Evaluation of Reanalysis and Analysis Datasets against Measured Wind Data for Wind Resource Assessment

Tahir, Zia ul Rehman; Asim, Muhammad; Hayat, Nasir; Farooq, Muhammad; Abdullah, Muhammad; Azhar, Muhammad

doi:10.1177/0958305x221084078

Cited by 12 publications

(10 citation statements)

References 50 publications

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“…Referring to previous assessment methods for global reanalysis datasets [26,27], ERA5 data [28] replaced its predecessor products. Compared to ERA-interim and observational data, ERA5 data improves spatiotemporal resolution and accuracy, which is reflected in the precipitation, wind speed, wave height and temperature in the troposphere.…”

Section: Datamentioning

confidence: 99%

Wind Energy Resources at Antarctic Stations Based on ERA5

Wang,

Wu,

et al. 2023

Atmosphere

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The harsh scientific research environment of Antarctic stations demands a reliable energy supply; however, traditional methods not only pose a challenge in supply but also harm the environment. Antarctic energy supply has become a new choice for energy development in Antarctica due to its abundant wind energy resources. Using ERA5 10 m wind field reanalysis data, we compared and analyzed the correlation (r) and Root Mean Square Error (RMSE) between some observation stations and reanalysis data, with correlations above 0.67 and root mean square error below 2.3. This indicates that the accuracy of the ERA5 data is suitable for resource assessment at stations in Antarctica. We assessed the wind energy potential of the Great Wall, Zhongshan, Kunlun and Taishan Stations. The results show that the annual distribution and long-term trend of wind energy at Taishan Station are the best, followed by the Great Wall, Zhongshan and Kunlun Stations. Taishan Station has stable wind direction and abundant wind energy, the average wind power density is 800 W/m2, with an annual growth trend of 2.02 W/m2·yr−1. The effective wind speed occurrence and energy level occurrence are generally above 90% and the coefficient of variation is generally below 0.8. The dominant direction of wind energy is northeast and the wind direction is stable, which is conducive to the development and utilization of wind energy.

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Section: Datamentioning

confidence: 99%

Wind Energy Resources at Antarctic Stations Based on ERA5

Wang,

Wu,

et al. 2023

Atmosphere

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“…The scenario evaluated will include estimations of changes in the climate of the Earth's system as a result of these variables. The current CFSR will be extended in the future as a real-time operating product from the research data archive (RDA) summary on CFSR (Parshotam 2019;Kanwal et al 2022;Zhou et al 2022). CFSR data are available at https://globalweather.tamu.edu/.…”

Section: Cfsr Satellite Rainfall Productsmentioning

confidence: 99%

Evaluation of multiple bias correction methods with different satellite rainfall products in the Main Beles Watershed, Upper Blue Nile (Abbay) Basin, Ethiopia

Nigussie

Tenfie

Zimale

et al. 2022

Journal of Water and Climate Change

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This study investigates the utility of satellite-based rainfall products and the performance of bias correction methods in one of the sub-basins of the Upper Blue Nile Basin (Main Beles basin). Four satellite rainfall products are used as Climate Prediction Center (CPC) MORPHing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42V7 (TMPA 3B42V7), and Climate Forecast System Reanalysis (CFSR). The performance of the satellite rainfall products (SRPs) was compared using three bias correction methods such as Delta, Empirical Quantile Mapping (EQM), and Quantile Mapping (QM) on five metrological stations. Six statistical performance measuring techniques were employed. The evaluation was carried out from the year 2003 to 2016 on daily and monthly time scales. The results depicted that SRPs and bias correction methods of CMORPH_QM (r = 0.538) and TMPA_3B42V7_EQM (r = 0.95) data showed good performance, while PERSIANN_EQM (r = 0.348) and PERSIANN_Delta (r = 0.83) performed worst at daily and monthly time scales, respectively. This study highlights the benefits of using SRPs and bias correction methods to enhance the distribution of local rainfall data, which is critical for water resource planning and other related sectors.

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“…The ERA5 at 31 km resolution and hourly outputs (Hersbach et al., 2020), its predecessor ERA‐Interim at 79 km and three‐hourly outputs (Dee et al., 2011), or the MERRA‐2 at about 50 km (Gelaro et al., 2017) are examples for global reanalyzes that are used in the context of renewable energy planning and potential assessments for different spatial domains (e.g., Caglayan et al., 2019; Jurasz et al., 2021; Ryberg et al., 2019; Zhang, Ren, et al., 2020). Many studies have addressed the suitability of these or similar reanalysis data sets for REP assessments, and depending on the study site and data set, even lower resolution data are considered as suitable inputs (Doddy Clarke et al., 2021; Kanwal et al., 2022); for certain studies the reanalyzes systematically underestimate wind speeds (Tahir et al., 2018). In general, higher temporal as well as spatial resolutions in model outputs lead to better agreement with observations (Davidson & Millstein, 2022; Samal, 2021).…”

Section: Introductionmentioning

confidence: 99%

Convection‐Permitting ICON‐LAM Simulations for Renewable Energy Potential Estimates Over Southern Africa

Chen,

Poll,

Hendricks Franssen

et al. 2024

JGR Atmospheres

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Renewable energy is recognized in Africa as a means for climate change mitigation, but also to provide access to electricity in sub‐Saharan Africa, where three‐quarters of the global population without electricity resides. Reliable and highly resolved renewable energy potential (REP) information is indispensable to support power plants expansion. Existing atmospheric data sets over Africa that are used for REP estimates are often characterized by data gaps, or coarse resolution. With the aim to overcome these challenges, the ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON‐NWP) model in its Limited Area Mode (ICON‐LAM) is implemented and run over southern Africa in a hindcast dynamical downscaling setup at a convection‐permitting 3.3 km horizontal resolution. The simulation time span covers contrasting solar and wind weather years from 2017 to 2019. To assess the suitability of the novel simulations for REP estimates, the simulated hourly 10 m wind speed (sfcWind) and hourly surface solar irradiance (rsds) are extensively evaluated against a large compilation of in situ observations, satellite, and composite data products. ICON‐LAM reproduces the spatial patterns, temporal evolution, the variability, and absolute values of sfcWind sufficiently well, albeit with a slight overestimation and a mean bias (mean error (ME)) of 1.12 m s−1 over land. Likewise the simulated rsds with an ME of 50 W m−2 well resembles the observations. This new ICON simulation data product will be the basis for ensuing REP estimates that will be compared with existing lower resolution data sets.

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Evaluation of Reanalysis and Analysis Datasets against Measured Wind Data for Wind Resource Assessment

Cited by 12 publications

References 50 publications

Wind Energy Resources at Antarctic Stations Based on ERA5

Wind Energy Resources at Antarctic Stations Based on ERA5

Evaluation of multiple bias correction methods with different satellite rainfall products in the Main Beles Watershed, Upper Blue Nile (Abbay) Basin, Ethiopia

Convection‐Permitting ICON‐LAM Simulations for Renewable Energy Potential Estimates Over Southern Africa

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