards the sea surface. A parametrization of the vertical profile of the shape parameter fits well with observations over land, coastal regions and over the sea. An applied model for the dependence of the reversal height on the surface roughness is in good agreement with the observations over land.

Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting

It is important to forecast the wind speed for managing operations in wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and deviation of the wind. As existing forecasting methods directly model the raw wind speed data, it is difficult for them to provide higher inference accuracy. Differently, this paper presents a sophisticated deep-learning technique for short-term and long-term wind speed forecast, i.e., the predictive deep Boltzmann machine (PDBM) and corresponding learning algorithm. The proposed deep model forecasts wind speed by analyzing the higher level features abstracted from lower level features of the wind speed data. These automatically learnt features are very informative and appropriate for the prediction. The proposed PDBM is a deep stochastic model that can represent the wind speed very well, and is inspired by two aspects. 1)The stochastic model is suitable to capture the probabilistic characteristics of wind speed. 2)Recent developments in neural networks with deep architectures show that deep generative models have competitive capability to approximate nonlinear and nonsmooth functions. The evaluation of the proposed PDBM model is depicted by both hour-ahead and day-ahead prediction experiments based on real wind speed datasets. The prediction accuracy of the PDBM model outperforms existing methods by more than 10%.

Buoyancy effects on the scaling characteristics of atmospheric boundary-layer wind fields in the mesoscale range.

We have analyzed long-term wind speed time series from five field sites up to a height of 300 m from the ground. Structure function-based scaling analysis has revealed that the scaling exponents in the mesoscale regime systematically depend on height. This anomalous behavior is likely caused by the buoyancy effects. In the framework of the extended self-similarity, the relative scaling exponents portray quasiuniversal behavior.