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 The Influence of Boundary Layer Processes on the Diurnal Variation of the Climatological Near-Surface Wind Speed Probability Distribution over Land*

2012 ◽  
Vol 25 (18) ◽  
pp. 6441-6458 ◽  
Author(s):  
Yanping He ◽  
Norman A. McFarlane ◽  
Adam H. Monahan
Keyword(s):  
Boundary Layer ◽  
Standard Deviation ◽  
Wind Speed ◽  
Probability Distribution ◽  
Wind Power ◽  
Land Surface ◽  
Vertical Structure ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Near Surface

Abstract Knowledge of the diurnally varying land surface wind speed probability distribution is essential for surface flux estimation and wind power management. Global observations indicate that the surface wind speed probability density function (PDF) is characterized by a Weibull-like PDF during the day and a nighttime PDF with considerably greater skewness. Consideration of long-term tower observations at Cabauw, the Netherlands, indicates that this nighttime skewness is a shallow feature connected to the formation of a stably stratified nocturnal boundary layer. The observed diurnally varying vertical structure of the leading three climatological moments of near-surface wind speed (mean, standard deviation, and skewness) and the wind power density at the Cabauw site can be successfully simulated using the single-column version of the Canadian Centre for Climate Modelling and Analysis (CCCma) fourth-generation atmospheric general circulation model (CanAM4) with a new semiempirical diagnostic turbulent kinetic energy (TKE) scheme representing downgradient turbulent transfer processes for cloud-free conditions. This model also includes a simple stochastic representation of intermittent turbulence at the boundary layer inversion. It is found that the mean and the standard deviation of wind speed are most influenced by large-scale “weather” variability, while the shape of the PDF is influenced by the intermittent mixing process. This effect is quantitatively dependent on the asymptotic flux Richardson number, which determines the Prandtl number in stable flows. High vertical resolution near the land surface is also necessary for realistic simulation of the observed fine vertical structure of wind speed distribution.

scholarly journals Wind potential assessment over Morocco`s Marrakesh - Safi Region in 2021-2050 based on the RCM`s forecasts as part of the CORDEX-Africa project

2021 ◽  
pp. 34-41
Author(s):  
Y. El. Hadri ◽  
M. Slizhe ◽  
K. Sernytska
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Atlantic Coast ◽  
Surface Wind ◽  
Specific Power ◽  
Wind Flow ◽  
Model Calculations ◽  
Near Surface ◽  
Wind Speeds ◽  
Wind Potential

The purpose of the study is to determine the features of the spatial distribution of the wind speed in Marrakesh - Safi region in 2021-2050, as well as the distribution of the specific power of the wind flow at various altitudes above the earth’s surface to determine the wind class of the area in the coming decades. Currently, the region has two large wind farms: Essaouira-Amogdoul and Tarfayer. To assess the future state of climate in Marrakesh − Safi region, the results of calculations of regional climate models (RCM) of the CORDEX-Africa project for the period 2021-2050 were used. The RCM modeling was carried out for the region of Africa, in a rectangular coordinate system with a spatial resolution of ~ 44 km. Model calculation was performed taking into account the greenhouse gas concentration trajectory of RCP 4.5. As a result of simulation for the period 2021-2050, mean monthly values of wind speed "sfcWind" (m·s-1) and the daily maximum near-surface wind speed "sfcwindmax" (m·s-1) at 10 m height were obtained. Then, based on the wind speed rows, the values of the wind power density at a height of 50 m and 100 m were calculated. The results of model calculations of wind speed showed that the ensemble mean of wind speed for the period 2021-2050 will be from 3.8 m∙s-1 in Kelaat Sraghna Province to 7.2 m∙s-1 on the stretch of the Atlantic coast between Cap Sim and Cap Tafelny.The distribution over the territory will be influenced by proximity to the ocean, models predict the highest wind speeds on the coast, and when moving deep into the region, the wind speed will decrease.The analysis of simulation results showed that in the coastal areas of the region favorable conditions in terms of wind energy development will remain, and the highest wind speeds of the model are predicted on the Atlantic coast between Cap Sim and Cap Tafelny. By the size of the specific power of the wind flow, significant wind resources will have the territory lying along the coast from Cap Sim to the southern border of the region, and in the area of the power plants Essaouira-Amogdoul and Tarfayer models predict the conditions corresponding to the outstanding wind power class.

