Wind Velocity Time Series Research Articles

Applicability of double power law model for the statistical analysis of meteorological wind velocity time series data

Applicability of double power law model for the statistical analysis of meteorological wind velocity time series data

Applicability of double power law model for the statistical analysis of meteorological wind velocity time series data

Applicability of double power law model for the statistical analysis of meteorological wind velocity time series data

Wind Velocity Field Simulation of the Large-Span Spatial Structure Based on the Wavelet Decomposition Method

In this study, the wavelet decomposition method is combined with the vector AR model of the linear filter method to present a new method for simulating the fluctuating wind field of a large-span spatial structure. This method performs wavelet expansion on the autoregressive coefficients of the vector AR model in adequate space and adopts the least square method to estimate the autoregressive coefficients of the AR model. The implementation steps of this method are to simulate the wind field of large-span structures are given. The method is applied to the wind velocity field simulation of a large-span spatial structure, and the comparison between the simulation result and the target value and the comparison with the simulation result of the vector AR model are given. The results prove that the proposed method can reduce the information loss of wind velocity time-series analysis in the frequency domain, can accurately simulate the wind velocity time series of large-span spatial structures, and has high computational efficiency.

The high-frequency response correction of eddy covariance fluxes – Part 2: An experimental approach for analysing noisy measurements of small fluxes

Abstract. Fluxes measured with the eddy covariance (EC) technique are subject to flux losses at high frequencies (low-pass filtering). If not properly corrected for, these result in systematically biased ecosystem–atmosphere gas exchange estimates. This loss is corrected using the system's transfer function which can be estimated with either theoretical or experimental approaches. In the experimental approach, commonly used for closed-path EC systems, the low-pass filter transfer function (H) can be derived from the comparison of either (i) the measured power spectra of sonic temperature and the target gas mixing ratio or (ii) the cospectra of both entities with vertical wind speed. In this study, we compare the power spectral approach (PSA) and cospectral approach (CSA) in the calculation of H for a range of attenuation levels and signal-to-noise ratios (SNRs). For a systematic analysis, we artificially generate a representative dataset from sonic temperature (T) by attenuating it with a first order filter and contaminating it with white noise, resulting in various combinations of time constants and SNRs. For PSA, we use two methods to account for the noise in the spectra: the first is the one introduced by Ibrom et al. (2007a) (PSAI07), in which the noise and H are fitted in different frequency ranges, and the noise is removed before estimating H. The second is a novel approach that uses the full power spectrum to fit both H and noise simultaneously (PSAA21). For CSA, we use a method utilizing the square root of the H with shifted vertical wind velocity time series via cross-covariance maximization (CSAH,sync). PSAI07 tends to overestimate the time constant when low-pass filtering is low, whilst the new PSAA21 and CSAH,sync successfully estimate the expected time constant regardless of the degree of attenuation and SNR. We further examine the effect of the time constant obtained with the different implementations of PSA and CSA on cumulative fluxes using estimated time constants in frequency response correction. For our example time series, the fluxes corrected using time constants derived by PSAI07 show a bias between 0.1 % and 1.4 %. PSAA21 showed almost no bias, while CSAH,sync showed bias of ±0.4 %. The accuracies of both PSA and CSA methods were not significantly affected by SNR level, instilling confidence in EC flux measurements and data processing in set-ups with low SNR. Overall we show that, when using power spectra for the empirical estimation of parameters of H for closed-path EC systems the new PSAA21 outperforms PSAI07, while when using cospectra the CSAH,sync approach provides accurate results. These findings are independent of the SNR value and attenuation level.

