Spatiotemporal Characteristics of Near-Surface Wind in Shenzhen

The spatiotemporal characteristics of near-surface wind in Shenzhen were investigated in this study by using hourly observations at 92 automatic weather stations (AWSs) from 2009 to 2018. The results show that during the past 10 years, most of the stations showed a decreasing trend in the annual mea...

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Bibliographic Details
Main Authors: Cheng Liu, Qinglan Li, Wei Zhao, Yuqing Wang, Riaz Ali, Dian Huang, Xiaoxiong Lu, Hui Zheng, Xiaolin Wei
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:Sustainability
Subjects:
Online Access:https://www.mdpi.com/2071-1050/12/2/739
Description
Summary:The spatiotemporal characteristics of near-surface wind in Shenzhen were investigated in this study by using hourly observations at 92 automatic weather stations (AWSs) from 2009 to 2018. The results show that during the past 10 years, most of the stations showed a decreasing trend in the annual mean of the 10 min average wind speed (avg-wind) and the mean of the 3 s average wind speed (gust wind). Over half of the decreasing trends at the stations were statistically significant (<i>p</i> &lt; 0.05). Seasonally, the decrease in wind speed was the most severe in spring, followed by autumn, winter, and summer. The distribution of wind speed tends to be greater in the east and coastal areas for both avg-wind and gust wind. From September to March of the following year, the prevailing wind direction in Shenzhen was northerly, and from April to August, the prevailing wind direction was southerly. The seasonal wind speed distribution exhibited two different types, spring&#8722;summer type and autumn&#8722;winter type, which may be induced by their different prevailing wind directions. The analysis by the empirical orthogonal function (EOF) method confirmed the previous findings that the mean wind speed was decreasing in Shenzhen and that two different seasonal wind speed spatial distribution patterns existed. Such a study could provide references for wind forecasting and risk assessment in the study area.
ISSN:2071-1050