scholarly journals Wind speed characteristics and implications for wind power generation: Cape regions, South Africa

2017 ◽  
Vol 113 (7/8) ◽  
Author(s):  
Marc A. Wright ◽  
Stefan W. Grab
Keyword(s):  
South Africa ◽  
Power Generation ◽  
Wind Speed ◽  
Coastal Zone ◽  
Temporal Dynamics ◽  
Surface Wind ◽  
Eastern Cape ◽  
Near Surface ◽  
Wind Speeds ◽  
The Mean

Spatio-temporal dynamics of near-surface wind speeds were examined across the Northern, Western and Eastern Cape regions of South Africa. The regions assessed were geographically subdivided into three zones: coastal, coastal hinterland and inland. Wind speed data (10 m) were evaluated at monthly, seasonal, annual and zonal resolutions, with the aim to establish wind speed attributes and trends. Data from 19 weather stations with high-resolution wind records between 1995 and 2014 were evaluated. The majority of stations (79%) recorded a decrease in mean annual wind speed over the study period. The mean rate of decrease across all stations over the 20-year period equates to -1.25%, quantifying to an annual decrease of -0.002 m/s/year (-0.06% pa). The largest seasonal decline of -0.006 m/s/year (-0.15% pa) was recorded in summer. Statistically significant declines in mean annual wind speed are somewhat more pronounced for the coastal zone (-0.003 m/s/year, -0.08% pa) than over interior regions (-0.002 m/s/year, -0.06% pa) for the study period. The largest decrease (-0.08% pa) was recorded for the coastal zone, followed by the inland zone (-0.06% pa), equating to an annual reduction in available energy of 0.18% pa and 0.09% pa, respectively. When considering all stations over the study period, the mean inter-annual variability is 3.11%. Despite such decreases in wind speed, the variance identified in this study would not have posed any risk to power generation from wind across the assessed stations, based on the period 1995 to 2014.

scholarly journals The super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality

2019 ◽  
Vol 4 (2) ◽  
pp. 343-353 ◽  
Author(s):  
Tyler C. McCandless ◽  
Sue Ellen Haupt
Keyword(s):  
Machine Learning ◽  
Standard Deviation ◽  
Wind Speed ◽  
Wind Power ◽  
Power Conversion ◽  
Jensen’S Inequality ◽  
Jensen's Inequality ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind

Abstract. Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for the plant is a function of the underlying meteorological phenomena being predicted; however, the wind speed for each turbine at the farm is also a function of the local terrain and the array orientation. Conversion algorithms that assume an average wind speed for the plant, i.e., the super-turbine power conversion, assume that the effects of the local terrain and array orientation are insignificant in producing variability in the wind speeds across the turbines at the farm. Here, we quantify the differences in converting wind speed to power at the turbine level compared with a super-turbine power conversion for a hypothetical wind farm of 100 2 MW turbines as well as from empirical data. The simulations with simulated turbines show a maximum difference of approximately 3 % at 11 m s−1 with a 1 m s−1 standard deviation of wind speeds and 8 % at 11 m s−1 with a 2 m s−1 standard deviation of wind speeds as a consequence of Jensen's inequality. The empirical analysis shows similar results with mean differences between converted wind speed to power and measured power of approximately 68 kW per 2 MW turbine. However, using a random forest machine learning method to convert to power reduces the error in the wind speed to power conversion when given the predictors that quantify the differences due to Jensen's inequality. These significant differences can lead to wind power forecasters overestimating the wind generation when utilizing a super-turbine power conversion for high wind speeds, and indicate that power conversion is more accurately done at the turbine level if no other compensatory mechanism is used to account for Jensen's inequality.

scholarly journals Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 738 ◽  
Author(s):  
Wenqing Xu ◽  
Like Ning ◽  
Yong Luo
Keyword(s):  
Wind Speed ◽  
Decision Tree ◽  
Wind Power ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Gradient Boosting ◽  
Post Processing ◽  
Near Surface ◽  
Wind Power Forecasting ◽  
Numerical Weather