Characterization of vertical wind velocity variability based on fractal dimension analysis

Abstract Modelling and forecasting the temporal behaviour of wind is of substantial importance in a variety of applications, which depend largely on the understanding and quantification of its variability. Existing literature focusing on wind speed variability is predominantly associated with horizontal wind component, whereas the knowledge of vertical wind component is much less documented. This paper investigates the variability of vertical wind velocity in the context of fractal dimension analysis. It is shown that the time series of vertical wind velocity are monofractals and weak anti-persistent. The median value of fractal dimension decreases from 1.582 ​at 10 ​m height to 1.547 ​at 320 ​m height. The vertical profile of fractal dimension is strongly tied with atmospheric stability condition, in terms of both magnitude and profile shape, and the deviation is most pronounced at higher levels. Moreover, the fractal dimension is found to increase with the increase in the mean horizontal wind speed, which can be partly attributed to the effect of atmospheric stability. Such relationship is also height-dependent. Similar to the horizontal wind component, the fractal dimension of vertical wind component also exhibits a positive correlation with window width.

Lorentzian Filter Correction of Turbulence Measurements on Oscillating Floating Platforms: Impact on Wind Spectra and Eddy‐Covariance Fluxes

AbstractTurbulence and eddy‐covariance measurements on a floating platform over water surfaces can be contaminated by platform oscillations, which may affect the calculated air‐water exchange. The conventional method for decontamination of the platform oscillations from the wind velocity measurements requires the installation of an additional sensitive, and often costly, motion sensor. This paper examines a new mathematical decontamination method, termed Lorentzian filter, which avoids the need for such an instrument. The method, based on the Lorentzian function, capitalizes on the pseudo‐harmonic behavior of the platform oscillations and reduces the amplitude of turbulent wind velocity data detected as artifacts at the specific natural frequencies of the platform. The Lorentzian filter was applied to wind velocity data measured by sonic anemometer and eddy‐covariance system over the Dead Sea, Israel, for 30 days. We examined two approaches of dealing with motion contamination: Lorentzian filter decontamination and motion sensor decontamination. Both approaches were compared to nonfiltered raw wind velocity as well. Using the 3D wind velocity series, we examined the wind spectra, the cospectra of water vapor concentration and horizontal wind speed with vertical wind speed, and H2O and momentum fluxes. The Lorentzian filter performed very well in decontaminating the wind spectrum, meaning that it efficiently identified the contamination in the natural oscillation frequency and returned a decontaminated wind velocity time series. The cospectra and fluxes were less prone to the contamination of platform oscillations, presumably due to low correlations between the spurious wind velocity components and other measured scalars, such as water vapor.

Warm-air entrainment and advection during alpine blowing snow events

Abstract. Blowing snow transport has considerable impact on the hydrological cycle in alpine regions both through the redistribution of the seasonal snowpack and through sublimation back into the atmosphere. Alpine energy and mass balances are typically modeled with time-averaged approximations of sensible and latent heat fluxes. This oversimplifies nonstationary turbulent mixing in complex terrain and may overlook important exchange processes for hydrometeorological prediction. To determine if specific turbulent motions are responsible for warm- and dry-air advection during blowing snow events, quadrant analysis and variable interval time averaging was used to investigate turbulent time series from the Fortress Mountain Snow Laboratory alpine study site in the Canadian Rockies, Alberta, Canada, during the winter of 2015–2016. By analyzing wind velocity and sonic temperature time series with concurrent blowing snow, such turbulent motions were found to supply substantial sensible heat to near-surface wind flows. These motions were responsible for temperature fluctuations of up to 1 ∘C, a considerable change for energy balance estimation. A simple scaling relationship was derived that related the frequency of dominant downdraft and updraft events to their duration and local variance. This allows for the first parameterization of entrained or advected energy for time-averaged representations of blowing snow sublimation and suggests that advection can strongly reduce thermodynamic feedbacks between blowing snow sublimation and the near-surface atmosphere. The downdraft and updraft scaling relationship described herein provides a significant step towards a more physically based blowing snow sublimation model with more realistic mixing of atmospheric heat. Additionally, calculations of return frequencies and event durations provide a field-measurement context for recent findings of nonstationarity impacts on sublimation rates.

