حساسیت سنجی مدل WRF در شبیه‌سازی باد سطحی شهر تهران نسبت به طرح‌واره فیزیکی و شرایط مرزی

نوع مقاله : مقاله کامل

نویسندگان

1 گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران

2 گروه جغرافیای طبیعی، دانشکده جغرافیا، دانشگاه تهران,، تهران، ایران

چکیده

سنجش نقش باد در کیفیت اقلیم و تهویه طبیعی هوای فضاهای شهری با روش‌های مختلفی مورد توجه پژوهشگران است. اما شبیه‌سازی باد با مدل‌های عددی آب‌وهوا مانند مدل تحقیقات و پیش‌بینی وضع هوا‌(WRF) همواره با عدم قطعیت‌هایی همراه است. در این پژوهش نقش داده‌های شرایط مرزی (اولیه)ECMWF-ERA5 و NCEP-FNL همراه با هفت پیکربندی فیزیکی متفاوت بر الگوی وزش باد مورد سنجش قرار گرفته است. هدف از تحقیق ارزیابی برونداد مدل WRF در شبیه‌سازی جهت و سرعت باد سطحی و همچنین تعیین اثر انواع طرحواره‌های فیزیکی بر بهبود نتایج ارزیابی است. نتایج به‌دست‌آمده نشان می‌دهد که جهت باد شبیه‌سازی ‌شده با مدل WRF با اختلاف قابل‌توجهی از داده‌های مشاهداتی همراه است، اما این اختلاف برای سرعت باد انحراف کمتری دارد. بر همین اساس برای متغیر سرعت باد به ترتیب پیکربندی‌های Exp(2,6,1) و برای متغیر جهت باد پیکربندی‌های Exp(3,7)، دارای نزدیک‌ترین شبیه‌سازی به مشاهدات هستند و به‌عنوان پیکربندی‌های برتر انتخاب گردیدند. خروجی‌های مدل نشان داد که داده‌های اولیه شرایط مرزی همانند طرحواره‌های فیزیکی اثر قابل‌توجهی در شبیه‌سازی جهت و سرعت باد دارند؛ به‌طوریکه در شهر تهران، عموماً شرایط مرزی‌ERA5 برای شبیه‌سازی جهت باد به ‌استثنای ماه ژانویه و شرایط مرزی‌FNL برای سرعت باد به غیر از جولای، گویای عملکرد بهتری هستند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Sensitivity of WRF model in simulation of surface wind in Tehran to physical schemas and boundary conditions

نویسندگان [English]

  • Naser Izadi 1
  • Aliakbar Shamsipour 2
  • Ghasem Azizi 1
1 Physical Geography Department, Faculty of Geography, University of Tehran
2 Physical geography department, Geography faculty, Tehran university, Tehran, Iran
چکیده [English]

Extended Abstract
Introduction
The wind has always been considered an energy source from two perspectives: pattern and behavior in urban contexts and potential in suburban environments. There are usually two major strategies for this purpose: one based on observational data and the other providing simulation data with the creation of climate models at various numerical scales (Han et al., 2014: 17). Numerical models are used in most studies to evaluate regional winds nowadays (Haman et al., 2010: 954; Shimada et al., 2011: 21). Simulated weather research and forecasting (WRF) has been used to conduct studies on this topic (Liu et al., 582: 2018; Salvaso et al., 276: 2018; Matar et al., 22: 2016; Charabi et al., 1: 2019; Tokhtenhagen et al., 119: 2020). The sensitivity and performance of the WRF model to initial and boundary conditions, as well as its impact on wind simulation, are investigated in this study. A planetary boundary layer scheme is also chosen to simulate the wind field in the city of Tehran.
 
Materials and methods
The Meteorological Organization provided observational data on wind direction and speed for Mehrabad, Chitgar, Geophysical, and North Tehran (Shemiran) synoptic stations from 2018 on a three-hour time scale (Table 2). Data analysis time series from two databases, the National Environmental Forecasting Center (NCEP-FNL) and the European Center for Medium-Term Weather Forecasting (ECMWF-) ERA5), were used as the initial and boundary conditions to achieve the frequency and distribution of wind direction and velocity for January, May, July, and October. The WRF model, version 4.1.2, was used to simulate the components of wind speed and direction using boundary condition data in this investigation. The RRTM longwave radiation model, the Goddard shortwave radiation design, the Noah surface model, the WSM6 microphysical schema, the two-dimensional Cumulus Betts-Miller-Janjic schema, and the three-dimensional Grell-Freitas schema were all employed in the study. The MRF Medium-Range Prediction Model, the Younesi University YSU Scheme, the MYJ Scheme, the second ACM2 Asymmetric Convection Scheme, the QNSE Normal Gaussian Scale, and the second and third MYNN Turbulence Scale are all used to test the performance sensitivity of the planetary boundary layer schemas.
 
