Long-Term Trends in Near-Surface Wind Speed over the Southern Hemisphere: A Preliminary Analysis

Abstract Near-surface wind speeds recorded at 117 stations in Canada for the period from 1953 to 2006 were analyzed in this study. First, metadata and a logarithmic wind profile were used to adjust hourly wind speeds measured at nonstandard anemometer heights to the standard 10-m level. Monthly mean near-surface wind speed series were then derived and subjected to a statistical homogeneity test, with homogeneous monthly mean geostrophic wind (geowind) speed series being used as reference series. Homogenized monthly mean near-surface wind speed series were obtained by adjusting all significant mean shifts, using the results of the statistical test and modeling along with all available metadata, and were used to assess the long-term trends. This study shows that station relocation and anemometer height change are the main causes for discontinuities in the near-surface wind speed series, followed by instrumentation problems or changes, and observing environment changes. It also shows that the effects of artificial mean shifts on the results of trend analysis are remarkable, and that the homogenized near-surface wind speed series show good spatial consistency of trends, which are in agreement with long-term trends estimated from independent datasets, such as surface winds in the United States and cyclone activity indices and ocean wave heights in the region. These indicate success in the homogenization of the wind data. During the period analyzed, the homogenized near-surface wind speed series show significant decreases throughout western Canada and most parts of southern Canada (except the Maritimes) in all seasons, with significant increases in the central Canadian Arctic in all seasons and in the Maritimes in spring and autumn.

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Effects of surface friction and turbulent mixing on long-term changes in the near-surface wind speed over the Eastern China Plain from 1981 to 2010

Climate Dynamics ◽

10.1007/s00382-017-4012-3 ◽

2017 ◽

Vol 51 (5-6) ◽

pp. 2285-2299 ◽

Cited By ~ 7

Author(s):

Jian Wu ◽

Jinlin Zha ◽

Deming Zhao ◽

Qidong Yang

Keyword(s):

Wind Speed ◽

Turbulent Mixing ◽

Eastern China ◽

Surface Wind ◽

Surface Friction ◽

Surface Wind Speed ◽

Near Surface ◽

Long Term Changes

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Terrestrial stilling will continue during the 21st century

10.21203/rs.3.rs-288223/v1 ◽

2021 ◽

Author(s):

Deliang Chen ◽

Kaiqiang Deng ◽

Cesar Azorin-Molina ◽

Song Yang ◽

Gangfeng Zhang ◽

...

Keyword(s):

Wind Speed ◽

21St Century ◽

Surface Wind ◽

Surface Wind Speed ◽

New Paradigm ◽

Near Surface ◽

The Pacific ◽

Long Time

Abstract The near-surface wind speed over land has declined in recent decades, a trend known as terrestrial stilling (TS)1-2. However, recent studies have indicated a reversal of the TS in the Northern Hemisphere (NH) during the last decade3-6, triggering renovated interest in the wind speed changes. Here we show that the TS in the NH mid-latitudes will continue in all seasons throughout the 21st century, especially in summer. The recent reversal of TS is most likely a multi-decadal fluctuation related to the Pacific and Atlantic climate variations, rather than a secular trend. A new paradigm of the future TS is further proposed, which is related to an intensified subsidence inversion over the mid-latitudes, caused by enhanced tropical and subtropical convections. This study reveals the important role of global warming in reducing the near-surface wind speed on long time scales. The continuing TS means a long-term strategy for wind energy production needs to be developed, particularly for the NH mid-latitude countries.

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1605 ◽

2014 ◽

Vol 599-601 ◽

pp. 1605-1609 ◽

Cited By ~ 1

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.

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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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