Performance evaluation of WRF/CALMET integrated model in expanding inflow wind field to air quality models

Document Type : Research Article


1 Faculty of Water and Environment, Shahid Beheshti University, Tehran, Iran

2 Department of Environmental Technologies, Environmental Sciences Research Institute, Shahid Beheshti University, Tehran, Iran


The necessity of having comprehensive information on wind field in a particular area is important for various reasons. How air pollutants are scattered and released is one of the most important ones. In this study we explore the efficiency and usefulness of integrating WRF mesoscale numerical model with CALMET meteorological calculation model in order to generate high accurate wind field in a region centered in Tehran in a five days period from 5th to 10th July 2014. On the first stage three dimensional wind fields of the region were processed using WRF model and the result made available to CALMET model as an initial guess. On the second stage the WRF model was put aside and surface meteorological data along with the atmosphere above the Mehrabad metrological station were processed using SMERGE and READ62 model respectively; the results used directly in CALMET model. Finally, the CALMET model was implemented using the combination of WRF model data and observational data. Then the efficiency of mentioned methods was explored using statistical analysis, comparing temperature profiles, wind speed and simulated data with observational data of Imam Khomeni airport metrological station in the intended period. Results of the statistical indexes which were used in this study including index of agreement (IOA), mean bias error (MBE), root mean-square error (RMSE) and mean absolute error (MAE) indicate the high power of WRF/CALMET integrated model in simulating wind field of the region so that the value of index of agreement for wind speed in the first stage is 0.70- 0.85, in the second stage is 0.59-0.83 and in the third stage is 0.76-0.90. Generally the results of this study shows that using and combining the outputs of WRF model with observational data as input for CALMET model is an efficient way for generating accurate metrological data for studying air quality modeling specially in countries like Iran in which the upper atmosphere data is hardly measured.


