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The characteristics of eolian sand activity are greatly influenced by the wind regime, and wind regimes have been changing around the world in response to climate change. This has also been true in the desert area of northwestern China since 1965, and these changes have changed the region’s landforms, sandstorm frequency, and desertification. In this study, we analyzed the temporal and spatial variation of the region’s near-surface wind field since 1965. We found an average annual wind speed during this period of 1.7 m s−1, with a decreasing trend from 1965 to 2000 and an increasing trend from 2000 to 2015. The maximum rate of decrease occurred in the spring and in the eastern Taklimakan Desert. The variation of the average wind speed depended on the frequency of winds strong enough to entrain sand (with a wind speed > 6 m s−1). We also found that variations of the drift potential were primarily controlled by three prevailing wind groups (winds from the northwest, north, and northeast), but showed complex changes between seasons and regions. The wind direction in the Taklimakan Desert is characterized by two characteristics of branch and steering, the branch line is swinging in the direction of the east and the west (81.5° E~84° E). The changes in wind speed were mainly caused by a decreased frequency of strong winds, precipitation, and urban development. However, the variation of wind speed had less impact on the desert environment than the variation of wind direction.

Several major storms pounded western Europe in January 2018, generating large damages and casualties. The two most impactful ones, Eleanor and Friederike, are analysed here in the context of climate change. Near surface wind speed station observations exhibit a decreasing trend in the frequency of strong winds associated with such storms. High-resolution regional climate models, on the other hand, show no trend up to now and a small increase in storminess in future due to climate change. This shows that factors other than climate change, which are not in the climate models, caused the observed decline in storminess over land. A large part is probably due to increases in surface roughness, as shown for a small set of stations covering the Netherlands and in previous studies. This observed trend could therefore be independent from climate evolution. We concluded that human-induced climate change has had so far no significant influence on storms like the two mentioned. However, all simulations indicate that global warming could lead to a marginal increase (0 %–20 %) in the probability of extreme hourly winds until the middle of the century, consistent with previous modelling studies. This excludes other factors, such as surface roughness, aerosols, and decadal variability, which have up to now caused a much larger negative trend. Until these factors are correctly simulated by climate models, we cannot give credible projections of future storminess over land in Europe.

The performance of analog-based and Kalman filter (KF) postprocessing methods is tested in climatologically and topographically different regions for point-based wind speed predictions at 10 m above the ground. The results are generated using several configurations of the mesoscale numerical weather prediction model ALADIN. This study shows that deterministic analog-based predictions (ABPs) improve the correlation between predictions and measurements while reducing the forecast error, with respect to both the starting model predictions and the KF-based correction. While the KF generally outperforms the ABPs in bias reduction, the combination of the KF and analog approach can be similarly successful. In the coastal complex area, characterized with a larger frequency of strong wind, the ABPs are more successful in reducing the dispersion error than the KF. The application of the KF algorithm to the analogs in the so-called analog space (KFAS) is the least prone to standard deviation underestimation among the ABPs. All ABPs improve the prediction of larger-than-diurnal motions, and KFAS is superior among all ABPs in predicting alternating wind regimes on time scales shorter than a day. The ABPs better distinguish different wind speed categories in the coastal complex terrain by using a higher-resolution model input. Differences among starting model and postprocessed forecasts in other types of terrain are less pronounced.

A 5-yr climatology of westerly wind events in Owens Valley, California, is derived from data measured by a mesoscale network of 16 automatic weather stations. Thermally driven up- and down-valley flows are found to account for a large part of the diurnal wind variability in this approximately north–south-oriented deep U-shaped valley. High–wind speed events at the western side of the valley deviate from this basic pattern by showing a higher percentage of westerly winds. In general, strong westerly winds in Owens Valley tend to be more persistent and to display higher sustained speeds than strong winds from other quadrants. The highest frequency of strong winds at the valley floor is found in the afternoon hours from April to September, pointing to thermal forcing as a plausible controlling mechanism. However, the most intense westerly wind events (westerly windstorms) can happen at any time of the day throughout the year. The temperature and humidity variations caused by westerly windstorms depend on the properties of the approaching air masses. In some cases, the windstorms lead to overall warming and drying of the valley atmosphere, similar to foehn or chinook intrusions. The key dynamical driver of westerly windstorms in Owens Valley is conjectured to be the downward penetration of momentum associated with mountain waves produced by the Sierra Nevada ridgeline to the west of the valley.

