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<p>Wind energy has exceptional and untapped potential in South Asia, a region characterized by a diverse climate and geography. The novelty of this research lies in using systematic selection threshold criteria, ensuring a comprehensive representation of wind speed patterns to determine wind energy generation potential classification and standardize wind anomaly over diverse topographic and climatic conditions of South Asia. Further, using reanalysis products (ERA5, JRA55, NCEP/NCAR), a multi-reanalysis ensemble (MRE), and wind speed trends, this work systematically evaluates the reliability and accuracy of the performance of reanalysis products to perceive uncertainty in reanalysis products. For this determination, a 10&#xa0;m above ground level (AGL) of wind speed was utilized for 1973–2005. A high percentage of stations with missing wind speed values is identified in the study, emphasizing the importance of data quality. Overall, providing strong correlations, low bias, and close alignment with observed data, JRA55 is a reliable choice for wind energy assessments and climate studies. Also, the research reveals a downward trend in annual mean wind speed (−&#xa0;0.010&#xa0;ms<sup>−1</sup><InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="477_2025_2918_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="47" /> </InlineMediaObject> <EquationSource Format="TEX">\({\text{year}}^{-1}\)</EquationSource> </InlineEquation>) in South Asia, aligning with the global terrestrial stilling phenomenon, although significant localized variations exist. The findings based on the localized regions, such as the Hyderabad Airport station, offer Excellent prospects for developing wind energy yield when classified. The reasons for inconsistencies between reanalysis products and observations are examined. A robust systematic threshold criteria research method is delineated to enhance data quality, revealing insights into spatial coverage, station selection, and biases. Ultimately, the study contributed to South Asia's climate resilience, policymakers, regional climate dynamics, and sustainable development of renewable energy sources.</p>

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Assessing wind power generation potential over South Asia using wind speed observation and reanalysis datasets

  • 期刊论文

  • 发表日期：

    2025年2月8日

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  • Muhammad Abid Khan,
  • Koji Dairaku,
  • Saurabh Kelkar

摘要

Wind energy has exceptional and untapped potential in South Asia, a region characterized by a diverse climate and geography. The novelty of this research lies in using systematic selection threshold criteria, ensuring a comprehensive representation of wind speed patterns to determine wind energy generation potential classification and standardize wind anomaly over diverse topographic and climatic conditions of South Asia. Further, using reanalysis products (ERA5, JRA55, NCEP/NCAR), a multi-reanalysis ensemble (MRE), and wind speed trends, this work systematically evaluates the reliability and accuracy of the performance of reanalysis products to perceive uncertainty in reanalysis products. For this determination, a 10 m above ground level (AGL) of wind speed was utilized for 1973–2005. A high percentage of stations with missing wind speed values is identified in the study, emphasizing the importance of data quality. Overall, providing strong correlations, low bias, and close alignment with observed data, JRA55 is a reliable choice for wind energy assessments and climate studies. Also, the research reveals a downward trend in annual mean wind speed (− 0.010 ms−1 \({\text{year}}^{-1}\) ) in South Asia, aligning with the global terrestrial stilling phenomenon, although significant localized variations exist. The findings based on the localized regions, such as the Hyderabad Airport station, offer Excellent prospects for developing wind energy yield when classified. The reasons for inconsistencies between reanalysis products and observations are examined. A robust systematic threshold criteria research method is delineated to enhance data quality, revealing insights into spatial coverage, station selection, and biases. Ultimately, the study contributed to South Asia's climate resilience, policymakers, regional climate dynamics, and sustainable development of renewable energy sources.