Recurrent Neural Networks for Meteorological Time Series Imputation

The aim of the work presented in this paper is to analyze the effectiveness of recurrent neural networks in imputation processes of meteorological time series, for this six different models based on recurrent neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are implemented and it is experimented with hourly meteorological time series such as temperature, wind direction and wind velocity. The implemented models have architectures of 2, 3 and 4 sequential layers and their results are compared with each other, as well as with other imputation techniques for univariate time series mainly based on moving averages. The results show that for temperature time series on average the recurrent neural network achieve better results than the imputation techniques based on moving averages; in the case of wind direction time series, on average only one model based on RNN manages to exceed the models based on moving averages; and finally, for wind velocity time series on average, no RNN-based model manages to exceed the results achieved by moving averages-based models.

Ensemble streamflow projections for a small watershed with HSPF model.

A watershed modeling tool, Hydrological Simulation Program-FORTRAN (HSPF), was utilized to model the hydrological processes in the agricultural Sarısu watershed in western Turkey. The meteorological input data were statistically downscaled time series from General Circulation Model simulations. The input data were constructed as an ensemble of 400 individual time series of temperature, precipitation, dewpoint temperature, solar radiation, potential evapotranspiration, cloudiness, and wind velocity, as required by HSPF. The ensemble was divided into four subsets, each comprising of 100 time series, of different Special Report on Emissions Scenarios. Yearly and monthly total streamflow time series were obtained from the calibrated and validated HSPF model spanning a period of 116 years between the water years of 1984 and 2099. The projections in the watershed showed a median increase of 3 °C in yearly average temperatures between the beginning and end 30-year periods of the 116-year simulation periods based on 400 ensemble members while the corresponding change in total yearly precipitation was - 71 mm. These changes led to a decrease in yearly streamflows by 40% which reflected itself to varying degrees in monthly flows. Correlations were established between the principal drivers of the watershed hydrological cycle, namely temperature and precipitation, and streamflow. The results showed that the changes in the climatic conditions will greatly affect water-related issues in the watershed and emphasize the necessity of preparing carefully to adapt to a warmer and drier climate.

Satellite observations of the effect of the \u201cGodzilla El Ni\xf1o\u201d on the Tehuantepec upwelling system in the Mexican Pacific

During 2015–2016, a strong El Niño, the “Godzilla El Niño,” which is similar to El Niño events that occurred in 1982/1983 and 1997/1998, occurred in the Pacific Ocean. In this paper, we report on the influence of the “Godzilla El Niño” on the sea surface temperature (SST) and chlorophyll-a (Chla) concentrations in the Tehuantepec upwelling system using satellite observation data. A time-series of the SST and Chla levels in a circular site with a diameter of approximately 54 km centered at the fixed position (15°N, 94.75°W) for the period from January 2003 to December 2016 was obtained using a Moderate Resolution Imaging Spectroradiometer. To estimate the vertical water velocity, a wind velocity time series was obtained from the Copernicus Marine Environment Monitoring Service to assess its variation over the period from January 2014 to December 2016. The results showed unusually high SSTs (> 28 °C) and atypically low Chla concentrations (< 0.1 mg m−3) in the domain of interest during the winter of 2015/2016. In the region of study, the SST in January 2016 was 5.13 °C higher than it had been in January of 2015, whereas the Chla concentration was 1.56 mg m−3 lower over this period. We found that the “Godzilla El Niño” impacted the Tehuantepec upwelling system in the following ways: (1) the wind and vertical water velocity during the winter of 2015/2016 were slightly higher than those observed during the winters of 2013/2014 and 2014/2015; (2) the coastal SST values were elevated during the winter of 2015/2016 compared to their levels during the previous two winters, revealing nutrient-poor water advection; and (3) the Chla concentrations during the 2015/2016 winter season were unusually low compared to their levels during the previous two winters. Our interpretation of these results is that in the Tehuantepec Gulf the wind was strong and induced vertical water velocities reaching up to 6 m day−1 during the “Godzilla El Niño” event (winter 2015/2016); however, the levels of Chla during this period were lower than they had been in previous years. In particular, the levels were lower than they had been during the previous winters. This suggests that, although the wind during the event favored strong upwelling, the water that was advected to the upper layer was nutrient poor.