The instrumental observations of dust storm (DS) in China and in most countries of the world have only a history of 50–60 years, and the DS variability beyond this timescale cannot be understood properly. Here, we show that the DS frequency can be reconstructed using the coral reef environment records as a proxy. Based on the high-resolution sea surface temperature (SST) records previously reconstructed by Liu et al. (2008) and Sun et al. (2004), we reconstructed the variations of DS frequency and strong wind frequency in China from 1908 to 1959, using the 5-year moving average of the longitudinal SST gradient (GX-H,SST,5m) in the northern South China Sea (SCS) as an indicator. The calibration equation shows that GX-H,SST,5m explains 66% of the variation in the 5-year average of the DS frequency (FDS,5m) and 86% of the variation in the 5-year moving average of strong wind (FSW,5m) in China, respectively. A comparison between the reconstructed long series (1908–1990) and the observed short series (1960–1990) FDS,5m indicates that the mean, maximum, and minimum of the latter series is 10.8, 20.9, and 36.1% smaller than that of the former, demonstrating that the DS frequency strongly depends on timescales; the statistical characteristics over short timescales are quite different from those over long timescales.

Wind energy, an important component of clean energy, is highly dictated by the disposable wind speed within the working regime of wind turbines (typically between 3 and 25 m s −1 at the hub height). Following a continuous reduction (‘stilling’) of global annual mean surface wind speed (SWS) since the 1960s, recently, researchers have reported a ‘reversal’ since 2011. However, little attention has been paid to the evolution of the effective wind speed for wind turbines. Since wind speed at hub height increases with SWS through power law, we focus on the wind speed frequency variations at various ranges of SWS through hourly in-situ observations and quantify their contributions to the average SWS changes over 1981–2021. We found that during the stilling period (here 1981–2010), the strong SWS (⩾ 5.0 m s −1 , the 80th of global SWS) with decreasing frequency contributed 220.37% to the continuous weakening of mean SWS. During the reversal period of SWS (here 2011–2021), slight wind (0 m s −1 < SWS < 2.9 m s −1 ) contributed 64.07% to a strengthening of SWS. The strengthened strong wind (⩾ 5.0 m s −1 ) contributed 73.38% to the trend change of SWS from decrease to increase in 2010. Based on the synthetic capacity factor series calculated by considering commercial wind turbines (General Electric GE 2.5-120 model with rated power 2.5 MW) at the locations of the meteorological stations, the frequency changes resulted in a reduction of wind power energy (−10.02 TWh yr −1 , p < 0.001) from 1981 to 2010 and relatively weak recovery (2.67 TWh yr −1 , p < 0.05) during 2011–2021.

Limited knowledge exists about ∼100-m-scale precipitation processes within U.S. northeast coastal snowstorms because of a lack of high-resolution observations. We investigate characteristics of microscale updraft regions within the cyclone comma head and their relationships with snowbands, wind shear, frontogenesis, and vertical mass flux using high-spatiotemporal-resolution vertically pointing Ka-band radar measurements, soundings, and reanalysis data for four snowstorms observed at Stony Brook, New York. Updraft regions are defined as contiguous time–height plotted areas with upward Doppler velocity without hydrometeor sedimentation that is equal to or greater than 0.4 m s −1 . Most updraft regions in the time–height data occur on a time scale of seconds (<20 s), which is equivalent to spatial scales < 500 m. These small updraft regions within cloud echo occur more than 30% of the time for three of the four cases and 18% for the other case. They are found at all altitudes and can occur with or without frontogenesis and with or without snowbands. The updraft regions with relatively large Doppler spectrum width (>0.4 m s −1 ) occur more frequently within midlevels of the storms, where there are strong wind shear layers and moist shear instability layers. This suggests that the dominant forcing for the updrafts appears to be turbulence associated with the vertical shear instability. The updraft regions can be responsible for upward mass flux when they are closer together in space and time. The higher values of column mean upward mass flux often occur during snowband periods.

