Evaluation of planetary boundary layer simulations for wind resource study in east of Iran

The usual way to collect wind data for wind resource assessment is installing wind masts, which might not be feasible due to the cost or time constraints. Conducting qualified long term numerical simulations is classified as a new method for this purpose. In this study, WRF model experiments are evaluated for simulating wind field over east of Iran. Planetary Boundary Layer (PBL) physical parameterization plays an important role in the structure of simulated low level wind field. Evaluation of PBL schemes over the study area could be an essential issue for reduction of simulated wind errors. The ACM2, MYJ, MYNN2.5, QNSE and YSU PBL schemes are evaluated during July and December 2007. The MYJ PBL scheme showed the best performance for Fadeshk area. We then carried out one-year simulations for the whole of 2007 and simulated wind field and wind energy productions compared to the measurement wind data. Wind distribution during 2007 was simulated well with this PBL scheme, although it showed overestimations over 3 a.m. – 8 p.m. of day and underestimation over 8 p.m. -3 a.m. Relative errors for shape parameter, scale parameter, mean of Weibull distribution and wind power are estimated equal to 13.2%, 5.54%, 4.85% and −0.47% respectively. Overall, model has good performance in simulation of wind energy parameters in this area.

[1]  Moncho Gómez-Gesteira,et al.  A sensitivity study of the WRF model in wind simulation for an area of high wind energy , 2012, Environ. Model. Softw..

[2]  G. Giebel,et al.  Wind-Climate Estimation Based on Mesoscale and Microscale Modeling: Statistical–Dynamical Downscaling for Wind Energy Applications , 2014 .

[3]  C. Draxl,et al.  Evaluating winds and vertical wind shear from Weather Research and Forecasting model forecasts using seven planetary boundary layer schemes , 2014 .

[4]  R. Vecchi,et al.  Characterisation of PM10 and PM2.5 particulate matter in the ambient air of Milan (Italy) , 2001 .

[5]  C. Balaji,et al.  Impact of physics parameterization and 3DVAR data assimilation on prediction of tropical cyclones in the Bay of Bengal region , 2015, Natural Hazards.

[6]  M. Nakanishi.  Large-Eddy Simulation Of Radiation Fog , 2000 .

[7]  Jordan G. Powers,et al.  A Description of the Advanced Research WRF Version 2 , 2005 .

[8]  Tanya L. Otte,et al.  The Impact of Nudging in the Meteorological Model for Retrospective Air Quality Simulations. Part I: Evaluation against National Observation Networks , 2008 .

[9]  Alexis K.H. Lau,et al.  Evaluation of nonlocal and local planetary boundary layer schemes in the WRF model , 2012 .

[10]  G. Mellor,et al.  A Hierarchy of Turbulence Closure Models for Planetary Boundary Layers. , 1974 .

[11]  Jimy Dudhia,et al.  Mesoscale modeling of offshore wind turbine wakes at the wind farm resolving scale: a composite‐based analysis with the Weather Research and Forecasting model over Horns Rev , 2015 .

[12]  Song‐You Hong,et al.  The WRF Single-Moment 6-Class Microphysics Scheme (WSM6) , 2006 .

[13]  H. Niino,et al.  An Improved Mellor–Yamada Level-3 Model with Condensation Physics: Its Design and Verification , 2004 .

[14]  J. Dudhia,et al.  A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes , 2006 .

[15]  John L. Schroeder,et al.  Evaluation of Wind Forecasts and Observation Impacts from Variational and Ensemble Data Assimilation for Wind Energy Applications , 2015 .

[16]  Husain Alsamamra,et al.  Modeling of wind energy in some areas of Palestine , 2012 .

[17]  Song-You Hong,et al.  Intercomparison of Planetary Boundary-Layer Parametrizations in the WRF Model for a Single Day from CASES-99 , 2011 .

