Comparison of Near-Surface Wind Speed Estimation Techniques Using Different Damage Indicators from a Damage Survey of Naplate, IL EF-3 Tornado

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.

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Sea surface wind speed estimation based on GNSS signal measurements

2012 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2012.6350950 ◽

2012 ◽

Cited By ~ 1

Author(s):

Kegen Yu ◽

Chris Rizos ◽

Andrew Dempster

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

Sea Surface ◽

Speed Estimation ◽

Sea Surface Wind Speed ◽

Sea Surface Wind

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Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data

Renewable Energy ◽

10.1016/j.renene.2018.04.019 ◽

2018 ◽

Vol 126 ◽

pp. 1043-1054 ◽

Cited By ~ 43

Author(s):

Victoria Hoolohan ◽

Alison S. Tomlin ◽

Timothy Cockerill

Keyword(s):

Wind Speed ◽

Gaussian Process ◽

Meteorological Data ◽

Surface Wind ◽

Gaussian Process Regression ◽

Surface Wind Speed ◽

Near Surface ◽

Numerical Weather

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

Urban Climate ◽

10.1016/j.uclim.2020.100703 ◽

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

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

Atmosphere ◽

10.3390/atmos11070738 ◽

2020 ◽

Vol 11 (7) ◽

pp. 738 ◽

Cited By ~ 1

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.

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