Future projections of the near-surface wind speed over eastern China based on CMIP5 datasets

2020 ◽  
Vol 54 (3-4) ◽  
pp. 2361-2385 ◽  
Author(s):  
Jinlin Zha ◽  
Jian Wu ◽  
Deming Zhao ◽  
Wenxuan Fan
Keyword(s):  
Wind Speed ◽  
Eastern China ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Near Surface ◽  
Future Projections

scholarly journals Slowdown and reversal of terrestrial near-surface wind speed and its future changes over eastern China

2021 ◽  
Vol 16 (3) ◽  
pp. 034028
Author(s):  
Jinlin Zha ◽  
Cheng Shen ◽  
Deming Zhao ◽  
Jian Wu ◽  
Wenxuan Fan
Keyword(s):  
Wind Speed ◽  
Eastern China ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Near Surface

A possible recovery of the near-surface wind speed in Eastern China during winter after 2000 and the potential causes

2018 ◽  
Vol 136 (1-2) ◽  
pp. 119-134 ◽  
Author(s):  
Jinlin Zha ◽  
Jian Wu ◽  
Deming Zhao ◽  
Jianping Tang
Keyword(s):  
Wind Speed ◽  
Eastern China ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Near Surface

scholarly journals Contribution of variations in Northern Hemisphere annular mode to the near-surface wind speed changes over Eastern China for 1979-2017

2021 ◽  
Author(s):  
Jinlin Zha ◽  
Cheng Shen ◽  
Jian Wu ◽  
Deming Zhao ◽  
Cesar Azorin-Molina
Keyword(s):  
Wind Speed ◽  
Temperature Gradient ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Eastern China ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Near Surface ◽  
Annular Mode ◽  
Northern Hemisphere Annular Mode

Abstract Studies have shown that large-scale ocean-atmosphere circulations (LOACs) played the major role to the near-surface wind speed (NWS) changes over China; however, the mechanisms whereby LOACs influences NWS to have received little attention. In this study, the processes of the Northern Hemisphere annular mode (NAM) influencing the NWS changes are revealed over eastern China for 1979–2017. The results showed a slowdown in NWS, at a rate of − 0.09 ± 0.01 m s− 1 decade− 1; meanwhile, this decline could be partly driven by the weakening of the zonal wind component. When the NAM exhibits positive phases, the zonal-mean westerly weakens at the low-to-mid-latitudes (10°–40°N); meanwhile, in the troposphere descending flows prevail near 40°N and ascending flows prevail near 65°N, and in the lower troposphere there are northerly anomalies at the low-to-mid-latitudes and southerly anomalies at mid-to-high latitudes (40°–70°N). The anomalous meridional flows transport heat from lower latitudes to higher latitudes and weaken the north–south air temperature gradient. The decreased air temperature gradient over East Asia reduces the pressure-gradient near the surface in eastern China, thereby decreasing the NWS. Furthermore, the effects of NAM on NWS changes are more significant at interannual scale than decadal scale. 32.0 ± 15.8 % of the changes in the annual mean NWS are caused by the variations in NAM; meanwhile, the NAM contribution to the interannual changes in the zonal component of NWS reach 45.0 ± 12.9 %.

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.

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