With the large-scale development of wind energy, wind power forecasting plays a key role in power dispatching in the electric power grid, as well as in the operation and maintenance of wind farms. The most important technology for wind power forecasting is forecasting wind speed. The current mainstream methods for wind speed forecasting involve the combination of mesoscale numerical meteorological models with a post-processing system. Our work uses the WRF model to obtain the numerical weather forecast and the gradient boosting decision tree (GBDT) algorithm to improve the near-surface wind speed post-processing results of the numerical weather model. We calculate the feature importance of GBDT in order to find out which feature most affects the post-processing wind speed results. The results show that, after using about 300 features at different height and pressure layers, the GBDT algorithm can output more accurate wind speed forecasts than the original WRF results and other post-processing models like decision tree regression (DTR) and multi-layer perceptron regression (MLPR). Using GBDT, the root mean square error (RMSE) of wind speed can be reduced from 2.7–3.5 m/s in the original WRF result by 1–1.5 m/s, which is better than DTR and MLPR. While the index of agreement (IA) can be improved by 0.10–0.20, correlation coefficient be improved by 0.10–0.18, Nash–Sutcliffe efficiency coefficient (NSE) be improved by −0.06–0.6. It also can be found that the feature which most affects the GBDT results is the near-surface wind speed. Other variables, such as forecast month, forecast time, and temperature, also affect the GBDT results.

scholarly journals CYGNSS Ocean Surface Wind Validation in the Tropics

2020 ◽  
Author(s):  
Shakeel Asharaf ◽  
Duane E. Waliser ◽  
Derek J. Posselt ◽  
Christopher S. Ruf ◽  
Chidong Zhang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Satellite System ◽  
Cold Pool ◽  
Tropical Ocean ◽  
Near Surface ◽  
Wind Speeds ◽  
Low Wind Speeds ◽  
Global Navigation Satellite

AbstractSurface wind plays a crucial role in many local/regional weather and climate processes, especially through the exchanges of energy, mass and momentum across the Earth’s surface. However, there is a lack of consistent observations with continuous coverage over the global tropical ocean. To fill this gap, the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016, consisting of a constellation of eight small spacecrafts that remotely sense near surface wind speed over the tropical and sub-tropical oceans with relatively high sampling rates both temporally and spatially. This current study uses data obtained from the Tropical Moored Buoy Arrays to quantitatively characterize and validate the CYGNSS derived winds over the tropical Indian, Pacific, and Atlantic Oceans. The validation results show that the uncertainty in CYGNSS wind speed, as compared with these tropical buoy data, is less than 2 m s-1 root mean squared difference, meeting the NASA science mission Level-1 uncertainty requirement for wind speeds below 20 m s-1. The quality of the CYGNSS wind is further assessed under different precipitation conditions, and in convective cold-pool events, identified using buoy rain and temperature data. Results show that CYGNSS winds compare fairly well with buoy observations in the presence of rain, though at low wind speeds the presence of rain appears to cause a slight positive wind speed bias in the CYGNSS data. The comparison indicates the potential utility of the CYGNSS surface wind product, which in turn may help to unravel the complexities of air-sea interaction in regions that are relatively under-sampled by other observing platforms.

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 Homogenization and Trend Analysis of Canadian Near-Surface Wind Speeds

2010 ◽  
Vol 23 (5) ◽  
pp. 1209-1225 ◽  
Author(s):  
Hui Wan ◽  
Xiaolan L. Wang ◽  
Val R. Swail
Keyword(s):  
Wind Speed ◽  
Trend Analysis ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
The United States ◽  
Homogeneity Test ◽  
Near Surface ◽  
Wind Speeds ◽  
Long Term Trends

Abstract Near-surface wind speeds recorded at 117 stations in Canada for the period from 1953 to 2006 were analyzed in this study. First, metadata and a logarithmic wind profile were used to adjust hourly wind speeds measured at nonstandard anemometer heights to the standard 10-m level. Monthly mean near-surface wind speed series were then derived and subjected to a statistical homogeneity test, with homogeneous monthly mean geostrophic wind (geowind) speed series being used as reference series. Homogenized monthly mean near-surface wind speed series were obtained by adjusting all significant mean shifts, using the results of the statistical test and modeling along with all available metadata, and were used to assess the long-term trends. This study shows that station relocation and anemometer height change are the main causes for discontinuities in the near-surface wind speed series, followed by instrumentation problems or changes, and observing environment changes. It also shows that the effects of artificial mean shifts on the results of trend analysis are remarkable, and that the homogenized near-surface wind speed series show good spatial consistency of trends, which are in agreement with long-term trends estimated from independent datasets, such as surface winds in the United States and cyclone activity indices and ocean wave heights in the region. These indicate success in the homogenization of the wind data. During the period analyzed, the homogenized near-surface wind speed series show significant decreases throughout western Canada and most parts of southern Canada (except the Maritimes) in all seasons, with significant increases in the central Canadian Arctic in all seasons and in the Maritimes in spring and autumn.