Transient behavior in impinging jets in crossflow with application to downburst flows

Thunderstorm winds and their interaction with structures are transient. The transient nature of downbursts is related to their embedment into stationary atmospheric boundary layer (ABL) winds. This paper introduces a signal analysis technique for the detection of abrupt changes in wind velocity time series with transient features. The detection method is then applied to explore the superposition between a straight wind and a downburst-like outflow. To study this interaction, the WindEEE Dome at Western University simultaneously produced straight and impinging jet flows in a closed-circuit mode of operation. The strength and characteristics of these two flows (when simultaneously produced) are studied herein for nine different flow rate ratios between the two types of flows. The method defined for detection of abrupt changes in velocity signals is used to determine the beginning of the impinging jets, as well as the slowdown of the ABL winds. In addition, the proposed methodology is tested for the case of a real downburst event recorded in Livorno, Italy, on 1 October 2012. The method accurately identifies abrupt changes in both mean and standard deviation of velocity signals in transient flows. The largest interference between ABL and downburst winds occurs when both flows are weak.

Quantifying Highly Variable Air–Sea Momentum Flux Using Wavelet Analysis

AbstractSurface wind stress is a crucial driver of upper-ocean processes, impacting air–sea gas flux, wind-wave development, and material transport. Conventional eddy covariance (EC) processing requires imposing a fixed averaging window on the wind velocity time series in order to estimate the downward flux of momentum. While this method has become the standard means of directly measuring the wind stress, the use of a fixed averaging interval inherently constrains one’s ability to resolve transient signals that may have net effects on the air–sea interactions. Here we utilize the wavelet transform to develop a new technique for directly quantifying the wind stress magnitude from the wavelet coscalogram products. The time averages of these products evaluated at the scale of maximum amplitude are highly correlated with the EC estimates (R2 = 0.99; 5-min time windows), suggesting that stress is particularly sensitive to the dominant turbulent eddies. By taking advantage of the new method’s high temporal resolution, transient wind forcing and its dominant scales may be explicitly computed and analyzed. This technique will allow for more general investigations into air–sea dynamics under nonstationary or spatially inhomogeneous conditions, such as within the nearshore region.

Resuspension of ash after the 2014 phreatic eruption at Ontake volcano, Japan

We determined the resuspension process of an ash deposit after the phreatic eruption of September 27th, 2014 at Ontake volcano, Japan, by analyzing the time series data of particle concentrations obtained using an optical particle counter and the characteristics of an ash sample. The time series of particle concentration was obtained by an optical particle counter installed 11km from the volcano from September 21st to October 19th, 2014. The time series contains counts of dust particles (ash and soil), pollen, and water drops, and was corrected to calculate the concentration of dust particles based on a polarization factor reflecting the optical anisotropy of particles. The dust concentration was compared with the time series of wind velocity. The dust concentration was high and the correlation coefficient with wind velocity was positive from September 28th to October 2nd. Grain-size analysis of an ash sample confirmed that the ash deposit contains abundant very fine particles (<30μm). Simple theoretical calculations revealed that the daily peaks of the moderate wind (a few m/s at 10m above the ground surface) were comparable with the threshold wind velocity for resuspension of an unconsolidated deposit with a wide range of particle densities. These results demonstrate that moderate wind drove the resuspension of an ash deposit containing abundant fine particles produced by the phreatic eruption.

