scholarly journals 1980–2010 Variability in U.K. Surface Wind Climate

2013 ◽  
Vol 26 (4) ◽  
pp. 1172-1191 ◽  
Author(s):  
Nick Earl ◽  
Steve Dorling ◽  
Richard Hewston ◽  
Roland von Glasow
Keyword(s):  
Wind Speed ◽  
Decadal Variability ◽  
Surface Wind ◽  
Rayleigh Distribution ◽  
Daily Maximum ◽  
International Studies ◽  
Atlantic Sector ◽  
Wind Speeds ◽  
Low Wind Speed ◽  
Wind Climate

Abstract The climate of the northeast Atlantic region comprises substantial decadal variability in storminess. It also exhibits strong inter- and intra-annual variability in extreme high and low wind speed episodes. Here the authors quantify and discuss causes of the variability seen in the U.K. wind climate over the recent period 1980–2010. Variations in U.K. hourly mean (HM) wind speeds, in daily maximum gust speeds and in associated wind direction measurements, made at standard 10-m height and recorded across a network of 40 stations, are considered. The Weibull distribution is shown to generally provide a good fit to the hourly wind data, albeit with the shape parameter k spatially varying from 1.4 to 2.1, highlighting that the commonly assumed k = 2 Rayleigh distribution is not universal. It is found that the 10th and 50th percentile HM wind speeds have declined significantly over this specific period, while still incorporating a peak in the early 1990s. The authors' analyses place the particularly “low wind” year of 2010 into longer-term context and their findings are compared with other recent international studies. Wind variability is also quantified and discussed in terms of variations in the exceedance of key wind speed thresholds of relevance to the insurance and wind energy industries. Associated interannual variability in energy density and potential wind power output of the order of ±20% around the mean is revealed. While 40% of network average winds are in the southwest quadrant, 51% of energy in the wind is associated with this sector. The findings are discussed in the context of current existing challenges to improve predictability in the Euro-Atlantic sector over all time scales.

scholarly journals Model-Based Projections and Uncertainties of Near-Surface Wind Climate in Western Canada

2016 ◽  
Vol 55 (10) ◽  
pp. 2229-2245 ◽  
Author(s):  
Jeffrey T. Daines ◽  
Adam H. Monahan ◽  
Charles L. Curry
Keyword(s):  
Power Generation ◽  
Standard Deviation ◽  
Wind Speed ◽  
Wind Power ◽  
Surface Wind ◽  
Western Canada ◽  
Near Surface ◽  
Wind Speeds ◽  
Ensemble Mean ◽  
Wind Climate

AbstractNear-surface wind is important in forestry, agriculture, air pollution, building energy use, and wind power generation. In western Canada it presently plays a minor role in power generation, but ongoing reductions in the cost of wind power infrastructure and the increasing costs of conventional power generation (including environmental costs) motivate the assessment of the projected future wind climate and uncertainties in this projection. Multiple realizations of the Canadian Regional Climate Model (CRCM) at 45-km resolution were driven by two global climate models over the periods 1971–2000 (using historical greenhouse gas concentrations) and 2031–60 (using the SRES-A2 concentration scenario). Hourly wind speeds from 30 stations were analyzed over 1971–2000 and used to calibrate downscaled ensembles of projected wind speed distributions over 2031–60. At most station locations modest increases in mean wind speed were found for a majority of the projections, but with an ensemble spread of the same order of magnitude as the increases. Relative changes in mean wind speeds at station locations were found to be insensitive to the station observations and calibration technique. In view of this result, projected relative changes in future wind climate over the entire CRCM domain were estimated using uncalibrated pairs of past-period and future-period wind speed distributions. The relative changes are robust, in the sense that their ensemble mean relative change is greater than their standard deviation, but are not very substantial, in the sense that their ensemble mean change is generally less than the standard deviation of their annual means.

scholarly journals Point Downscaling of Surface Wind Speed for Forecast Applications

2018 ◽  
Vol 57 (3) ◽  
pp. 659-674 ◽  
Author(s):  
Brian H. Tang ◽  
Nick P. Bassill
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Spectral Density ◽  
Power Spectral Density ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
High Frequency Component ◽  
Daily Maximum ◽  
Wind Speeds ◽  
Power Spectral

