scholarly journals Deterministic nature of the underlying dynamics of surface wind fluctuations

2012 ◽  
Vol 30 (10) ◽  
pp. 1503-1514 ◽  
Author(s):  
R. C. Sreelekshmi ◽  
K. Asokan ◽  
K. Satheesh Kumar
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Structural Engineering ◽  
Deterministic Model ◽  
Surface Wind ◽  
Wind Speed Data ◽  
Mean Wind Speed ◽  
Low Dimensional ◽  
Short Term Prediction ◽  
Speed Fluctuations

Abstract. Modelling the fluctuations of the Earth's surface wind has a significant role in understanding the dynamics of atmosphere besides its impact on various fields ranging from agriculture to structural engineering. Most of the studies on the modelling and prediction of wind speed and power reported in the literature are based on statistical methods or the probabilistic distribution of the wind speed data. In this paper we investigate the suitability of a deterministic model to represent the wind speed fluctuations by employing tools of nonlinear dynamics. We have carried out a detailed nonlinear time series analysis of the daily mean wind speed data measured at Thiruvananthapuram (8.483° N,76.950° E) from 2000 to 2010. The results of the analysis strongly suggest that the underlying dynamics is deterministic, low-dimensional and chaotic suggesting the possibility of accurate short-term prediction. As most of the chaotic systems are confined to laboratories, this is another example of a naturally occurring time series showing chaotic behaviour.

Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

2020 ◽  
Author(s):  
Yagya Dutta Dwivedi ◽  
Vasishta Bhargava Nukala ◽  
Satya Prasad Maddula ◽  
Kiran Nair
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Surface Roughness ◽  
Wind Speed ◽  
Atmospheric Turbulence ◽  
Surface Boundary ◽  
Low Frequencies ◽  
Surface Boundary Layer ◽  
Power Spectral ◽  
Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

scholarly journals The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

2018 ◽  
Vol 7 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Adekunlé Akim Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Mawugno Koffi Kodjo ◽  
Seydou Ouedraogo ◽  
Koffi-Sa Bédja
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Data ◽  
Empirical Method ◽  
Likelihood Method ◽  
Series Data ◽  
New Approach ◽  
Weibull Parameters ◽  
Distribution Histogram ◽  
Mean Wind Speed

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

Stochastic generation of hourly mean wind speed data

2004 ◽  
Vol 29 (14) ◽  
pp. 2111-2131 ◽  
Author(s):  
Hafzullah Aksoy ◽  
Z Fuat Toprak ◽  
Ali Aytek ◽  
N Erdem Ünal
Keyword(s):  
Wind Speed ◽  
Stochastic Generation ◽  
Wind Speed Data ◽  
Mean Wind Speed

Diurnal variation in surface latent heat flux and the effect of diurnal variability on the climatological latent heat flux over the tropical oceans

2021 ◽  
Author(s):  
Yunwei Yan ◽  
Lei Zhang ◽  
Xiangzhou Song ◽  
Guihua Wang ◽  
Changlin Chen
Keyword(s):  
Heat Flux ◽  
Wind Speed ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Warm Pool ◽  
Diurnal Variability ◽  
Tropical Oceans ◽  
Mean Wind Speed

AbstractDiurnal variation in surface latent heat flux (LHF) and the effects of diurnal variations in LHF-related variables on the climatological LHF are examined using observations from the Global Tropical Moored Buoy Array. The estimated amplitude of the climatological diurnal LHF over the Indo-Pacific warm pool and the equatorial Pacific and Atlantic cold tongues is remarkable, with maximum values exceeding 20.0 W m−2. Diurnal variability of sea surface skin temperature (SSTskin) is the primary contributor to the diurnal LHF amplitude. Because the diurnal SSTskin amplitude has an inverse relationship with surface wind speed over the tropical oceans, an inverse spatial pattern between the diurnal LHF amplitude and surface wind speed results. Resolving diurnal variations in the SSTskin and wind improves the estimate of the climatological LHF by properly capturing the daytime SSTskin and daily mean wind speed, respectively. The diurnal SSTskin-associated contribution is large over the warm pool and equatorial cold tongues where low wind speeds tend to cause strong diurnal SSTskin warming, while the magnitude associated with the diurnal winds is large over the highly dynamic environment of the Inter-Tropical Convergence Zone. The total diurnal contribution is about 9.0 W m−2 on average over the buoy sites. There appears to be a power function (linear) relationship between the diurnal SSTskin-associated (wind-associated) contribution and surface mean wind speed (wind speed enhancement from diurnal variability). The total contribution from diurnal variability can be estimated accurately from high-frequency surface wind measurements using these relationships.

scholarly journals Evaluation of Hurricane Wind Speed Analyses in a Simulation of Hurricane Earl (2010) Using Low-Order Wavenumbers

2017 ◽  
Vol 145 (8) ◽  
pp. 3223-3245 ◽  
Author(s):  
George Andrew Soukup ◽  
Frank D. Marks
Keyword(s):  
Time Series ◽  
Steady State ◽  
Wind Speed ◽  
Data Collection ◽  
Surface Wind ◽  
Time Interval ◽  
System Sensitivity ◽  
Sensitivity Experiments ◽  
Hurricane Wind ◽  
Low Order

To determine how well a low-order wavenumber representation describes a hurricane wind speed field, given its natural variability in space and time, low-order wavenumber representations were calculated for hourly “snapshots” of the 10-m wind speed field generated by the current operational hurricane model. Two distinct periods were examined: the first when the storm is in a reasonably steady state over 7–8 h and the second where the storm is changing its internal structure over a similar time interval. Observing system sensitivity experiments were also performed using wind speed field time series obtained from interpolation of the model snapshots for each of the two periods. The time series were sampled along the flight legs of a typical “figure four” aircraft flight pattern to simulate the surface wind data collection process to ascertain the effects of the wind speed field’s temporal and spatial variability upon the low-order wavenumber analyses. The comparison between the model wind speed field at any time and the wavenumber representations during the “steady state” period shows that the essential features of the wind speed field are captured by wavenumbers 0 and 1 and that including up to wavenumber 3 practically reproduces the model field. However, in the “nonsteady” period the wavenumber 0 and 1 representation is frequently unable to capture the essential characteristics of the wind speed field. The observing system sensitivity experiments suggest that when the primary circulation is rapidly changing in amplitude and/or structure during the data collection period, the low-order wavenumbers analysis of the wind speed field will only represent the temporal mean structure.

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