Sensitivity analysis of WRF configurations for accurately predicting
operational wind farm data
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.