Sensitivity of WRF-simulated 10 m wind over the Persian Gulf to different boundary conditions and PBL parameterization schemes

Gholami, Siavash; Ghader, Sarmad; Khaleghi-Zavareh, Hasan; Ghafarian, Parvin

doi:10.1016/j.atmosres.2020.105147

Cited by 26 publications

(14 citation statements)

References 46 publications

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“…The MBE and RMSE values for wind direction were ranging from −31.22°to 6.45°and 47.52°to 69.02°respectively; the errors are higher than those reported by Carvalho et al and Gholami et al. 18,19,51 NCEP-CFSR data show the best results among all datasets for six sites of the present study based on MBE and RMSE. NCEP-NCAR, NCEP-DOE and NCEP-CFSR were projects of the same organization, yet the performance of NCEP-CFSR data have shown significant improvement due to higher temporal resolution and model with biases removed by variational bias technique and use of coupled atmosphere model.…”

Section: Discussioncontrasting

confidence: 62%

Evaluation of Reanalysis and Analysis Datasets against Measured Wind Data for Wind Resource Assessment

Tahir

Asim

Hayat

et al. 2022

Energy & Environment

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The evaluation of reanalysis and analysis data (estimated data) against in-situ measured data is essential to find uncertainties before its use for wind resource assessment. The performance evaluation of four different generations reanalysis datasets (NCEP-CFSR, NCEP-DOE, NCEP-NCAR and JRA-55) and two analysis datasets (NCEP-FNL and NCEP-GFS) was done against measured data for six sites using statistical analysis. A comparison of monthly mean time-series, Weibull probability distribution function and wind rose diagram of measured and estimated data was performed. The MBE and RMSE for wind speed range from −2.18 to 2.01 m/s and 1.34 to 3.00 m/s respectively; whereas MBE and RMSE for wind direction range from −34.34° to 13.90° and 40.58° to 71.28° respectively for six sites using all datasets. NCEP-CFSR data show promising results for most of the sites with the lowest errors and better correlation coefficients. NCEP-CFSR data being the new generation reanalysis having higher spatial resolution show better results compared to other reanalyses and analyses. The reanalysis and analysis wind data can be used as alternative to measured data to assess wind energy potential.

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Section: Discussioncontrasting

confidence: 62%

Evaluation of Reanalysis and Analysis Datasets against Measured Wind Data for Wind Resource Assessment

Tahir

Asim

Hayat

et al. 2022

Energy & Environment

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“…Cup anemometer data were used to compare with vertical wind speed and wind shear, it is predicated that different PBL schemes could be suitable for simulating wind fields under specific stable conditions [8]. Wind speeds above 10 m at ground level were employed to evaluate the sensitivity of the WRF model with various initial condition datasets, noting that nonlocal closure schemes such as YSU and ERA-Interim reanalysis data provide the best estimation of wind speed [9].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Major Impact of Planetary Boundary Layer Schemes on Simulation of Vertical Wind Structure

et al. 2021

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The structure and evolution of the atmospheric planetary boundary layer (PBL) plays an important role in the physical and chemical processes of cloud–radiation interaction, vertical mixing and pollutant transport in the atmosphere. The PBL parameterization scheme describes the vertical transport of atmospheric momentum, heat, water vapor and other physical quantities in the boundary layer. The accuracy of wind field simulation and prediction is one of the most significant parameters in the field of atmospheric science and wind energy. Limited by the observation data, there are few studies on wind energy development. A 3D Doppler wind LiDAR (DWL) providing the high-vertical-resolution wind data over the urban complex underlying surface in February 2018 was employed to systematically evaluate the accuracy of vertical wind field simulation for the first time. 11 PBL schemes of the Weather Research and Forecasting Model (WRF) were employed in simulation. The model results were evaluated in groups separated by weather (sunny days, hazy days and windy days), observation height layers of wind field, and various observation wind speeds. Among these factors, the simulation accuracy is most closely related to the observation height layers of wind field. The simulation is fairly accurate at a height of 1000–2000 m, as most of the relative mean biases for wind speed and wind direction are less than 20% and 6% respectively. Below 1000 m, the wind speed and direction biases are about 30–150% m·s−1 and 6–30%, respectively. Moreover, when the observed wind speed was lower than 5 m·s−1, the biases were usually large, and the wind speed relative mean bias reaches up to 50–300%. In addition, the accuracy of the simulated wind profile is better in the range of 10–15 m·s−1 than other speed ranges, and is better above 1000 m than below 1000 m in the boundary layer. We see that the WRF boundary layer schemes have different applicabilities to different weather conditions. The WRF boundary layer schemes have significant differences in wind field simulations, with larger error under the complex topographies. A PBL scheme is not likely to maintain its advantages in the long term under different conditions including altitude and weather conditions.

