2020
DOI: 10.1175/jcli-d-19-0603.1
|View full text |Cite
|
Sign up to set email alerts
|

Variability of Daily Maximum Wind Speed across China, 1975–2016: An Examination of Likely Causes

Abstract: Assessing change in daily maximum wind speed and its likely causes is crucial for many applications such as wind power generation and wind disaster risk governance. Multidecadal variability of observed near-surface daily maximum wind speed (DMWS) from 778 stations over China is analyzed for 1975–2016. A robust homogenization protocol using the R package Climatol was applied to the DMWS observations. The homogenized dataset displayed a significant (p < 0.05) declining trend of −0.038 m s−1 decade−1 for a… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

2
27
0

Year Published

2020
2020
2022
2022

Publication Types

Select...
7

Relationship

3
4

Authors

Journals

citations
Cited by 44 publications
(29 citation statements)
references
References 77 publications
2
27
0
Order By: Relevance
“…According to Geostrophic Approximation Theory (Lin et al., 2013), near‐surface wind speed variability over northern China may be explained by differences in near‐surface air temperature and pressure between the high‐latitude zone (50°–60°N) and the northern China zone (35°–45°N) both between 75°E and 135°E. Following previous studies (Y. Li et al., 2018; Zhang et al., 2020), the meridional air temperature and pressure gradient with respect to latitude was defined as: SG=ABS[i=1nCHLii=1nCNCin] where SG is the gradient in either air temperature (°C) or pressure (hPa); ABS means absolute value; n is the total number of grid cells at a given latitude; and CHL and CNC are the near‐surface air temperature or pressure for grid cells in the high‐latitude zone and the northern China zone, respectively.…”
Section: Methodsmentioning
confidence: 68%
See 2 more Smart Citations
“…According to Geostrophic Approximation Theory (Lin et al., 2013), near‐surface wind speed variability over northern China may be explained by differences in near‐surface air temperature and pressure between the high‐latitude zone (50°–60°N) and the northern China zone (35°–45°N) both between 75°E and 135°E. Following previous studies (Y. Li et al., 2018; Zhang et al., 2020), the meridional air temperature and pressure gradient with respect to latitude was defined as: SG=ABS[i=1nCHLii=1nCNCin] where SG is the gradient in either air temperature (°C) or pressure (hPa); ABS means absolute value; n is the total number of grid cells at a given latitude; and CHL and CNC are the near‐surface air temperature or pressure for grid cells in the high‐latitude zone and the northern China zone, respectively.…”
Section: Methodsmentioning
confidence: 68%
“…Note that differences in wind speed evolution among seasons were not fully related to the differences of the air temperature and pressure gradients, which means that other physical processes also affected wind speed. For instance, wind speed in northern China was partly regulated by synoptic circulation systems (i.e., extratropical cyclones, Zhang et al., 2020). Increased surface roughness resulting from forest growth and urbanization is another cause for global terrestrial wind stilling (Z. Li et al., 2018; Vautard et al., 2010; Z. Zhang et al., 2019).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…There are frequent sandstorms, and the average annual wind speed is 2.2 m s −1 , with a maximum of 3.0 m s −1 in April and a minimum of 1.7 m s −1 in September. The average seasonal wind speeds are 2.8 m s −1 in spring, 2.2 m s −1 in summer, 1.8 m s −1 in autumn, and 2.0 m s −1 in winter, and the daily average maximum wind speeds are 9.4 m s −1 in spring, 8.0 m s −1 in summer, 8.0 m s −1 in autumn, and 8.3 m s −1 in winter, respectively (Zhang et al, 2020). The primary vegetation includes Artemisia ordosica and Salix cheilophila Schneid , with sparsely distributed Phyllostachys propinqua , Hedysarum scoparium , Hedysarum fruticosum , and Setaria viridis (L.) Beauv (Yuan, Zhang, Bu, Yang, & Yuan, 2016).…”
Section: Methodsmentioning
confidence: 99%
“…While the soil moisture algorithm has been used at a small number of the Mid-Atlantic observation sites and has been introduced in previous studies (Smith and Chang 2020), it has not been applied to region-wide data capturing the majority of certified locations. Similarly, while Climatol has been utilized in studies on several continents (e.g., Zhang et al 2020), it had not yet been deployed with North American data nor with trends in evapotranspiration. This analysis is also the first in which the algorithms have been paired.…”
Section: Scopementioning
confidence: 99%