Characteristics and causes of surface wind speed variations in Northwest China from 1979 to 2019

Near surface wind speed has significant impacts on ecological environment change and climate change. Based on the CN05.1 observation data (a gridded monthly dataset with the resolution of 0.25 latitude by 0.25 longitude over China), this study evaluated the ability of 25 Global Climate Models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating the wind speed in the Arid Region of Northwest China (ARNC) during 1971–2014. Then, the temporal and spatial variations in the surface wind speed of ARNC in the 21st century were projected under four Shared Socioeconomic Pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0, and SP5-8.5. The results reveal that the preferred-model ensemble (PME) can fairly evaluate the temporal and spatial distribution of surface wind speed with the temporal and spatial correlation coefficients exceeding 0.5 at the significance level of p = 0.05 when compared to the 25 single models and their ensemble mean. After deviation correction, the PME can reproduce the distribution characteristics of high wind speed in the east and low in the west, high in mountainous areas, and low in basins. Unfortunately, no models or model ensemble can accurately reproduce the decreasing magnitude of observed wind speed. In the 21st century, the surface wind speed in the ARNC is projected to increase under SSP1-2.6 scenario but will decrease remarkably under the other three scenarios. Moreover, the higher the emission scenarios, the more significant the surface wind speed decreases. Spatially, the wind speed will increase significantly in the west and southeast of Xinjiang, decrease in the north of Xinjiang and the south of Tarim Basin. What’s more, under the four scenarios, the surface wind speed will decrease in spring, summer and autumn, especially in summer, and increase in winter. The wind speed will decrease significantly in the north of Tianshan Mountains in summer, decrease significantly in the north of Xinjiang and the southern edge of Tarim Basin in spring and autumn, and increase in fluctuation with high values in Tianshan Mountains in winter.

Download Full-text

Estimation of Ocean Surface Wind Speed and Direction From Polarimetric Radiometry Data

10.21236/ada533831 ◽

2008 ◽

Author(s):

David R. Lyzenga

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Ocean Surface ◽

Surface Wind Speed ◽

Ocean Surface Wind

Download Full-text

Challenges in developing a high-quality surface wind-speed data-set for Australia

Australian Meteorological and Oceanographic Journal ◽

10.22499/2.6004.001 ◽

2010 ◽

Vol 60 (04) ◽

pp. 227-236 ◽

Cited By ~ 13

Author(s):

D Jakob

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

High Quality ◽

Data Set ◽

Wind Speed Data ◽

Quality Surface ◽

High Quality Surface

Download Full-text

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.

Download Full-text

Ocean surface wind speed retrieval from C-band quad-polarized SAR measurements at optimal spatial resolution

Remote Sensing Letters ◽

10.1080/2150704x.2020.1846220 ◽

2020 ◽

Vol 12 (2) ◽

pp. 155-164

Author(s):

He Fang ◽

William Perrie ◽

Gaofeng Fan ◽

Tao Xie ◽

Jingsong Yang

Keyword(s):

Wind Speed ◽

Spatial Resolution ◽

Surface Wind ◽

Ocean Surface ◽

Surface Wind Speed ◽

Ocean Surface Wind

Download Full-text

Lidar In-space Technology Experiment measurements of sea surface directional reflectance and the link to surface wind speed

Applied Optics ◽

10.1364/ao.37.005550 ◽

1998 ◽

Vol 37 (24) ◽

pp. 5550 ◽

Cited By ~ 24

Author(s):

Robert T. Menzies ◽

David M. Tratt ◽

William H. Hunt

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

Sea Surface ◽

Space Technology ◽

Directional Reflectance

Download Full-text

A Composite View of Surface Signatures and Interior Properties of Nonlinear Internal Waves: Observations and Applications

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2007jtecho574.1 ◽

2008 ◽

Vol 25 (7) ◽

pp. 1218-1227 ◽

Cited By ~ 26

Author(s):

Ming-Huei Chang ◽

Ren-Chieh Lien ◽

Yiing Jang Yang ◽

Tswen Yung Tang ◽

Joe Wang

Keyword(s):

Wind Speed ◽

Surface Waves ◽

Surface Wind ◽

Surface Wind Speed ◽

Horizontal Velocity ◽

Surface Scattering ◽

Full Width ◽

Empirical Formulas ◽

Marine Radar ◽

Scattering Strength

Abstract Surface signatures and interior properties of large-amplitude nonlinear internal waves (NLIWs) in the South China Sea (SCS) were measured during a period of weak northeast wind (∼2 m s−1) using shipboard marine radar, an acoustic Doppler current profiler (ADCP), a conductivity–temperature–depth (CTD) profiler, and an echo sounder. In the northern SCS, large-amplitude NLIWs propagating principally westward appear at the tidal periodicity, and their magnitudes are modulated at the spring–neap tidal cycle. The surface scattering strength measured by the marine radar is positively correlated with the local wind speed when NLIWs are absent. When NLIWs approach, the surface scattering strength within the convergence zone is enhanced. The sea surface scattering induced by NLIWs is equivalent to that of a ∼6 m s−1 surface wind speed (i.e., 3 times greater than the actual surface wind speed). The horizontal spatial structure of the enhanced sea surface scattering strength predicts the horizontal spatial structure of the NLIW. The observed average half-amplitude full width of NLIWs λη/2 is 1.09 ± 0.2 km; the average half-amplitude full width of the enhanced scattering strength λI/2 is ∼0.57 λη/2. The average half-amplitude full width of the enhanced horizontal velocity convergence of NLIWs λ∂xu/2 is approximately equal to λI/2. The peak of the enhanced surface scattering leads the center of NLIWs by ∼0.46 λη/2. NLIW horizontal velocity convergence is positively correlated with the enhancement of the surface scattering strength. NLIW amplitude is positively correlated with the spatial integration of the enhancement of the surface scattering strength within the convergence zone of NLIWs. Empirical formulas are obtained for estimating the horizontal velocity convergence and the amplitude of NLIWs using radar measurements of surface scattering strength. The enhancement of the scattering strength exhibits strong asymmetry; the scattering strength observed from behind the propagating NLIW is 24% less than that observed ahead, presumably caused by the skewness and the breaking of surface waves induced by NLIWs. Above the center of NLIWs, the surface scattering strength is enhanced slightly, associated with isotropic surface waves presumably induced or modified by NLIWs. This analysis concludes that in low-wind conditions remote sensing measurements may provide useful predictions of horizontal velocity convergences, amplitudes, and spatial structures of NLIWs. Further applications and modification of the presented empirical formulas in different conditions of wind speed, surface waves, and NLIWs or with other remote sensing methods are encouraged.

Download Full-text