Wind modelling, validation and sensitivity study using Weather Research and Forecasting model in complex terrain. (April 2017)

• Record Type: Journal Article
• Authors: Mughal, Muhammad Omer; Lynch, Mervyn; Yu, Frank; McGann, Brendan; Jeanneret, Francois; Sutton, John
• Is Part Of: Environmental modelling & software. Volume 90(2017)
• Page Start: 107
• Page End: 125
• Publication Date: 2017-04
• Subjects: Sensitivity study -- Wind forecasting -- Boundary layer -- Validation; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.01.009
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: A physically-based wind model is applied to determine wind speed and direction and to conduct a model sensitivity analysis. The focus is the East African site of the Lake Turkana Wind Farm, characterized by complex terrain and high diurnal variability that creates a nocturnal jet of typically 15 m/s. Observations from three tall meteorological masts are compared with Weather Research and Forecast (WRF) model outputs. WRF is configured with four domains nested down to 900 m spatial resolution. The model is tested with initialization fields from two different sources, optimised using different grid configurations and parameterization schemes. Comparing model and data from 3 tall masts A, B and C yields that the primary source of error in WRF model simulation in a complex terrain is due to incorrect specification of boundary fields used to initialize the model. RMSEs achieved in this research are ≤2 m/s representing good model performance (Emery et al., 2001). Highlights: Optimized WRF configuration investigation for a complex terrain. Simulations evaluated against situ measurements at the Lake Turkana wind farm. Improved wind speed and direction prediction using ECMWF/ERA initialization fields. 87% reduction in power prediction error. The study established WRF's short term forecasting accuracy in a data sparse region.