Sensitivity Analysis of the WRF Model: Wind-Resource Assessment for Complex Terrain

Fernández‐González, Sergio; Martín, María Luisa Novo; García‐Ortega, Eduardo; Merino, Andrés; Lorenzana, Jesús; Sánchez, José Luis; Valero, Francisco; Rodrigo, Javier Sanz

doi:10.1175/jamc-d-17-0121.1

Cited by 74 publications

(58 citation statements)

References 56 publications

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“…For instance, the YSU scheme was observed to generally predict higher wind speeds than the MYNN3 scheme due the relatively shallower mixed layer that it simulates [2,3,21]. PBL schemes were found to have more significant impacts on error metrics of surface wind speed hindcasts than other parameterization schemes in studies in similarly coastal terrains [2,22]. Furthermore, the often relatively better ranking of the MYNN3 PBL scheme (over the other local closure schemes) is possible due to it being of a higher order closure.…”

Section: Discussionmentioning

confidence: 99%

“…However, studies in the tropics [17][18][19][20][21] have often not tested the different PBL schemes with different compatible SL schemes. In addition, the impact of the schemes on model performance is influenced by local terrain features and atmospheric conditions, which often vary with geographical location [2,3]. Against this background, in this paper, we investigate the impact of selected PBL schemes, paired with several compatible SL schemes, on wind hindcasts for wind energy assessment purposes in an area in coastal Ghana.…”

Section: Planetary Boundary and Surface Layer Parameterizationmentioning

confidence: 99%

“…Owing to diverse model options, identifying optimum model configurations (which are basically combinations of the options available in the models) for an application sometimes requires sensitivity tests, which assess, comparatively, the effects of varying model options on model performance. Predictions of surface winds by NWP models such as WRF are sensitive to model options such as simulation grid size, model physics, initial and boundary data, and parameterization of processes at the subgrid scale [2,3]. This paper focuses on selected parameterization options in the Advanced Research WRF (ARW).…”

Section: Introductionmentioning

confidence: 99%

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Impact of Selected Options in the Weather Research and Forecasting Model on Surface Wind Hindcasts in Coastal Ghana

Dzebre

Adaramola

2019

Energies

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This paper examines the impacts of five planetary boundary layer (PBL) parameterization schemes paired with several compatible surface layer (SL) parameterization schemes in the Weather Research and Forecasting Model on wind hindcasts for resource assessment purposes in a part of Coastal Ghana. Model predictions of hourly wind speeds at 3 × 3 km2 and 9 × 9 km2 grid boxes were compared with measurements at 40 m, 50 m, and 60 m. It was found that the Mellor-Yamada Nakanishi and Niino Level 3 (MYNN3) PBL scheme generally predicted winds with a relatively better combination of error metrics, irrespective of the SL scheme it was paired with. When paired with the Eta surface layer scheme, it often produced some of the relatively fewest errors in estimated mean wind power density (WPD) and Weibull cumulative density. A change in the simulation grid size did not have a significant impact on the conclusions of the relative performance of the PBL-SL pairs that were tested. The results indicate that the MYNN3 PBL and Eta SL pair is probably best for wind speed and energy assessments for this part of coastal Ghana.

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Section: Discussionmentioning

confidence: 99%

Section: Planetary Boundary and Surface Layer Parameterizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impact of Selected Options in the Weather Research and Forecasting Model on Surface Wind Hindcasts in Coastal Ghana

Dzebre

Adaramola

2019

Energies

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“…In fact, wind field simulation over complex terrain is an important topic for consideration in the atmospheric science field. Traditionally, mesoscale models, such as the Fifth-Generation Penn State/National Center for Atmospheric Research Mesoscale Model (MM5), Regional Atmospheric Modelling System (RAMS), and Weather Research and Forecasting (WRF), are important tools in predicting the wind field over complex terrain [1][2][3][4][5][6][7][8]. However, a terrain-following coordinate system and smoothing processes for terrain data are applied to deal with the complex terrain in most mesoscale…”

Section: Introductionmentioning

confidence: 99%

A Study on Microscale Wind Simulations with a Coupled WRF–CFD Model in the Chongli Mountain Region of Hebei Province, China

