Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil

Gruber, K.; Klöckl, Claude; Regner, Peter; Baumgärtner, Johann; Schmidt, Johannes

doi:10.1016/j.energy.2019.116212

Cited by 48 publications

(32 citation statements)

References 24 publications

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“…This fits well to the results of our current study, where the results of different countries or regions vary in terms of whether the GWA improves the quality of wind power simulation time series or not. Another study which uses the GWA and MERRA-2 for wind power simulation in Brazil finds that bias correction in general improves results [66].…”

Section: Discussionmentioning

confidence: 99%

Towards global validation of wind power simulations: A multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the global wind atlas

Gruber

Regner

Wehrle

et al. 2022

Energy

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Section: Discussionmentioning

confidence: 99%

Towards global validation of wind power simulations: A multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the global wind atlas

Gruber

Regner

Wehrle

et al. 2022

Energy

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“…Although the lack of reliable measured data in many areas of the world (Staffell and Pfenninger, 2016; Harris et al ., 2020) is reflected in the limited amount of literature about wind speed, when compared with other climate variables (typically temperature and precipitation), several studies have analysed wind speed variability over different continents (Vautard et al ., 2010; Wan et al ., 2010; Kim and Paik, 2015; Hansen et al ., 2018; Gruber et al ., 2019; Tian et al ., 2019; Zeng et al ., 2019), and over Europe in particular (Azorin‐Molina et al ., 2014; Lorente‐Plazas et al ., 2015b), using different observational data sources and methodologies. In the classical European wind Atlas (Troen and Petersen, 1989), it is observed that wind regimes over Europe mainly depend on the different regional climates and sea‐land distributions.…”

Section: Introductionmentioning

confidence: 99%

Comparison of ERA5 surface wind speed climatologies over Europe with observations from the HadISD dataset

Molina

Gutiérrez

Sánchez

2021

Intl Journal of Climatology

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Understanding space-time features of wind speed is of high interest in meteorology and several applied sciences. Accurate wind speed measurements in combination with reliable gridded products, such as reanalyses, are needed to address the main characteristics of the wind field. Hourly 10 m wind speed from the European Centre for Medium-Range Weather Forecasts (ECMWF) latest reanalysis (ERA5) is compared with HadISD wind observations from 245 stations across Europe. Averaged ERA5 hourly data is able to reproduce the annual cycle of monthly wind speed in Europe. ERA5 presents slightly larger (shorter) monthly medians in winter (summer) than observations. ERA5 is compared against observations for each station using a frequency distribution-based score (score, from 0 to 1). Most of the stations exhibit hourly scores ranging from 0.8 to 0.9, indicating that ERA5 is able to reproduce the wind speed spectrum range, from light to strong relative frequencies, for any location over Europe. Ranges of mean values, variability, distribution function parameters and high or low wind thresholds frequencies are shown for this ensemble of European stations, allowing for an overall description of wind features. Generally, there is no clear relationship between scores and the variables analysed. The correlation and scores between ERA5 and HadISD is even further increased at longer time frequencies (6-24 hourly), together with centred rootmean-square error (RMSE) and standard deviation decreases. Hourly wind data from ERA5 reanalysis is, despite some shortcomings, valuable information to perform further detailed studies with a regular spatial and time wind distribution, from the climatological or renewable energy perspectives, for example.

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“…Additionally, meteorological towers are expensive and for an accurate WSA many towers would be required. For these reasons, limited measurements and expensive equipment, Numerical Weather Prediction (NWP) models and global re-analysis data are important alternatives (Al-Yahyai et al, 2010;Gruber et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Weather research & forecasting model and MERRA-2 data for wind energy evaluation at different altitudes in Bolivia

Mamani

Hendrick

2021

Wind Engineering

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Wind energy is one of the most promising alternatives for a clean and ecological electricity generation. However, the implementation of efficient wind farms requires accurate data and measurements. This work analyses the MERRA-2 satellite datasets to compare and complement it with WRF simulations in different regions and altitudes in Bolivia, such as the Altiplano, Amazon and Chaco. A 41 years of hourly wind speed from MERRA-2 was used to analyze wind averages and characteristics over the year. WRF simulations for representative months were used to analyze wind shear and wind flows along Bolivia. The main results are related to wind speed index in different sites which varied between 0.90 and 1.09 and the periods of high wind speeds that is May—October in the Altiplano, and June—December in the Amazon and Chaco. However, the main findings are the differences between MERRA-2 data and WRF simulations that is linked to the topography of the sites in study.

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Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil

Cited by 48 publications

References 24 publications

Towards global validation of wind power simulations: A multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the global wind atlas

Towards global validation of wind power simulations: A multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the global wind atlas

Comparison of ERA5 surface wind speed climatologies over Europe with observations from the HadISD dataset

Weather research & forecasting model and MERRA-2 data for wind energy evaluation at different altitudes in Bolivia

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