AbstractA statistical downscaling algorithm is introduced to forecast surface wind speed at a location. The downscaling algorithm consists of resolved and unresolved components to yield a time series of synthetic wind speeds at high time resolution. The resolved component is a bias-corrected numerical weather prediction model forecast of the 10-m wind speed at the location. The unresolved component is a simulated time series of the high-frequency component of the wind speed that is trained to match the variance and power spectral density of wind observations at the location. Because of the stochastic nature of the unresolved wind speed, the downscaling algorithm may be repeated to yield an ensemble of synthetic wind speeds. The ensemble may be used to generate probabilistic predictions of the sustained wind speed or wind gusts. Verification of the synthetic winds produced by the downscaling algorithm indicates that it can accurately predict various features of the observed wind, such as the probability distribution function of wind speeds, the power spectral density, daily maximum wind gust, and daily maximum sustained wind speed. Thus, the downscaling algorithm may be broadly applicable to any application that requires a computationally efficient, accurate way of generating probabilistic forecasts of wind speed at various time averages or forecast horizons.

scholarly journals Stilling and Recovery of the Surface Wind Speed Based on Observation, Reanalysis, and Geostrophic Wind Theory over China from 1960 to 2017

2020 ◽  
Vol 33 (10) ◽  
pp. 3989-4008 ◽  
Author(s):  
Zhengtai Zhang ◽  
Kaicun Wang
Keyword(s):  
Wind Speed ◽  
Decadal Variability ◽  
Surface Wind ◽  
Geostrophic Wind ◽  
Surface Wind Speed ◽  
Strong Wind ◽  
Wind Speeds ◽  
The North ◽  
Wind Theory ◽  
Weak Winds

AbstractSurface wind speed (SWS) from meteorological observation, global atmospheric reanalysis, and geostrophic wind speed (GWS) calculated from surface pressure were used to study the stilling and recovery of SWS over China from 1960 to 2017. China experienced anemometer changes and automatic observation transitions in approximately 1969 and 2004, resulting in SWS inhomogeneity. Therefore, we divided the entire period into three sections to study the SWS trend, and found a near-zero annual trend in the SWS in China from 1960 to 1969, a significant decrease of −0.24 m s−1 decade−1 from 1970 to 2004, and a weak recovery from 2005 to 2017. By defining the 95th and 5th percentiles of daily mean wind speeds as strong and weak winds, respectively, we found that the SWS decrease was primarily caused by a strong wind decrease of −8% decade−1 from 1960 to 2017, but weak wind showed an insignificant decreasing trend of −2% decade−1. GWS decreased with a significant trend of −3% decade−1 before the 1990s; during the 1990s, GWS increased with a trend of 3% decade−1 whereas SWS continued to decrease with a trend of 10% decade−1. Consistent with SWS, GWS demonstrated a weak increase after the 2000s. After detrending, both SWS and GWS showed synchronous decadal variability, which is related to the intensity of Aleutian low pressure over the North Pacific. However, current reanalyses cannot reproduce the decadal variability and cannot capture the decreasing trend of SWS either.

Stilling and Recovery of the Surface Wind Speed Based on Observation, Reanalysis, and Geostrophic Wind Theory over China from 1960 to 2017

2020 ◽  
Author(s):  
Zhengtai Zhang ◽  
Kaicun Wang
Keyword(s):  
Wind Speed ◽  
Decadal Variability ◽  
Surface Wind ◽  
Geostrophic Wind ◽  
Surface Wind Speed ◽  
Strong Wind ◽  
Wind Speeds ◽  
The North ◽  
Wind Theory ◽  
Weak Winds

