Nonlinear analysis of the near-surface wind speed time series

2012 ◽  
Author(s):  
Ming Zeng ◽  
Haiyan Jia ◽  
Qinghao Meng ◽  
Tiemao Han ◽  
Zhengcun Liu
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Nonlinear Analysis ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Near Surface

scholarly journals A spectral analysis of near-surface wind speed and possible sources of predictability in the Iberian Peninsula

2021 ◽  
Author(s):  
Eduardo Utrabo-Carazo ◽  
Cesar Azorin-Molina ◽  
Enric Aguilar ◽  
Manola Brunet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Wind Speed ◽  
Iberian Peninsula ◽  
Southern Oscillation ◽  
Statistical Significance ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Near Surface ◽  
Mean Values

<p>Conventional time series analysis of observed near-surface wind speed (SWS) have focused both on mean values and on the sign, magnitude and statistical significance of trends. Specifically, a decrease in the SWS has been detected in continental surfaces of the planet's mid-latitudes from 1979 to 2010 approximately, the so-called <em>stilling</em> phenomenon; and an increase from 2010 until now, the <em>reversal</em> phenomenon. However, although various hypotheses have been proposed in the scientific literature, the mechanisms behind these phenomena and what evolution this parameter will follow in the future are still understudied, mainly because the response of a variable dependent on atmospheric circulation, such as wind speed, to a warming climate is uncertain. This study aims to use spectral analysis (Fourier and wavelet) to determine the most significant frequency modes associated with the SWS time series in the Iberian Peninsula (IP), for both mean wind speed and daily peak wind gusts, as well as its temporal evolution for 1961-2019. Subsequently, this study will also attempt to relate these modes to those corresponding to various modes of ocean-atmosphere variability such as the El Niño-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) or, due to the influence of sudden stratospheric warming (SSW) in the European troposphere, the polar vortex. The ultimate goal will be to explore possible sources of predictability in the medium-long term for SWS across the IP, which would have direct applications in areas such as: wind-power generation, agriculture, air quality, insurance and fishing industries, among many others socioeconomic and environmental issues. </p>

Recurrence Characteristics Analysis of Near-Surface Wind Speed Signal with Different Sampling Frequencies

2014 ◽  
Vol 599-601 ◽  
pp. 1605-1609 ◽  
Author(s):  
Ming Zeng ◽  
Zhan Xie Wu ◽  
Qing Hao Meng ◽  
Jing Hai Li ◽  
Shu Gen Ma
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Recurrence Plot ◽  
Sampling Frequency ◽  
Near Surface ◽  
Gas Leakage ◽  
Time Period ◽  
Characteristics Analysis ◽  
Quantification Analysis

The wind is the main factor to influence the propagation of gas in the atmosphere. Therefore, the wind signal obtained by anemometer will provide us valuable clues for searching gas leakage sources. In this paper, the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) are applied to analyze the influence of recurrence characteristics of the wind speed time series under the condition of the same place, the same time period and with the sampling frequency of 1hz, 2hz, 4.2hz, 5hz, 8.3hz, 12.5hz and 16.7hz respectively. Research results show that when the sampling frequency is higher than 5hz, the trends of recurrence nature of different groups are basically unchanged. However, when the sampling frequency is set below 5hz, the original trend of recurrence nature is destroyed, because the recurrence characteristic curves obtained using different sampling frequencies appear cross or overlapping phenomena. The above results indicate that the anemometer will not be able to fully capture the detailed information in wind field when its sampling frequency is lower than 5hz. The recurrence characteristics analysis of the wind speed signals provides an important basis for the optimal selection of anemometer.

A preliminary study on the influence of Beijing urban spatial morphology on near-surface wind speed

Urban Climate ◽  
2020 ◽  
Vol 34 ◽  
pp. 100703
Author(s):  
Yonghong Liu ◽  
Yongming Xu ◽  
Fangmin Zhang ◽  
Wenjun Shu
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Near Surface ◽  
Spatial Morphology ◽  
Preliminary Study

scholarly journals Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 738 ◽  
Author(s):  
Wenqing Xu ◽  
Like Ning ◽  
Yong Luo
Keyword(s):  
Wind Speed ◽  
Decision Tree ◽  
Wind Power ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Gradient Boosting ◽  
Post Processing ◽  
Near Surface ◽  
Wind Power Forecasting ◽  
Numerical Weather

With the large-scale development of wind energy, wind power forecasting plays a key role in power dispatching in the electric power grid, as well as in the operation and maintenance of wind farms. The most important technology for wind power forecasting is forecasting wind speed. The current mainstream methods for wind speed forecasting involve the combination of mesoscale numerical meteorological models with a post-processing system. Our work uses the WRF model to obtain the numerical weather forecast and the gradient boosting decision tree (GBDT) algorithm to improve the near-surface wind speed post-processing results of the numerical weather model. We calculate the feature importance of GBDT in order to find out which feature most affects the post-processing wind speed results. The results show that, after using about 300 features at different height and pressure layers, the GBDT algorithm can output more accurate wind speed forecasts than the original WRF results and other post-processing models like decision tree regression (DTR) and multi-layer perceptron regression (MLPR). Using GBDT, the root mean square error (RMSE) of wind speed can be reduced from 2.7–3.5 m/s in the original WRF result by 1–1.5 m/s, which is better than DTR and MLPR. While the index of agreement (IA) can be improved by 0.10–0.20, correlation coefficient be improved by 0.10–0.18, Nash–Sutcliffe efficiency coefficient (NSE) be improved by −0.06–0.6. It also can be found that the feature which most affects the GBDT results is the near-surface wind speed. Other variables, such as forecast month, forecast time, and temperature, also affect the GBDT results.

scholarly journals Uncertainty in recent near-surface wind speed trends: a global reanalysis intercomparison

2017 ◽  
Vol 12 (11) ◽  
pp. 114019 ◽  
Author(s):  
Verónica Torralba ◽  
Francisco J Doblas-Reyes ◽  
Nube Gonzalez-Reviriego
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Near Surface ◽  
Global Reanalysis ◽  
Reanalysis Intercomparison
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