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Sensitivity analysis of WRF configurations for accurately predicting operational wind farm data
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  • A.S. Hernández-Acosta,
  • E. Ramos,
  • J. Meza-Carreto,
  • Carlos Lopez-Villalobos
A.S. Hernández-Acosta
Universidad Nacional Autonoma de Mexico Instituto de Energias Renovables
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E. Ramos
Universidad Nacional Autonoma de Mexico Instituto de Energias Renovables
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J. Meza-Carreto
Universidad Nacional Autonoma de Mexico Biblioteca Conjunta de Ciencias de la Tierra
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Carlos Lopez-Villalobos
Universidad Nacional Autonoma de Mexico Instituto de Energias Renovables

Corresponding Author:[email protected]

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Abstract

A versatile and widely used tool in atmospheric research and operational forecasting is the Weather Research and Forecasting (WRF) model. The WRF model utilizes advanced algorithms and physics parameterizations to simulate complicated atmospheric phenomena. However, this model relies on a configuration that encompasses spin-up time, different Planetary Boundary Layer (PBL), and Land Surface Model (LSM) schemes to describe the physics near the surface. This work aims to optimize the configuration of the WRF model by evaluating the impact of the spin-up period, PBL, and LSM schemes to obtain the best representation of the wind field to calculate the power production of a wind farm located in the complex terrain of La Rumorosa, Baja California, Mexico. This site was chosen because of the availability of data, the challenging conditions of the terrain complexity, and severe changes in the wind direction. Results indicate that one day spin-up is the best option to predict the wind speed for one month or even one year. Moreover, the best statistical metrics are obtained when the Mellor-Yamada-Nakanishi–Niino Level 2.5 (MYNN2) PBL scheme is coupled with Noah-Multiphysics LSM. The best set-up for the WRF model was used to simulate the performance of a wind turbine in the farm with excellent results.
09 Aug 2024Submitted to Wind Energy
11 Aug 2024Submission Checks Completed
11 Aug 2024Assigned to Editor
11 Aug 2024Review(s) Completed, Editorial Evaluation Pending
03 Sep 2024Reviewer(s) Assigned
23 Sep 2024Editorial Decision: Revise Major
04 Nov 20241st Revision Received