Sun

Zhang

et al. 2019

Atmosphere

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To ensure successful hosting of the 2022 Olympic Winter Games, a comprehensive understanding of the wind field characteristics in the Chongli Mountain region is essential. The purpose of this research was to accurately simulate the microscale wind in the Chongli Mountain region. Coupling the Weather Research and Forecasting (WRF) model with a computational fluid dynamics (CFD) model is a method for simulating the microscale wind field over complex terrain. The performance of the WRF-CFD model in the Chongli Mountain region was enhanced from two aspects. First, as WRF offers multiple physical schemes, a sensitivity analysis was performed to evaluate which scheme provided the best boundary condition for CFD. Second, to solve the problem of terrain differences between the WRF and CFD models, an improved method capable of coupling these two models is proposed. The results show that these improvements can enhance the performance of the WRF-CFD model and produce a more accurate microscale simulation of the wind field in the Chongli Mountain region.Atmosphere 2019, 10, 731 2 of 21 models [9][10][11]. Therefore, great terrain differences exist between reality and the mesoscale models, and nearly no mesoscale model can work over extremely steep terrain. Some scholars applied WRF in the large eddy simulation (LES) mode to increase model resolution [12][13][14], but the low vertical resolution and the need for a smoothing process for terrain data still exist. Moreover, the computational cost of a WRF-LES simulation is relative high. Fortunately, the computational fluid dynamics (CFD) model can partially compensate for the shortcomings of the mesoscale model. First, CFD can simulate the wind field with higher spatial resolutions (a few meters to tens of meters) than those of the mesoscale models [15][16][17][18]. Moreover, most CFD models are based on the finite volume method, which can improve their ability to depict realistic terrain [19]. However, CFD has its shortcoming in coping with the boundary conditions, and usually uses a simple wind profile as the boundary condition in some research. The mesoscale model can be initialized using global-scale data, such as the National Centers for Environmental Prediction (NCEP) reanalysis data. Therefore, coupling the mesoscale models with the CFD models is one way of simulating the microscale wind field over complex terrain. In this coupled system, the mesoscale and CFD models are combined in an off-line way, and the boundary condition that drives the CFD simulation is taken from the outputs of the mesoscale model [20]. First, the wind field with low spatial resolution is simulated by the WRF model. Second, the WRF wind data are imposed on the boundary of the CFD model. Finally, a wind field with higher spatial resolution is simulated by the CFD model. The advantages of this system is that the mesoscale model can provide more realistic boundary conditions and CFD can provide a wind field simulation with much higher spatial resolution.In recent years, a large amount of research on ...

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“…Fernández‐González et al . () concluded that the WRF model could simulate wind flow realistically in complex terrain when high‐resolution grid spacing was used. However, they emphasized the importance of conducting sensitivity analyses to find the best initial and boundary conditions and the best combination of physical parametrizations to produce the best simulations in different environments.…”

Section: Model Description and Experimental Setupmentioning

confidence: 99%

Analyses of the winter low‐level jet over the southern Red Sea using the Weather Research and Forecasting model

Samman

Gallus

2019

Quart J Royal Meteoro Soc

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The three-dimensional structure of the Red Sea Low-Level Jet (RSLLJ) that occurs over the southern Red Sea during the winter season is examined using the Weather Research and Forecasting (WRF) model. The causes of the jet formation are investigated, and the simulations confirm that the jet is a terrain-induced phenomenon. It initiates as a gap flow as the jet forms north of the strait of Bab el-Mandab as a result of a hydraulic effect when a stably stratified cool layer is channeled through the mountainous gap at low levels over the strait into the southern Red Sea. The jet intensity is enhanced by the Red Sea trough over the southern Red Sea. The cool dense air mass south of the strait is supported by a generally persistent synoptic regime that includes a high-pressure system over most of the eastern Arabian Peninsula and the Sudan low-pressure system over Sudan. The jet extends more than 500 km northward to slightly north of 17 • N from the strait around 12.5 • N, with a width of 100-150 km over the southern Red Sea. The jet core lies at 500-600 m above sea level between 14 • N and 16 • N, with wind speeds exceeding 25 m/s. The north-south pressure gradient along the strait, as well as the depth of the stratified cool layer south of the strait, are directly responsible for the jet length and speed over the southern Red Sea, with stronger pressure gradients and deeper cool layers resulting in longer and stronger jets. Sea/land breezes also directly influence the spatial scale, intensity, and diurnal variation of the jet, with land breezes causing faster and more constricted jets at night and divergence due to sea breezes leading to relatively slower jets during the daytime.

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Sensitivity Analysis of the WRF Model: Wind-Resource Assessment for Complex Terrain

Cited by 74 publications

References 56 publications

Impact of Selected Options in the Weather Research and Forecasting Model on Surface Wind Hindcasts in Coastal Ghana

Impact of Selected Options in the Weather Research and Forecasting Model on Surface Wind Hindcasts in Coastal Ghana

A Study on Microscale Wind Simulations with a Coupled WRF–CFD Model in the Chongli Mountain Region of Hebei Province, China

Analyses of the winter low‐level jet over the southern Red Sea using the Weather Research and Forecasting model

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