scholarly journals Local-scale wind speed features captured by seasonal forecasts

2021 ◽  
Author(s):  
Jaume Ramon ◽  
Llorenç Lledó ◽  
Pierre-Antoine Bretonnière ◽  
Margarida Samsó ◽  
Francisco J. Doblas-Reyes
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Surface Wind ◽  
Climate Modelling ◽  
Seasonal Forecasts ◽  
Economic Sectors ◽  
Skill Levels ◽  
Near Surface ◽  
Wind Speeds ◽  
Surface Climate

<p>Thanks to the recent advances in climate modelling, seasonal predictions are becoming more skilful at anticipating the future state of near-surface climate variables over extratropics. Nevertheless, such predictions are delivered on too coarse grids with horizontal resolutions of hundreds of kilometres so that local events happening at much finer scales cannot be reproduced. This is particularly noted for variables with high spatial variability like wind or precipitation: wind speeds can vary substantially over a few kilometres, from the top of a mountain to a valley floor. The differences in magnitude might be relevant for the deriving sectoral indicators, for example, within the wind industry and at a wind farm level.</p><p>This work presents and applies a downscaling methodology to generate fine-scale seasonal forecasts ---up to station scale--- for near-surface wind speeds in Europe. The hybrid forecasts are based on a statistical downscaling with a perfect prognosis approach, fitting a multi-linear regression with the four main Euro-Atlantic Teleconnections (EATC) indices as predictors. Seasonal predictions of EATC indices, which are predictable with relatively good skill levels, are later inserted into the multi-linear model. This results in skilful seasonal predictions of surface wind speeds. Indeed, the comparison of the hybrid forecasts against the dynamical forecasts of wind speed shows that the skill of such forecasts is not only maintained but also increased over most of Europe. The hybrid forecasts are generated at 17 locations where tall tower wind speed data are available and at a pan-European scale using the 100-metre wind speeds from the ERA5 reanalysis. Improving the accuracy of seasonal predictions is an essential step to inform weather-and-climate-vulnerable socio-economic sectors of seasonal anomalies a few months ahead.</p>

scholarly journals The Super-Turbine Wind Power Conversion Paradox: Using Machine Learning to Reduce Errors Caused by Jensen’s Inequality

2019 ◽  
Author(s):  
Tyler C. McCandless ◽  
Sue Ellen Haupt
Keyword(s):  
Machine Learning ◽  
Standard Deviation ◽  
Wind Speed ◽  
Wind Power ◽  
Power Conversion ◽  
Jensen’S Inequality ◽  
Jensen's Inequality ◽  
Average Wind Speed ◽  
Wind Speeds ◽  
Average Wind

Abstract. Wind power is a variable generation resource and therefore requires accurate forecasts to enable integration into the electric grid. Generally, the wind speed is forecast for a wind plant and the forecasted wind speed is converted to power to provide an estimate of the expected generating capacity of the plant. The average wind speed forecast for the plant is a function of the underlying meteorological phenomena being predicted; however, the wind speed for each turbine at the farm is also a function of the local terrain and the array orientation. Conversion algorithms that assume an average wind speed for the plant, i.e., the super-turbine power conversion, assume that the effects of the local terrain and array orientation are insignificant in producing variability in the wind speeds across the turbines at the farm. Here, we quantify the differences in converting wind speed to power at the turbine level compared to a super-turbine power conversion for a hypothetical wind farm of 100 2-MW turbines as well as from empirical data. The simulations with simulated turbines show a maximum difference of approximately 3 % at 11 m s−1 with 1 m s−1 standard deviation of wind speeds and 8 % at 11 m s−1 with 2 m s−1 standard deviation of wind speeds as a consequence of Jensen’s Inequality. The empirical analysis shows similar results with mean differences between converted wind speed to power and measured power of approximately 68 kW per 2 MW turbine. However, using a random forest machine learning method to convert to power reduces the error in the wind speed to power conversion when given the predictors that quantify the differences due to Jensen’s Inequality. These significant differences can lead to wind power forecasters over-estimating the wind generation when utilizing a super-turbine power conversion for high wind speeds, and indicates that power conversion is more accurately done at the turbine level if no other compensatory mechanism is used to account for Jensen’s Inequality.

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