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A Chebyshev Polynomial‐Based Wind Speed Profile Characterization Framework: Applications in Mesoscale Model Evaluation.

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Title: A Chebyshev Polynomial‐Based Wind Speed Profile Characterization Framework: Applications in Mesoscale Model Evaluation.
Authors: Baki, Harish1 (AUTHOR) hbaki@albany.edu, Basu, Sukanta1,2 (AUTHOR)
Source: Wind Energy. Feb2026, Vol. 29 Issue 2, p1-22. 22p.
Subjects: Chebyshev polynomials, Wind speed, Weather, Shear (Mechanics), Numerical weather forecasting, LIDAR, Wind speed measurement, Atmospheric physics
Abstract: This study presents a polynomial‐based wind speed profile characterization framework that approximates full vertical profiles using five physically meaningful Chebyshev polynomial coefficients. The framework captures key morphological features of wind profiles, including mean wind speed, vertical shear, curvature, inflection‐related behavior, and higher order fluctuations. Its accuracy is demonstrated using well‐documented wind regimes such as well‐mixed, shear‐dominant, logarithmic‐shaped, and low‐level jet (LLJ) profiles, showing consistent performance across diverse atmospheric conditions. To evaluate its practical utility by moving beyond conventional error metrics, the framework was applied to compare simulated wind profiles from a mesoscale model‐generated dataset against lidar observations. Results show that the mesoscale model reliably captured mean and shear structures, especially at coastal sites, while underrepresenting curvature and higher order variations at inland locations. The framework also facilitated a spatial and temporal assessment of model behavior, distinguishing coastal from inland site characteristics and capturing diurnal and seasonal variability, including LLJs driven by sea‐breeze circulation. [ABSTRACT FROM AUTHOR]
Copyright of Wind Energy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Chebyshev+polynomials%22">Chebyshev polynomials</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+speed%22">Wind speed</searchLink><br /><searchLink fieldCode="DE" term="%22Weather%22">Weather</searchLink><br /><searchLink fieldCode="DE" term="%22Shear+%28Mechanics%29%22">Shear (Mechanics)</searchLink><br /><searchLink fieldCode="DE" term="%22Numerical+weather+forecasting%22">Numerical weather forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22LIDAR%22">LIDAR</searchLink><br /><searchLink fieldCode="DE" term="%22Wind+speed+measurement%22">Wind speed measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+physics%22">Atmospheric physics</searchLink>
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  Data: This study presents a polynomial‐based wind speed profile characterization framework that approximates full vertical profiles using five physically meaningful Chebyshev polynomial coefficients. The framework captures key morphological features of wind profiles, including mean wind speed, vertical shear, curvature, inflection‐related behavior, and higher order fluctuations. Its accuracy is demonstrated using well‐documented wind regimes such as well‐mixed, shear‐dominant, logarithmic‐shaped, and low‐level jet (LLJ) profiles, showing consistent performance across diverse atmospheric conditions. To evaluate its practical utility by moving beyond conventional error metrics, the framework was applied to compare simulated wind profiles from a mesoscale model‐generated dataset against lidar observations. Results show that the mesoscale model reliably captured mean and shear structures, especially at coastal sites, while underrepresenting curvature and higher order variations at inland locations. The framework also facilitated a spatial and temporal assessment of model behavior, distinguishing coastal from inland site characteristics and capturing diurnal and seasonal variability, including LLJs driven by sea‐breeze circulation. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Wind Energy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/we.70080
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      – Code: eng
        Text: English
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        PageCount: 22
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      – SubjectFull: Chebyshev polynomials
        Type: general
      – SubjectFull: Wind speed
        Type: general
      – SubjectFull: Weather
        Type: general
      – SubjectFull: Shear (Mechanics)
        Type: general
      – SubjectFull: Numerical weather forecasting
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      – SubjectFull: LIDAR
        Type: general
      – SubjectFull: Wind speed measurement
        Type: general
      – SubjectFull: Atmospheric physics
        Type: general
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      – TitleFull: A Chebyshev Polynomial‐Based Wind Speed Profile Characterization Framework: Applications in Mesoscale Model Evaluation.
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              M: 02
              Text: Feb2026
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              Y: 2026
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