Importance of the Parameterization Schemes in the WRF Model for Wind Speed Forecasting: Case Study of Tepuxtepec, Michoacán

Authors

  • Itzagueri García-Rodríguez Universidad Michoacana de San Nicolás de Hidalgo
  • Alma Rosa Mendez-Gordillo Universidad Autónoma de Coahuila
  • Rafael Campos-Amezcua TecNM/Centro Nacional de Investigación y Desarrollo Tecnológico
  • Sixtos A. Arreola-Villa Universidad Autónoma de Coahuila
  • Erasmo Cadenas-Calderón Universidad Michoacana de San Nicolás de Hidalgo

DOI:

https://doi.org/10.13053/cys-29-3-5904

Keywords:

WRF model, parameterization schemes, wind speed forecasting

Abstract

The planning of wind energy dispatchconstantly faces the challenges of wind speed intermittency and variability. Therefore, it is crucialto have models that generate reliable forecasts tosupport the development of this renewable energysource. This study evaluates the performance of threeconfigurations of the Weather Research and Forecasting(WRF) model for hourly wind speed forecastingin Tepuxtepec, Michoacán, Mexico, with a 24-hourforecast horizon. Three parameterization schemeswere compared: WRF_WMK, WRF_TBG, andWRF_MYB. These schemes were selected based on thegeographic, climatic, and meteorological characteristicsof the region, as well as the need to assess theWRF model’s performance under different physicalconfigurations. Simulations were conducted for fourrepresentative dates—one per season—considering theannual temperature cycle that influences wind behavior.The simulations used MERRA-2 reanalysis data asinput and were evaluated against measurements fromNASA’s POWER project. The comparison betweensimulated and observed wind speeds was performedusing four error metrics: Root Mean Square Error(RMSE), Mean Absolute Error (MAE), Bias, and theCorrelation Coefficient (r). Additionally, PredictionIntervals (PIs) at 80% and 95% confidence levels werecalculated to assess the reliability of the forecasts.The results showed that the WRF_TBG configurationoutperformed the others, reducing RMSE by up to 60%compared to WRF_WMK. The forecasted values werewithin the 80% PI for up to 80% of the total values, andwithin the 95% PI for up to 100%. Seasonal evaluationrevealed that the model performed best in winter andworst in summer, likely due to the influence of intenseconvective processes during the latter season.

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Published

2025-09-26

Issue

Vol. 29 No. 3 (2025): Vol 29 No 3 (2025)

Section

Articles of the Thematic Section (2)

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