[18]  E. Batchvarova,et al.  A Study on the Effect of Nudging on Long-Term Boundary Layer Profiles of Wind and Weibull Distribution Parameters in a Rural Coastal Area. , 2013 .

[19]  Figen Balo,et al.  Investigation of wind characteristics and assessment of wind-generation potentiality in Uludağ-Bursa, Turkey , 2009 .

[20]  J. Pleim.  A Combined Local and Nonlocal Closure Model for the Atmospheric Boundary Layer. Part I: Model Description and Testing , 2007 .

[21]  Zhiwei Han,et al.  Evaluation and intercomparison of meteorological predictions by five MM5-PBL parameterizations in combination with three land-surface models , 2008 .

[22]  R. Vautard,et al.  A numerical approach for planning offshore wind farms from regional to local scales over the Mediterranean , 2016 .

[23]  E. Akpinar,et al.  Determination of the wind energy potential for Maden-Elazig, Turkey , 2004 .

[24]  John S. Kain,et al.  Convective parameterization for mesoscale models : The Kain-Fritsch Scheme , 1993 .

[25]  Z. Janjic.  The Step-Mountain Eta Coordinate Model: Further Developments of the Convection, Viscous Sublayer, and Turbulence Closure Schemes , 1994 .

[26]  Xiao-Ming Hu,et al.  Evaluation of the updated YSU planetary boundary layer scheme within WRF for wind resource and air quality assessments , 2013 .

[27]  Ali Mostafaeipour,et al.  Feasibility study of wind energy potential in two provinces of Iran: North and South Khorasan , 2011 .

[28]  S. Parsa,et al.  Wind power statistics and an evaluation of wind energy density , 1995 .

[29]  G. Mellor,et al.  Development of a turbulence closure model for geophysical fluid problems , 1982 .

[30]  Jason C. Knievel,et al.  A Mesoscale Model-Based Climatography of Nocturnal Boundary-Layer Characteristics over the Complex Terrain of North-Western Utah , 2015, Boundary-Layer Meteorology.

[31]  A. Keyhani,et al.  An assessment of wind energy potential as a power generation source in the capital of Iran, Tehran , 2010 .

[32]  Joachim Reuder,et al.  Validation of boundary layer parameterization schemes in the weather research and forecasting model under the aspect of offshore wind energy applications— Part I: Average wind speed and wind shear , 2015 .

[33]  Scott A. Braun,et al.  Sensitivity of High-Resolution Simulations of Hurricane Bob (1991) to Planetary Boundary Layer Parameterizations , 2000 .

[34]  M. Montgomery,et al.  Sensitivity of tropical‐cyclone models to the surface drag coefficient in different boundary‐layer schemes , 2014 .

[35]  Semion Sukoriansky,et al.  A quasi-normal scale elimination model of turbulence and its application to stably stratified flows , 2006 .

[36]  Improving the Mapping and Prediction of Offshore Wind Resources (IMPOWR): Experimental Overview and First Results , 2016 .

[37]  M. Gómez-Gesteira,et al.  WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal , 2014 .

[38]  Moncho Gómez-Gesteira,et al.  Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula , 2014 .

[39]  H. Madsen,et al.  Influence of local wind speed and direction on wind power dynamics - Application to offshore very short-term forecasting , 2011 .

[40]  Comparing WRF PBL Schemes with Experimental Data over Northern Italy , 2014 .

[41]  J. Dudhia.  Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model , 1989 .

[42]  E. Mlawer,et al.  Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave , 1997 .

[43]  Jimy Dudhia,et al.  On the Ability of the WRF Model to Reproduce the Surface Wind Direction over Complex Terrain , 2013 .

[44]  H. Pan,et al.  Nonlocal Boundary Layer Vertical Diffusion in a Medium-Range Forecast Model , 1996 .

[45]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[46]  Ioannis Antoniou,et al.  The influence of the wind speed profile on wind turbine performance measurements , 2009 .