With the development of the economy, urban expansion is occurring throughout the world [1
]. By 2025, two-thirds of the world’s population is expected to live in cities [4
]. While cities enable more prosperous societies and promote people’s living standards, urban ecosystems are facing increasingly severe challenges. Environmental problems, including urban heat island effects, air and water pollution, biodiversity reductions, and resource shortages, are becoming common concerns for cities around the world [5
]. In addition, urbanization and the associated land use changes alter the energy and radiation balance [9
], and therefore, wind patterns over city regions may also change.
Near-surface wind, which is at the bottom of the atmosphere and close to the land surface, has many direct influences on the city life and environment. Since near-surface wind can harm forests, aviation, buildings, and other structures, detailed knowledge about the spatial and temporal characteristics of near-surface wind is of great interest for many socioeconomic sectors, including forest administrations, insurance companies, and local authorities [8
]. As a renewable and clean resource, wind energy can provide a solution to release the city air pollution and energy shortages [8
]. Wind research in cities is also helpful in solving some environmental problems. Previous studies have pointed out that urban heat islands tend to be weaker when there are strong winds [5
]. The change in wind direction could affect the concentrations of air pollution on city streets and the transport of air pollutants among different areas [6
]. On the other hand, wind research is also an important indicator in investigations of local climate change. A study by Wu et al. reported that the wind speed decrease in East China was more severe in large cities than in small cities during 1980–2011 [16
]. Moreover, as a part of the atmosphere, near-surface wind variability can also reflect the change in atmospheric circulation and help us to forecast the consequent changes in rainfall and temperature [17
]. Therefore, detailed knowledge of near-surface wind can offer useful insights into creating a sustainable environment for cities.
Studies on near-surface wind have a long history and have greatly improved the understanding of local and global wind climate. Among the many wind data metrics, wind spatial distribution is one of the most concerning characteristics, as it is useful in evaluating wind resources and finding notable wind patterns [19
]. Long-term wind speed trends are another important issue under the background of global climate change. Many areas, such as Spain, Portugal, and China, have been reported to have a downward trend over the past decades [21
]. Overall, the near-surface wind speed decline seems to be a global phenomenon [23
]. There are two main factors that affect wind speed. Changes in driving forces and changes in surface friction are thought to be responsible for the global wind decline, but for a local area, the reasons remain unclear and require further analysis [23
Climate variations, including wind variations, are often considered to be the result of complex nonlinear interactions among many degrees of freedom and patterns [24
]. Consequently, finding the independent patterns in the wind dataset is a challenging task. In the last several decades, thanks to the efforts of meteorologists, the empirical orthogonal function (EOF) approach has been well developed and widely used to reduce dimensionality and extract important patterns from atmospheric observations [24
]. Using the EOF method, Ludwig et al. [26
] revealed that the dominant physical process of surface wind in the valleys south of Great Salt Lake was the diurnal cycle of thermally induced flows. Based on the cluster analysis on the EOF method, Beaver and Palazoglu [28
] identified the times of occurrence for wind patterns affecting the local ozone composition in the San Francisco Bay Area. The main reason for the popularity of the EOF analysis is that it can concisely describe the entire dataset by using a few important patterns (i.e., EOFs), which cannot be completed using a simple metric such as averages [27
]. More importantly, the identified important patterns can be associated with different physical processes. However, despite the simplicity and physical interpretability, the EOF method is not guaranteed to provide a full picture of the atmosphere [29
Shenzhen, one of the most prosperous and densely populated areas in the world, is located on the southern coast of China, bordering Hong Kong to its south. Tropical cyclones, strong convection, and cold waves occur frequently, which may bring strong wind to the area and severely threaten the safety of people’s lives and property [30
]. Wind-related environmental issues, such as the heat island effect and air pollution, also exist in Shenzhen [34
]. However, due to the lack of relevant research, the detailed wind characteristics in Shenzhen are still unclear. The existing research is limited along the coastal area and strong wind cases caused by monsoon and tropical cyclones [36
In the past 20 years, many automatic weather stations (AWSs) have been established in Shenzhen, making it possible to study the near-surface wind characteristics using a high spatial resolution dataset. In this study, qualified hourly wind data from 92 AWSs in Shenzhen during the period 2009–2018 were investigated. The aim of this paper is to study the detailed spatiotemporal characteristics of the near-surface wind in Shenzhen. The rest of this paper is organized as follows. The data and methodologies are described in Section 2
. The results and discussion are shown in Section 3
, and the conclusions are summarized in Section 4
This study examines the spatiotemporal characteristics of the near-surface wind by using high spatial resolution data over Shenzhen from 2009 to 2018. The results show that the large-scale atmospheric circulation, local topographic factors, and human activities have combined to shape the intricate patterns of the near-surface winds in Shenzhen.
Wind is stronger along the coastal area than inland, and wind is generally stronger in eastern Shenzhen than in western Shenzhen. The GF tends to decline with increasing wind speed, and the spatial distribution of GFs is almost the opposite of the wind speed distribution. The GFs along the coastal area are concentrated in [1.4, 1.8], while the GFs in the central-western inland area are between 1.8 and 2.3, and the GFs in the northeastern inland area are concentrated in [1.7, 2.0].
Dominated by the large-scale atmospheric circulation, the near-surface wind field in Shenzhen has significant seasonal variation characteristics, which can be classified into two patterns, spring–summer type and autumn–winter type. The transitions between the two patterns occur in approximately March and September. In spring and summer, the prevailing wind directions are mainly southerlies, while the prevailing wind directions are northerlies in autumn and winter.
The annual and seasonal mean wind speeds tend to decrease at most of the stations in Shenzhen, especially the spring wind. During the past ten years, the annual mean surface avg-wind speed has decreased by approximately 0.4 ms−1. Since there is no significant downward trend in the wind speed of the troposphere aloft over Shenzhen, the decrease in the surface wind is mainly due to the urbanization development.