Research and analysis on response characteristics of bracket-line coupling system under wind load

In this paper, a three-dimensional finite element model of bracket-line coupling system is established based on ANSYS software. Using the wind velocity time series which is generated by MATLAB as a power input, by comparing and analyzing the influence of different wind speeds and different wind attack angles, it is found that when 0 degree wind acts on the structure, wires have a certain damping effect in the bracket-line coupling system and at the same wind speed, the 90 degree direction is the most unfavorable wind direction for the whole structure according to the three kinds of angle wind calculated at present. In the bracket-line coupling system, the bracket structure is more sensitive to the increase of wind speed while the conductors are more sensitive to the change of wind attack angle.

An analytical model for the fluctuating wind velocity spectra of a moving vehicle

An analytical model for both the longitudinal and lateral fluctuating wind velocity spectra of a moving train in the random wind field has been developed based on Cooper's theory (Cooper, 1984). The fluctuating wind velocity spectrum of a moving point can be derived through the weighted linear superposition of the longitudinal and lateral spectra of the stationary point with both frequency and amplitude scaling. The proposed analytical model has been validated systematically, and based on the Simiu spectrum (Simiu and Scanlan, 1996), parametric studies were conducted for the influence on the fluctuating spectra of a moving train by wind flow angle from 0°, 30°, 60°, 90°, to 150° and varying speed ratios between vehicle and wind (mean wind velocity of 20m/s) from 1 to 4. It is revealed that the fluctuating wind velocity spectrum experienced by a moving train exhibits a Doppler effect phenomena due to the train movement, especially in the longitudinal case. The proposed spectrum model can provide an efficient simulation scheme for the fluctuating wind velocity time series of the moving train with adequate accuracy and can provide a rigorous analytical scheme for the subsequent aerodynamic response analysis of the moving train within the random wind field.

The evolution and sensitivity of katabatic flow dynamics to external influences through the evening transition

Data collected over an arid shallow slope (2–4°) during the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program are used to study the katabatic structure and onset of katabatic flow through the evening transition. An unprecedented suite of instrumentation, including a transect of five turbulence towers with 29 sonic anemometers, is used for the investigation. Fifteen transition periods with well‐defined katabatic flow and relatively little synoptic forcing are used in the study. The katabatic onset, jet velocity and jet height all show a large degree of interdiurnal and intersite variance. The slope‐aligned budgets of momentum and potential temperature are used to define time‐scales that describe the evolution of the katabatic flow. Composite wind velocity time series are used to show that ≈30 min elapses from the time when the katabatic flow initializes at 0.5 m to the point of initialization at 20 m. A simple katabatic model utilizing surface energy‐budget modelling is developed and used to model the interdiurnal katabatic variance. Finally, uni‐ and multi‐variate statistical analyses are used to diagnose the influence of specific external variables. Valley wind speed, turbulence structure, soil moisture, and shadow front speed are all found to influence the katabatic dynamics to varying degrees.

Lifting of time- and space-evolving winds for the determination of extreme hydraulic conditions

A semi-parametric method based on the theory of max-stable processes is proposed that can be used to determine the time- and space-evolving wind fields associated with a given return value of wind speed at a specified reference location. The method uses wind velocity time series over a grid of locations and their location-specific extreme value distributions in order to ‘lift’ observed extreme events (storms) into yet unobserved and even more extreme events. Because the lifted fields are needed to force hydrodynamic models used in the determination of extreme hydraulic conditions, they need to be continuous in time and space and to extend over a few days before the peak of the storm in order to allow the spin up of the hydrodynamic models. Thus, even if the original values of the wind fields are not above the threshold used to determine the location-specific extreme value distributions at a given location and instant (outside the validity of the method) they still need to be lifted. This can be achieved by augmenting the location-specific extreme value distributio\ s by the empirical distributions of the observations and lifting using the same method also the values below the thresholds. Mainly as a consequence of this procedure, the resulting lifted wind fields show low temporal and spatial wind speed gradients away from the peak of the storm. These gradients have raised concerns about the validity of the fields and it has been suggested that the lifting by means of a simple scaling factor may be preferable. To address these concerns, a thorough validation of the lifted fields has been carried out by 1) assessing the characteristics of the lifted fields and 2) assessing the ability of the lifted fields to reproduce extreme hydraulic conditions. The main recommendation of this study is that the proposed method be considered further for the determination of temporally and spatially evolving hydraulic conditions.