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“…In that study, the WRF model is configured with the Mellor-Yamada-Janjic (MYJ) scheme and overestimates wind speed by up to 100 %; however, the bias is significantly reduced when the non-local scheme developed at Yonsei University (YSU) is used instead. The YSU scheme also shows good model skill in simulating winds over the Iberian Peninsula, Persian Gulf, Tyrrhenian coast, and western Argentina (Jiménez and Dudhia, 2012;Puliafito et al, 2015;Falasca et al, 2021;Gholami et al, 2021). Other studies suggest that MYNN and ACM2 are more appropriate for wind simulations (Carvalho et al, 2014b;Chang et al, 2015;Prieto-Herráez et al, 2021;Rybchuk et al, 2021).…”

Section: Introductionmentioning

confidence: 82%

“…The impact of the planetary boundary layer (PBL) scheme on wind simulation has been studied for many years, as the PBL scheme plays a critical role in modulating mass, energy, and moisture fluxes between the land and atmosphere, which in turn influences the simulation of low-level temperatures, cloud formation, and wind fields (Jiménez and Dudhia, 2012;Gómez-Navarro et al, 2015;Gonçalves-Ageitos et al, 2015;Falasca et al, 2021;Gholami et al, 2021). Many studies indicate an overestimation of wind speed in WRF simulations with different PBL schemes (Jiménez and Dudhia, 2012;Carvalho et al, 2014a, b;Pan et al, 2021;Gholami et al, 2021;Dzebre and Adaramola, 2020). For example, Gómez-Navarro et al (2015) investigate the sensitivity of the WRF model to the PBL scheme by simulating wind storms over complex terrain at a horizontal grid spacing of 2 km.…”

Section: Introductionmentioning

confidence: 99%

Impact of physical parameterizations on wind simulation with WRF V3.9.1.1 under stable conditions at planetary boundary layer gray-zone resolution: a case study over the coastal regions of North China

Bai

Chen

et al. 2022

Geosci. Model Dev.

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Abstract. Reliable simulation of wind fields under stable weather conditions is vital for preventing air pollution. In this study, we investigate how different physical parameterizations impact simulated near-surface wind at 10 m height over the coastal regions of North China using the Weather Research and Forecasting (WRF) model with a horizontal grid spacing of 0.5 km. We performed 640 simulations using combinations of 10 planetary boundary layer (PBL), 16 microphysics (MP), and four shortwave–longwave radiation (SW–LW) schemes. Model performance is evaluated using measurements from 105 weather station observations. The results show that the WRF model can reproduce the temporal variation of wind speed in a reasonable way. The simulated wind speed is most sensitive to the PBL schemes, followed by SW–LW schemes and MP schemes. Among all PBL schemes, the MYJ scheme shows the best temporal correlation with the observed wind speed, while the Yonsei University (YSU) scheme has the lowest model bias. Dudhia–RRTM and MYDM7 show the best model performances out of all SW–LW and MP schemes, respectively, and the interactions among schemes also have large influences on wind simulation. Further investigation indicates that model sensitivity is also impacted by ocean proximity and elevation. For example, for coastal stations, MYNN shows the best correlation with observations among all PBL schemes, while Goddard shows the smallest bias of SW–LW schemes; these results are different from those of inland stations. In general, according to the bias metrics, WRF simulates wind speed less accurately for inland stations compared to coastal stations, and the model performance tends to degrade with increasing elevation. The WRF model shows worse performance in simulating wind direction under stable conditions over the study area, with lower correlation scores compared to wind speed. Our results indicate the role parameterizations play in wind simulation under stable weather conditions and provide a valuable reference for further research in the study area and nearby regions.

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Sensitivity of WRF-simulated 10 m wind over the Persian Gulf to different boundary conditions and PBL parameterization schemes

Cited by 26 publications

References 46 publications

Evaluation of Reanalysis and Analysis Datasets against Measured Wind Data for Wind Resource Assessment

Evaluation of Reanalysis and Analysis Datasets against Measured Wind Data for Wind Resource Assessment

Understanding the Major Impact of Planetary Boundary Layer Schemes on Simulation of Vertical Wind Structure

Impact of physical parameterizations on wind simulation with WRF V3.9.1.1 under stable conditions at planetary boundary layer gray-zone resolution: a case study over the coastal regions of North China

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