scholarly journals Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4169
Author(s):  
Giovanni Gualtieri
Keyword(s):  
Wind Speed ◽  
Prediction Models ◽  
Weather Prediction ◽  
Resource Assessment ◽  
Reanalysis Data ◽  
Solar Irradiation ◽  
Wind Resource Assessment ◽  
Regional Reanalysis ◽  
Wind Resource ◽  
Wind Resources

The reliability of ERA5 reanalyses for directly predicting wind resources and energy production has been assessed against observations from six tall towers installed over very heterogeneous sites around the world. Scores were acceptable at the FINO3 (Germany) offshore platform for both wind speed (bias within 1%, r = 0.95−0.96) and capacity factor (CF, at worst biased by 6.70%) and at the flat and sea-level site of Cabauw (Netherlands) for both wind speed (bias within 7%, r = 0.93−0.94) and CF (bias within 6.82%). Conversely, due to the ERA5 limited resolution (~31 km), large under-predictions were found at the Boulder (US) and Ghoroghchi (Iran) mountain sites, and large over-predictions were found at the Wallaby Creek (Australia) forested site. Therefore, using ERA5 in place of higher-resolution regional reanalysis products or numerical weather prediction models should be avoided when addressing sites with high variation of topography and, in particular, land use. ERA5 scores at the Humansdorp (South Africa) coastal location were generally acceptable, at least for wind speed (bias of 14%, r = 0.84) if not for CF (biased by 20.84%). However, due to the inherent sea–land discontinuity resulting in large differences in both surface roughness and solar irradiation (and thus stability conditions), a particular caution should be paid when applying ERA5 over coastal locations.

Wind Resource Assessment Using a Mesoscale Model: The Effect of Horizontal Resolution

2012 ◽  
Author(s):  
S. Jafari ◽  
T. Sommer ◽  
N. Chokani ◽  
R. S. Abhari
Keyword(s):  
Wind Energy ◽  
Complex Terrain ◽  
Prediction Models ◽  
Weather Prediction ◽  
Energy Resource ◽  
Resource Assessment ◽  
Land Area ◽  
Wind Resource Assessment ◽  
Wind Resource ◽  
Horizontal Grid

Prospecting for wind farm sites and pre-development studies of wind energy projects require knowledge of the wind energy resource over large areas (that is, areas of the order of 10’000 km2 and greater). One approach to detail this wind resource is the use of mesoscale numerical weather prediction models. In this paper, the mesoscale Weather Research and Forecasting (WRF) model is used to examine the effect of horizontal grid resolution on the fidelity of the predictions of the wind resource. The simulations are made for three test cases, Switzerland (land area 39’770 km2), Iowa (land area 145,743 km2) and Oregon (land area 248’647 km2), representing a range of terrain types, from complex terrain to flat terrain, over the period from 2006–2010. On the basis of comparisons to the data from meteorological masts and tall communication towers, guidelines are given for the horizontal grid required in the use of mesoscale models of large area wind resource assessment, especially over complex terrain.

Offshore wind resource assessment using reanalysis data

2022 ◽  
pp. 0309524X2110693
Author(s):  
Sajeer Ahmad ◽  
Muhammad Abdullah ◽  
Ammara Kanwal ◽  
Zia ul Rehman Tahir ◽  
Usama Bin Saeed ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Resource Assessment ◽  
Reanalysis Data ◽  
Measured Data ◽  
Offshore Wind ◽  
Technological Advancement ◽  
Wind Rose ◽  
Wind Resource Assessment ◽  
Wind Resource

The growth rate of offshore wind is increasing due to technological advancement and reduction in cost. An approach using mast measured data at coastline and reanalysis data is proposed for offshore wind resource assessment, especially for developing countries. The evaluation of fifth generation European Reanalysis (ERA5) data was performed against measured data using statistical analysis. ERA5 data slightly underestimates wind speed and wind direction with percentage bias of less than 1%. Wind resource assessment of region in Exclusive Economic Zone (EEZ) of Pakistan was performed in terms of wind speed and Wind Power Density (WPD). The range of monthly mean wind speed and WPD in the region was 4.03–8.67 m/second and 73–515 W/m2 respectively. Most-probable wind speed and dominating wind direction on corners and center of the region were found using probability distributions and wind rose diagrams respectively. Most-probable wind speed ranges 4.41–7.64 m/second and dominating wind direction is southwest.