<p>Surface wind speed (SWS) from meteorological observation, global atmospheric reanalysis, and geostrophic wind speed (GWS) calculated from surface pressure were used to study the stilling and recovery of SWS over China from 1960 to 2017. China experienced anemometer changes and automatic observation transitions in approximately 1969 and 2004, resulting in SWS inhomogeneity. Therefore, we divided the entire period into three sections to study the SWS trend, and found a near zero annual trend in the SWS in China from 1960 to 1969, a significant decrease of -0.24 m/s decade<sup>-1 </sup>from 1970 to 2004, and a weak recovery from 2005 to 2017. By defining the 95<sup>th</sup> and 5<sup>th</sup> percentiles of monthly mean wind speeds as strong and weak winds, respectively, we found that the SWS decrease was primarily caused by a strong wind decrease of -8 % decade<sup>-1</sup> from 1960 to 2017, but weak wind showed an insignificant decreasing trend of -2 % decade<sup>-1</sup>. GWS decreased with a significant trend of -3 % decade<sup>-1 </sup>before the 1990s, during the 1990s, GWS increased with a trend of 3 % decade<sup>-1 </sup>whereas SWS continued to decrease with a trend of 10 % decade<sup>-1</sup>. Consistent with SWS, GWS demonstrated a weak increase after the 2000s. After detrended, both of SWS and GWS showed synchronous decadal variability, which is related to the intensity of Aleutian low pressure over the North Pacific. However, current reanalyses cannot reproduce the decadal variability, and can not capture the decreasing trend of SWS either.</p>

scholarly journals Diurnal Variations in Surface Wind over the Tibetan Plateau

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 112 ◽  
Author(s):  
Yufei Zhao ◽  
Jianping Li ◽  
Qiang Zhang ◽  
Xiaowei Jiang ◽  
Aixia Feng
Keyword(s):  
Wind Speed ◽  
Tibetan Plateau ◽  
Qaidam Basin ◽  
Surface Wind ◽  
Diurnal Variations ◽  
Maximum Speed ◽  
Daily Maximum ◽  
Eastern Region ◽  
The Tibetan Plateau ◽  
Wind Speeds

This study uses hourly surface wind direction and wind speed observations from 53 meteorological stations on the Tibetan Plateau (TP) (70–105° E, 25–45° N) between 1995 and 2017 to investigate diurnal variations in the surface wind. The results show large diurnal variations in surface wind on the TP. The minimum wind speed occurs in the morning and the maximum in the afternoon. In all four seasons, the prevailing meridional wind is a southerly, and this is typically evident for more than two-thirds of each day. However, in the mornings during December–February and September–November, this southerly wind is replaced by a northerly, but remains southerly in the afternoon. The TP shows remarkable regional characteristics with respect to diurnal variations in wind speed. In the eastern region, the minimum and maximum daily wind speeds occur about 1 h later than in the west. Among the 53 meteorological stations, 79% observed that it took less time for the minimum speed to rise to the maximum speed than for the maximum to drop to the minimum. The blocking effect of the high surrounding terrain causes the diurnal variations seen in the surface winds at the three stations in the Qaidam Basin to differ significantly from those observed at the other stations elsewhere on the plateau. These Qaidam Basin stations recorded their maximum wind speeds around noon, with the minimum at dusk, which is around 1900 LST. The EOF1 (EOF = empirical orthogonal function) of the hourly wind speed on the TP indicates the key daily circulation feature of the region; i.e., the wind speed is high in the afternoon and low in the morning. The EOF2 reflects the regional differences in the diurnal variations of wind speed on the TP; i.e., the eastern region reaches the daily maximum and minimum wind speeds slightly later than the western region.

scholarly journals Topographic Speed-Up Effects and Observed Roof Damage on Bermuda following Hurricane Fabian (2003)

2013 ◽  
Vol 28 (1) ◽  
pp. 159-174 ◽  
Author(s):  
Craig Miller ◽  
Michael Gibbons ◽  
Kyle Beatty ◽  
Auguste Boissonnade
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Surface Wind ◽  
Open Water ◽  
Clear Trend ◽  
Wind Speeds ◽  
Research Division ◽  
Hurricane Wind ◽  
Layer Flow ◽  
Observed Damage