Main Subjects

[1]    W. Wang, W.J. Shaw, T.E. Seiple, J.P. Rishel, Y. Xie, An evaluation of a diagnostic wind model (CALMET), Journal of Applied Meteorology and Climatology, 47(6) (2008) 1739-1756.
[2]    Soltanzadeh, P. Zawar-Reza, A. Aliakbari-Bidokhti, A. Jalali, A. Torkzadeh, Study of local winds over Tehran using WRF in ideal conditions, Iranian Journal of Physics Research, 11(2) (2011) 199-213.
[3]    M. Milanese, L. Tornese, G. Colangelo, D. Laforgia, A. de Risi, Numerical method for wind energy analysis applied to Apulia Region, Italy, Energy, 128 (2017) 1-10.
[4]    W. Wang, W.J. Shaw, Evaluating wind fields from a diagnostic model over complex terrain in the Phoenix region and implications to dispersion calculations for regional emergency response, Meteorological applications, 16(4) (2009) 557-567.
[5]    S.H. Yim, J.C. Fung, A.K. Lau, Mesoscale simulation of year-to-year variation of wind power potential over southern China, Energies, 2(2) (2009) 340-361.
[6]    A.M. Omer, On the wind energy resources of Sudan, Renewable and Sustainable Energy Reviews, 12(8) (2008) 2117-2139.
[7]    N. Kumar, A.G. Russell, Comparing prognostic and diagnostic meteorological fields and their impacts on photochemical air quality modeling, Atmospheric Environment, 30(12) (1996) 1989-2010.
[8]    Chandrasekar, C.R. Philbrick, R. Clark, B. Doddridge, P. Georgopoulos, Evaluating the performance of a computationally efficient MM5/CALMET system for developing wind field inputs to air quality models, Atmospheric Environment, 37(23) (2003) 32673276.
[9]    U. EPA, User's guide for the AERMOD Meteorological Preprocessor (AERMET), Research Triangle Park, NC, Office of Air Quality Planning and Standards, (2004).
[10] J.S. Scire, F.R. Robe, M.E. Fernau, R.J. Yamartino, A user’s guide for the CALMET Meteorological Model, Earth Tech, USA, 37 (2000).
[11] N.L. Seaman, Meteorological modeling for airquality assessments, Atmospheric environment, 34(12-14) (2000) 2231-2259.
[12] M. Azadi, M. Soufiyani, G. Vakili, H. Ghaemi, A case study on the impact of synoptic and upper air data assimilation in WRF output for precipitation over Iran, Iranian Journal of Geophysics, 10(2) (2016) 110-119. (in persian).
[13] W. Wang, D. Barker, J. Bray, C. Bruyere, M. Duda, J. Dudhia, D. Gill, J. Michalakes, User’s Guide for Advanced Research WRF (ARW) Modeling System Version 3, Mesoscale and Microscale Meteorology Division–National Center for Atmospheric Research (MMM-NCAR),  (2007).
[14] G.A. Grell, J. Dudhia, D.R. Stauffer, A description of the fifth-generation Penn State/NCAR mesoscale model (MM5),  (1994).
[15] G. Doms, U. Schättler, A description of the nonhydrostatic regional model LM, Part I: Dynamics and Numerics, Deutscher Wetterdienst, Offenbach, (2002).
[16] D. Majewski, Hrm-user’s guide, Deutsche Wetter Dienst (DWD). Offenbach, Germany, 124 (2009).
[17] R.A. Pielke, W. Cotton, R.e.a. Walko, C.J. Tremback, W.A. Lyons, L. Grasso, M. Nicholls, M. Moran, D. Wesley, T. Lee, A comprehensive meteorological modeling system—RAMS, Meteorology and atmospheric Physics, 49(1-4) (1992) 69-91.
[18] S. Li, S. Xie, Spatial distribution and source analysis of SO2 concentration in Urumqi, International Journal of Hydrogen Energy, 41(35) (2016) 15899-15908.
[19] S.A. Abdul-Wahab, S.O. Fadlallah, A study of the effects of vehicle emissions on the atmosphere of Sultan Qaboos University in Oman, Atmospheric environment, 98 (2014) 158-167.
[20] Y. Rashidi, M. Rahimian, A. RashidiMehrabad, The analysis on distribution of NOX pollutant concentration from exhaust flues in Shahid Montazeri Power Plant at Isfahan using combined WRF-CALPUFF model, Amirkabir Journal of Civil Engineering, 51(2) (2019) 297-314. (in persian).
[21] Hernández, S. Saavedra, A. Rodríguez, J.A. Souto, J.J. Casares, Coupling WRF and CALMET models: Validation during primary pollutants glc episodes in an Atlantic coastal region, in:  Air Pollution Modeling and its Application XXII, Springer, 2014, pp. 681-684.
[22] L. Morales, F. Lang, C. Mattar, Mesoscale wind speed simulation using CALMET model and reanalysis information: An application to wind potential, Renewable Energy, 48 (2012) 57-71.
[23] S.H. Yim, J.C. Fung, A.K. Lau, S. Kot, Developing a high‐resolution wind map for a complex terrain with a coupled MM5/CALMET system, Journal of Geophysical Research: Atmospheres, 112(D5) (2007).
[24] F.L. Ludwig, D.K. Miller, S.G. Gallaher, Evaluating a Hybrid Prognostic–Diagnostic Model That Improves Wind Forecast Resolution in Complex Coastal Topography, Journal of Applied Meteorology and Climatology, 45(1) (2006) 155-177.
[25] J.G. Powers, J.B. Klemp, W.C. Skamarock, C.A. Davis, J. Dudhia, D.O. Gill, J.L. Coen, D.J. Gochis, R. Ahmadov, S.E. Peckham, G.A. Grell, J. Michalakes, S. Trahan, S.G. Benjamin, C.R. Alexander, G.J. Dimego, W. Wang, C.S. Schwartz, G.S. Romine, Z. Liu, C. Snyder, F. Chen, M.J. Barlage, W. Yu, M.G. Duda, The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions, Bulletin of the American Meteorological Society, 98(8) (2017) 17171737.
[26] M.-D. Chou, M.J. Suarez, X.-Z. Liang, M.M.-H. Yan, C. Cote, A thermal infrared radiation parameterization for atmospheric studies,  (2001).
[27] E.J. Mlawer, S.J. Taubman, P.D. Brown, M.J. Iacono, S.A. Clough, Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated‐k model for the longwave, Journal of Geophysical Research: Atmospheres, 102(D14) (1997) 16663-16682.
[28] Y.-L. Lin, R.D. Farley, H.D. Orville, Bulk parameterization of the snow field in a cloud model, Journal of Climate and Applied Meteorology, 22(6) (1983) 1065-1092.
[29] M. Tewari, F. Chen, W. Wang, J. Dudhia, M. LeMone, K. Mitchell, M. Ek, G. Gayno, J. Wegiel, R. Cuenca, Implementation and verification of the unified NOAH land surface model in the WRF model, in:  20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, 2004.
[30] Z.I. Janjić, The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes, Monthly Weather Review, 122(5) (1994) 927-945.
[31] A.D. Visscher, CALPUFF AND CALMET, in:  Air Dispersion Modeling, John Wiley & Sons, Inc, 2013, pp. 514-541.
[32] Y. Song, M. Zhang, X. Cai, PM10 modeling of Beijing in the winter, Atmospheric Environment, 40(22) (2006) 4126-4136.
[33] T.G. Farr, P.A. Rosen, E. Caro, R. Crippen, R. Duren, S. Hensley, M. Kobrick, M. Paller, E. Rodriguez, L. Roth, The shuttle radar topography mission, Reviews of geophysics, 45(2) (2007).
[34] W. Pfender, R. Graw, W. Bradley, M. Carney, L. Maxwell, Use of a complex air pollution model to estimate dispersal and deposition of grass stem rust urediniospores at landscape scale, Agricultural and forest meteorology, 139(1-2) (2006) 138-153.
[35] T. Tesche, D. McNally, C. Emery, E. Tai, Evaluation of the MM5 model over the Midwestern US for three 8-hour oxidant episodes, Prepared for the Kansas City Ozone Technical Workgroup, by Alpine Geophyisics, LLC, Ft. Wright, KY, and ENVIRON International Corp., Novato, CA,  (2001).
[36] C. Emery, E. Tai, G. Yarwood, Enhanced meteorological modeling and performance evaluation for two Texas ozone episodes, Prepared for the Texas Natural Resource Conservation Commission, by ENVIRON International Corporation,  (2001).
[37] J. Chang, S. Hanna, Air quality model performance evaluation, Meteorology and Atmospheric Physics, 87(1) (2004) 167-196.
[38] A.Q. Branch, Reassessment of the Interagency Workgroup on Air Quality Modeling (IWAQM) Phase 2 Summary Report: Revisions to Phase 2 Recommendations.