On the wind resource in Algeria: Probability distributions evaluation

2020 ◽  
pp. 095765092097588
Author(s):  
Houdayfa Ounis ◽  
Nawel Aries
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Probability Distributions ◽  
Resource Assessment ◽  
Descriptive Statistics ◽  
The Other ◽  
Wind Speeds ◽  
Wind Resource Assessment ◽  
The Mean ◽  
Wind Resource

The present study aims to present a contribution to the wind resource assessment in Algeria using ERA-Interim reanalysis. Firstly, the ERA-Interim reanalysis 10 m wind speed data are considered for the elaboration of the mean annual 10 m wind speed map for a period starting from 01-01-2000 to 31-12-2017. Moreover, the present study intends to highlight the importance of the descriptive statistics other than the mean in wind resource assessment. On the other hand, this study aims also to select the proper probability distribution for the wind resource assessment in Algeria. Therefore, nine probability distributions were considered, namely: Weibull, Gamma, Inverse Gaussian, Log Normal, Gumbel, Generalized Extreme Value (GEV), Nakagami, Generalized Logistic and Pearson III. Furthermore, in combination with the distribution, three parameter estimation methods were considered, namely, Method of Moment, Maximum Likelihood Method and L-Moment Method. The study showed that Algeria has several wind behaviours due to the diversified topographic, geographic and climatic properties. Moreover, the annual mean 10 m wind speed map showed that the wind speed varies from 2.3 to 5.3 m/s, where 73% of the wind speeds are above 3 m/s. The map also showed that the Algerian Sahara is windiest region, while, the northern fringe envelopes the lowest wind speeds. In addition, it has been shown that the study of the mean wind speeds for the evaluation of the wind potential alone is not enough, and other descriptive statistics must be considered. On the other hand, among the nine considered distribution, it appears that the GEV is the most appropriate probability distribution. Whereas, the Weibull distribution showed its performance only in regions with high wind speeds, which, implies that this probability distribution should not be generalized in the study of the wind speed in Algeria.

Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory

2020 ◽  
Author(s):  
Julian Steinheuer ◽  
Petra Friederichs
Keyword(s):  
Wind Speed ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Value Theory ◽  
Extreme Value ◽  
Horizontal Wind ◽  
Wind Gust ◽  
Post Processing ◽  
Regional Reanalysis

<div>Wind and gust statistics at the hub height of a wind turbine are important parameters for the planning in the renewable energy sector. However, reanalyses based on numerical weather prediction models typically only give estimates for wind gusts at the standard measurement height of 10 m above the land surface. We present here a statistical post-processing that gives a conditional distribution for hourly peak wind speeds as a function of height. The conditioning variables are provided by the regional reanalysis COSMO-REA6. The post-processing is developed on the basis of observations of the peak wind speed in five vertical layers between 10 m and 250 m of the Hamburg Weather Mast. The statistical post-processing is based on a censored generalized extreme value (cGEV) distribution with non-stationary parameters. To select the most meaningful variables we use a least absolute shrinkage and selection operator. The vertical variation of the cGEV parameters is approximated using Legendre polynomials, allowing gust prediction at any desired height within the training range. Furthermore, the Pickands dependence function is used to investigate dependencies between gusts at different heights. The main predictors are the 10 m gust diagnosis, the barotropic and baroclinic modes of absolute horizontal wind speed, the mean absolute horizontal wind in 700 hPa, the surface pressure tendency and the lifted index. Proper scores show improvements of up to 60 %, especially at higher vertical levels when compared to climatology. The post-processing model with a Legendre approximation is able to provide reliable predictions of gust statistics at unobserved intermediate levels. The strength of the dependence between the gusts at different levels is not stationary and strongly modulated by the vertical stability of the atmosphere.</div>

Wind Resource Assessment for the Kingdom of Bahrain

2008 ◽  
Vol 32 (5) ◽  
pp. 439-448 ◽  
Author(s):  
Hanan Al Buflasa ◽  
David Infield ◽  
Simon Watson ◽  
Murray Thomson
Keyword(s):  
Wind Speed ◽  
Energy Production ◽  
Resource Assessment ◽  
Measured Data ◽  
Shape Parameters ◽  
Main Island ◽  
Wind Resource Assessment ◽  
Mean Wind Speed ◽  
Wind Resource ◽  
Hourly Basis

The geographical distribution of wind speed (the wind atlas) for the kingdom of Bahrain is presented, based on measured data and on calculations undertaken using WAsP,. The data used were recorded by the Meteorological Directorate at a weather station situated at Bahrain International Airport, taken on an hourly basis for a period of time extended for ten years. These data indicate an annual mean wind speed of 4.6 m/s at 10 m height and mean Weibull scale and shape parameters C and k of 5.2 m/s and 1.9 respectively. At a typical wind turbine hub height of sixty metres, these values are extrapolated to 6.9 m/s, 7.8 m/s and 1.8 respectively, which suggests that the area has a good wind resource. The wind atlas shows that several locations in the less populated central and southern regions of the main island of the archipelago of Bahrain are potentially suitable for wind energy production.