Abstract In this study the impacts of the topography of Bermuda on the damage patterns observed following the passage of Hurricane Fabian over the island on 5 September 2003 are considered. Using a linearized model of atmospheric boundary layer flow over low-slope topography that also incorporates a model for changes of surface roughness, sets of directionally dependent wind speed adjustment factors were calculated for the island of Bermuda. These factors were then used in combination with a time-stepping model for the open water wind field of Hurricane Fabian derived from the Hurricane Research Division Real-Time Hurricane Wind Analysis System (H*Wind) surface wind analyses to calculate the maximum 1-min mean wind speed at locations across the island for the following conditions: open water, roughness changes only, and topography and roughness changes combined. Comparison of the modeled 1-min mean wind speeds and directions with observations from a site on the southeast coast of Bermuda showed good agreement between the two sets of values. Maximum open water wind speeds across the entire island showed very little variation and were of category 2 strength on the Saffir–Simpson scale. While the effects of surface roughness changes on the modeled wind speeds showed very little correlation with the observed damage, the effect of the underlying topography led to maximum modeled wind speeds of category 4 strength being reached in highly localized areas on the island. Furthermore, the observed damage was found to be very well correlated with these regions of topographically enhanced wind speeds, with a very clear trend of increasing damage with increasing wind speeds.

Climate Conditions at Perennially Ice-Covered Lake Untersee, East Antarctica

2015 ◽  
Vol 54 (7) ◽  
pp. 1393-1412 ◽  
Author(s):  
Dale T. Andersen ◽  
Christopher P. McKay ◽  
Victor Lagun
Keyword(s):  
Wind Speed ◽  
Austral Summer ◽  
Solar Flux ◽  
East Antarctica ◽  
Daily Maximum ◽  
Mean Annual Temperature ◽  
Secondary Source ◽  
Climate Conditions ◽  
Wind Speeds ◽  
Strong Winds

AbstractIn November 2008 an automated meteorological station was established at Lake Untersee in East Antarctica, producing a 5-yr data record of meteorological conditions at the lake. This dataset includes five austral summer seasons composed of December, January, and February (DJF). The average solar flux at Lake Untersee for the four years with complete solar flux data is 99.2 ± 0.6 W m−2. The mean annual temperature at Lake Untersee was determined to be −10.6° ± 0.6°C. The annual degree-days above freezing for the five years were 9.7, 37.7, 22.4, 7.0, and 48.8, respectively, with summer (DJF) accounting for virtually all of this. For these five summers the average DJF temperatures were −3.5°, −1.9°, −2.2°, −2.6°, and −2.5°C. The maximum (minimum) temperatures were +5.3°, +7.6°, +5.7°, +4.4°, and +9.0°C (−13.8°, −12.8°, −12.9°, −13.5°, and −12.1°C). The average of the wind speed recorded was 5.4 m s−1, the maximum was 35.7 m s−1, and the average daily maximum was 15 m s−1. The wind speed was higher in the winter, averaging 6.4 m s−1. Summer winds averaged 4.7 m s−1. The dominant wind direction for strong winds is from the south for all seasons, with a secondary source of strong winds in the summer from the east-northeast. Relative humidity averages 37%; however, high values will occur with an average period of ~10 days, providing a strong indicator of the quasi-periodic passage of storms across the site. Low summer temperatures and high wind speeds create conditions at the surface of the lake ice resulting in sublimation rather than melting as the main mass-loss process.

scholarly journals Wind speed modeling based on measurement data to predict future wind speed with modified Rayleigh model

2021 ◽  
Vol 12 (3) ◽  
pp. 1823
Author(s):  
Suwarno Suwarno ◽  
Rohana Rohana
Keyword(s):  
Wind Speed ◽  
Measurement Data ◽  
Rayleigh Distribution ◽  
Scale Factor ◽  
Percentage Error ◽  
Monthly Data ◽  
Wind Speeds ◽  
Wind Characteristics ◽  
One Year ◽  
The Many

The development of modeling wind speed plays a very important in helping to obtain the actual wind speed data for the benefit of the power plant planning in the future. The wind speed in this paper is obtained from a PCE-FWS 20 type measuring instrument with a duration of 30 minutes which is accumulated into monthly data for one year (2019). Despite the many wind speed modeling that has been done by researchers. Modeling wind speeds proposed in this study were obtained from the modified Rayleigh distribution. In this study, the Rayleigh scale factor (<em>C<sub>r</sub></em>) and modified Rayleigh scale factor (<em>C<sub>m</sub></em>) were calculated. The observed wind speed is compared with the predicted wind characteristics. The data fit test used correlation coefficient (R<sup>2</sup>), root means square error (RMSE), and mean absolute percentage error (MAPE). The results of the proposed modified Rayleigh model provide very good results for users.