The sub-daily variability of latent ( LHF ) and sensible heat flux ( SHF ) in the Bay of Bengal (BoB) during the summer (May–September) is examined using moored buoys data at 8° N (2008 and 2011), 12° N (2010, 2011, 2012, 2013, 2014, and 2015), and 15° N (2009, 2013, 2014, and 2015) along 90° E. In the weak wind regime (< 6 ms −1 ), LHF loss from the ocean shows semi-diurnal variability with a higher range at 8° N (~ 21 Wm −2 ) than at 12° N (~ 13 Wm −2 ) and 15° N (~ 8 Wm −2 ). However, LHF depicts a diurnal variability in the strong wind regime (> 6 ms −1 ) with a range of ~ 13 Wm −2 at 8° N and ~ 17 Wm −2 at 12° N and 15° N. In the strong wind regime, SHF shows heat gain by the ocean with a maximum (minimum) value during the daytime (night), while it shows heat loss from the ocean in the weak wind regime with maximum (minimum) value during the night (daytime). The diurnal range of SHF does not show significant meridional variation in the strong (~ 3.5 Wm −2 ) and weak (~ 2 Wm −2 ) wind regime. The difference in sub-daily evolution of air-temperature, air-specific humidity, and wind speed determines distinct evolutions of LHF and SHF in different wind regimes, which appears to be driven by atmospheric boundary layer processes and eastward propagating land-sea breeze signals over the BoB. Finally, we also establish the relationship between sub-daily evolutions of turbulent heat fluxes in the different wind regimes with synoptic conditions associated with the active and break phases of the Indian summer monsoon.

It is important to investigate the effects of current on wind waves, called the Doppler shift, at both normal and extremely high wind speeds. Three different types of wind-wave tanks along with a fan and pump are used to demonstrate wind waves and currents in laboratories at Kyoto University, Japan, Kindai University, Japan, and the Institute of Applied Physics, Russian Academy of Sciences, Russia. Profiles of the wind and current velocities and the water-level fluctuation are measured. The wave frequency, wavelength, and phase velocity of the significant waves are calculated, and the water velocities at the water surface and in the bulk of the water are also estimated by the current distribution. The study investigated 27 cases with measurements of winds, waves, and currents at wind speeds ranging from 7 to 67 m s−1. At normal wind speeds under 30 m s−1, wave frequency, wavelength, and phase velocity depend on wind speed and fetch. The effect of the Doppler shift is confirmed at normal wind speeds; i.e., the significant waves are accelerated by the surface current. The phase velocity can be represented as the sum of the surface current and artificial phase velocity, which is estimated by the dispersion relation of the deepwater waves. At extremely high wind speeds over 30 m s−1, a similar Doppler shift is observed as under the conditions of normal wind speeds. This suggests that the Doppler shift is an adequate model for representing the acceleration of wind waves by current, not only for wind waves at normal wind speeds but also for those with intensive breaking at extremely high wind speeds. A weakly nonlinear model of surface waves at a shear flow is developed. It is shown that it describes dispersion properties well not only for small-amplitude waves but also strongly nonlinear and even breaking waves, which are typical for extreme wind conditions (over 30 m s−1).

In efforts to fulfill the objectives of taking part in pragmatic cooperation in the Arctic, constructing the “Silk Road on Ice”, and ensuring ships’ safety and risk assessment in the Arctic, the two biggest hazards, which concern ships’ navigation in the Arctic, are wind and sea ice. Sea ice can result in a ship being besieged or crashing into an iceberg, endangering both human and property safety. Meanwhile, light winds can assist ships in breaking free of a sea-ice siege, whereas strong winds can hinder ships’ navigation. In this work, we first calculated the spatial and temporal characteristics of a number of indicators, including Arctic wind speed, sea-ice density, the frequency of different wind directions, the frequency of a sea-ice density of less than 20%, the frequency of strong winds of force six or above, etc. Using the ERA5 wind field and the SSMI/S sea-ice data, and applying statistical techniques, we then conducted a joint analysis to determine the correlation coefficients between the frequencies of various wind directions, the frequency of strong winds and its impact on the density of sea ice, the frequency of a sea-ice concentration (SIC) of less than 20%, and the correlation coefficient between winds and sea-ice density. In doing so, we determined importance of factoring the wind’s contribution into sea-ice analysis.