scholarly journals Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory

2019 ◽  
Author(s):  
Julian Steinheuer ◽  
Petra Friederichs
Keyword(s):  
Wind Speed ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Value Theory ◽  
Extreme Value ◽  
Horizontal Wind ◽  
Wind Gust ◽  
Post Processing ◽  
Regional Reanalysis

Abstract. Many applications require wind gust estimates at very different atmospheric height levels. For example, the renewable energy sector is interested in wind and gust predictions at the hub height of a wind power plant. However, numerical weather prediction models typically derive estimates for wind gusts at the standard measurement height of 10 m above the land surface only. Here, we present a statistical post-processing to derive a conditional distribution for hourly peak wind speed as a function of height. The conditioning variables are taken from the regional reanalysis COSMO-REA6. The post-processing is trained using peak wind speed observations at five vertical levels between 10 m and 250 m of the Hamburg Weather Mast. The statistical post-processing is based on a censored generalized extreme value (cGEV) distribution with non-stationary parameters. We use a least absolute shrinkage and selection operator to select the most informative variables. Vertical variations of the cGEV parameters are approximated using Legendre polynomials, such that predictions may be derived at any desired vertical height. Further, the Pickands dependence function is used to assess dependencies between gusts at different heights. The most important predictors are the 10 m gust diagnostic, the barotropic and the baroclinic mode of absolute horizontal wind speed, the mean absolute horizontal wind in 700 hPa, the surface pressure tendency, and the lifted index. Proper scores show improvements with respect to climatology of up to 60 % especially at higher vertical levels. The post-processing model with a Legendre approximation is able to provide reliable predictions of gusts statistics at non-observed intermediate levels. The strength of dependency between gusts at different levels is non-stationary and strongly modulated by the vertical stability of the atmosphere.

scholarly journals Wind Speed using Improved Mixture Weibull Distribution

2019 ◽  
Vol 8 (6) ◽  
pp. 1871-1874
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Weibull Distribution ◽  
Wind Farms ◽  
Resource Assessment ◽  
Estimation Of Parameters ◽  
Basic Properties ◽  
Wind Resource Assessment ◽  
Proposed Model ◽  
Wind Resource

Wind is random in nature both in space and in time. Several technologies are used in wind resource assessment (WRA).The appropriate probability distribution used to calculate the available wind speed at that particular location and the estimation of parameters is the essential part in installing wind farms. The improved mixture Weibull distribution is proposed model which is the mixture of two and three parameter Weibull distribution with parameters including scale, shape, location and weight component. The basic properties of the proposed model and estimation of parameters using various methods are discussed.

scholarly journals Vertical profiles of wind gust statistics from a regional reanalysis using multivariate extreme value theory

2020 ◽  
Vol 27 (2) ◽  
pp. 239-252
Author(s):  
Julian Steinheuer ◽  
Petra Friederichs
Keyword(s):  
Wind Speed ◽  
Prediction Models ◽  
Weather Prediction ◽  
Value Theory ◽  
Extreme Value ◽  
Processing Method ◽  
Horizontal Wind ◽  
Wind Gust ◽  
Post Processing ◽  
Regional Reanalysis

Abstract. Many applications require wind gust estimates at very different atmospheric height levels. For example, the renewable energy sector is interested in wind and gust predictions at the hub height of a wind power plant. However, numerical weather prediction models typically only derive estimates for wind gusts at the standard measurement height of 10 m above the land surface. Here, we present a statistical post-processing method to derive a conditional distribution for hourly peak wind speed as a function of height. The conditioning variables are taken from the COSMO-REA6 regional reanalysis. The post-processing method was trained using peak wind speed observations at five vertical levels between 10 and 250 m from the Hamburg Weather Mast. The statistical post-processing method is based on a censored generalized extreme value (cGEV) distribution with non-homogeneous parameters. We use a least absolute shrinkage and selection operator to select the most informative variables. Vertical variations of the cGEV parameters are approximated using Legendre polynomials, such that predictions may be derived at any desired vertical height. Further, the Pickands dependence function is used to assess dependencies between gusts at different heights. The most important predictors are the 10 m gust diagnostic, the barotropic and the baroclinic mode of absolute horizontal wind speed, the mean absolute horizontal wind at 700 hPa, the surface pressure tendency, and the lifted index. Proper scores show improvements of up to 60 % with respect to climatology, especially at higher vertical levels. The post-processing model with a Legendre approximation is able to provide reliable predictions of gusts' statistics at non-observed intermediate levels. The strength of dependency between gusts at different levels is non-homogeneous and strongly modulated by the vertical stability of the atmosphere.

Sign in / Sign up

Export Citation Format

Share Document