Near-surface wind speeds in ERA5: Climatology, decadal variability and long-term trends

2021 ◽  
Author(s):  
Terhi K. Laurila ◽  
Victoria A. Sinclair ◽  
Hilppa Gregow
Keyword(s):  
Iberian Peninsula ◽  
North Atlantic ◽  
Decadal Variability ◽  
Surface Wind ◽  
Northern Europe ◽  
Near Surface ◽  
Wind Speeds ◽  
The North ◽  
The North Atlantic

&lt;p&gt;The knowledge of long-term climate and variability of near-surface wind speeds is essential and widely used among meteorologists, climate scientists and in industries such as wind energy and forestry. The new high-resolution ERA5 reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF) will likely be used as a reference in future climate projections and in many wind-related applications. Hence, it is important to know what is the mean climate and variability of wind speeds in ERA5.&lt;/p&gt;&lt;p&gt;We present the monthly 10-m wind speed climate and decadal variability in the North Atlantic and Europe during the 40-year period (1979-2018) based on ERA5. In addition, we examine temporal time series and possible trends in three locations: the central North Atlantic, Finland and Iberian Peninsula. Moreover, we investigate what are the physical reasons for the decadal changes in 10-m wind speeds.&lt;/p&gt;&lt;p&gt;The 40-year mean and the 98th percentile wind speeds show a distinct contrast between land and sea with the strongest winds over the ocean and a seasonal variation with the strongest winds during winter time. The winds have the highest values and variabilities associated with storm tracks and local wind phenomena such as the mistral. To investigate the extremeness of the winds, we defined an extreme find factor (EWF) which is the ratio between the 98th percentile and mean wind speeds. The EWF is higher in southern Europe than in northern Europe during all months. Mostly no statistically significant linear trends of 10-m wind speeds were found in the 40-year period in the three locations and the annual and decadal variability was large.&lt;/p&gt;&lt;p&gt;The windiest decade in northern Europe was the 1990s and in southern Europe the 1980s and 2010s. The decadal changes in 10-m wind speeds were largely explained by the position of the jet stream and storm tracks and the strength of the north-south pressure gradient over the North Atlantic. In addition, we investigated the correlation between the North Atlantic Oscillation (NAO) and the Atlantic Multi-decadal Oscillation (AMO) in the three locations. The NAO has a positive correlation in the central North Atlantic and Finland and a negative correlation in Iberian Peninsula. The AMO correlates moderately with the winds in the central North Atlantic but no correlation was found in Finland or the Iberian Peninsula. Overall, our study highlights that rather than just using long-term linear trends in wind speeds it is more informative to consider inter-annual or decadal variability.&lt;/p&gt;

scholarly journals Frequency Distribution Model of Wind Speed Based on the Exponential Polynomial for Wind Farms

2019 ◽  
Vol 11 (3) ◽  
pp. 665 ◽  
Author(s):  
Lingzhi Wang ◽  
Jun Liu ◽  
Fucai Qian
Keyword(s):  
Wind Speed ◽  
Frequency Distribution ◽  
Least Squares Method ◽  
Wind Farms ◽  
Rayleigh Distribution ◽  
Distribution Model ◽  
Exponential Polynomial ◽  
Distribution Models ◽  
Wind Speed Data ◽  
Wind Speeds

This study introduces and analyses existing models of wind speed frequency distribution in wind farms, such as the Weibull distribution model, the Rayleigh distribution model, and the lognormal distribution model. Inspired by the shortcomings of these models, we propose a distribution model based on an exponential polynomial, which can describe the actual wind speed frequency distribution. The fitting error of other common distribution models is too large at zero or low wind speeds. The proposed model can solve this problem. The exponential polynomial distribution model can fit multimodal distribution wind speed data as well as unimodal distribution wind speed data. We used the linear-least-squares method to acquire the parameters for the distribution model. Finally, we carried out contrast simulation experiments to validate the effectiveness and advantages of the proposed distribution model.

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