Low-level jets and above-canopy drainage as causes of turbulent exchange in the nocturnal boundary layer

Abstract. Sodar (SOund Detection And Ranging), eddy-covariance, and tower profile measurements of wind speed and carbon dioxide were performed during 17 consecutive nights in complex terrain in northern Taiwan. The scope of the study was to identify the causes for intermittent turbulence events and to analyze their importance in nocturnal atmosphere–biosphere exchange as quantified with eddy-covariance measurements. If intermittency occurs frequently at a measurement site, then this process needs to be quantified in order to achieve reliable values for ecosystem characteristics such as net ecosystem exchange or net primary production. Fourteen events of intermittent turbulence were identified and classified into above-canopy drainage flows (ACDFs) and low-level jets (LLJs) according to the height of the wind speed maximum. Intermittent turbulence periods lasted between 30 and 110 min. Towards the end of LLJ or ACDF events, positive vertical wind velocities and, in some cases, upslope flows occurred, counteracting the general flow regime at nighttime. The observations suggest that the LLJs and ACDFs penetrate deep into the cold air pool in the valley, where they experience strong buoyancy due to density differences, resulting in either upslope flows or upward vertical winds. Turbulence was found to be stronger and better developed during LLJs and ACDFs, with eddy-covariance data presenting higher quality. This was particularly indicated by spectral analysis of the vertical wind velocity and the steady-state test for the time series of the vertical wind velocity in combination with the horizontal wind component, the temperature, and carbon dioxide. Significantly higher fluxes of sensible heat, latent heat, and shear stress occurred during these periods. During LLJs and ACDFs, fluxes of sensible heat, latent heat, and CO2 were mostly one-directional. For example, exclusively negative sensible heat fluxes occurred while intermittent turbulence was present. Latent heat fluxes were mostly positive during LLJs and ACDFs, with a median value of 34 W m−2, while outside these periods the median was 2 W m−2. In conclusion, intermittent turbulence periods exhibit a strong impact on nocturnal energy and mass fluxes.

Application of sliding window technique for prediction of wind velocity time series

The uncertainty caused by the discontinuous nature of wind energy affects the power grid. Hence, forecasting the behavior of this renewable resource is important for energy managers and electricity traders to overcome the risk of unpredictability and to provide reliability for the grid. The objective of this paper is to employ and compare the potential of various artificial neural network structures of multi-layer perceptron (MLP) and radial basis function for prediction of the wind velocity time series in Tehran, Iran. Structure analysis and performance evaluations of the established networks indicate that the MLP network with a 4-7-13-1 architecture is superior to others. The best networks were deployed to unseen data and were capable of predicting the velocity time series via using the sliding window technique successfully. Applying the statistical indices with the predicted and the actual test data resulted in acceptable RMSE, MSE and R 2 values with 1.19, 1.43 and 0.85, respectively, for the best network.

The Analysis and Application on the Typhoon-Induced Vibration Control of the Wind Turbine with a Pendulum Damper

As a kind of high-rise structure, the offshore wind turbine is sensitive to wind load; it can generate strong dynamic responses to the excitation of typhoon. In this paper, a three-dimensional finite element model of offshore wind turbine is established with ADINA, responses under strong wind excitation are numerically simulated and performed subsequently. The fluctuating wind velocity time series are simulated by the method of HSM (harmony superposition method). Based on the modal and tine-history analyses of the structures together with self-vibration character, the pendulum damper is employed to control the resulting undesirable vibrations that are induced by wind. With the damper installed, the displacement and acceleration of the tower are reduced by as much as 40% using 1% of the total effective mass.