Result and discussion
By checking the characteristics of the observation stations according to table 9, all the selected stations have an average height difference of at least 110 meters, and the difference between the lowest (Mehrabad) and the highest (Shimiran) station is 360 meters. According to the results from the selected stations, this feature can be effective in the accuracy of the simulations by the weather prediction research model. It can be stated that the model cannot correctly simulate the topography due to the low horizontal resolution in the inner domain (7 km) and static data (such as DEM and land cover (by default, these data in the model have a horizontal resolution of approximately 1 km)) to do Therefore, it is not possible to establish a meaningful relationship between the height difference of the stations and the output of the model. Still, the lack of proper introduction of the elevations of the land to the model causes the performance of the model to be weak so that it can simulate the surface currents resulting from local factors correctly.
 
Conclusion
According to the analyzes done with wind and statistics, it seems that the weather research and forecasting model is more weak in estimating the wind direction in the months when the average monthly wind speed is lower, and it can be said that in the months of July and October, the wind is generally controlled by local factors with Low speed is formed, on the other hand, due to static data with low spatial resolution, the morphology and morphology of the model is weak and due to the dependence of surface currents on topography, it causes a large error in the estimation of the wind direction by the model in the mentioned months, but this weakness in The cold months decrease with the passage of dynamic systems and the increase of the monthly average wind speed, but contrary to the wind direction, the wind speed estimation outputs by the model show that the increase of the monthly average wind speed causes a decrease in the accuracy of the model in the estimation of the wind speed variable, that is why in all the statistics, July has the best simulation in wind speed variable.
 From the results of these studies, the selected configuration for the direction may not necessarily be associated with the desired results for the speed. It may even be possible to achieve the best output in the months of the year with different configurations. According to the selected boundary configurations and data, the results of this study seem to be consistent with the research of Santos et al. (2013), Gholami et al., Ghafarian et al. (2018), and Laighi et al. (2015) are confirmed.

کلیدواژه‌ها [English]

  • Initial conditions
  • Wind direction and speed
  • Simulation
  • WRF
  • Tehran
  1. غلامی، سیاوش؛ قادر، سرمد؛ خالقی زواره، حسن و غفاریان، پروین. (1398). حساسیت‌سنجی میدان باد سطحی شبیه‌سازی‌شده ‌ توسط مدل WRF به شرایط اولیه و طرح‌واره‌های پارامترسازی لایه‌مرزی سیاره‌ای (مطالعه موردی: منطقه خلیج‌فارس).مجله ژئوفیزیک ایران، 13(1)،14-31.
  2. غفاریان، پروین؛ پگاه فر، نفیسه و محمدپور پنچاه، محمدرضا. (1398). شبیه‌سازی میدان باد سطحی در منطقه دریای عمان با مدل WRF با شرایط مرزی متفاوت.فیزیک زمین و فضا، 45(1)، 197-209.
  3. لایقی، بهزاد؛ سرمد، قادر؛ علی‌اکبری بیدختی، عباسعلی و آزادی، مجید. (1395). حساسیت سنجی شبیه‌سازی‌های مدل WRF به پارامترسازی های فیزیکی در محدوده خلیج‌فارس و دریای عمان در زمان مونسون تابستانی. مجله ژئوفیزیک ایران، 11(1)، 19-1.

 

  1. Awan, N.K., Truhetz, H., & Gobiet, A. (2011). Parameterization-induced error characteristics of MM5 and WRF operated in climate mode over the alpine region: an ensemble-based analysis. Clim. 24 (12), 3107–3123.
  2. Balzarini, A., Angelini, F., Ferrero, L., Moscatelli, M., Perrone, M. G., Pirovano, G., & Bolzacchini, E. (2014). Sensitivity analysis of PBL schemes by comparing WRF model and experimental data. Geoscientific Model Development Discussions, 7(5), 6133-6171.
  3. Bernier, N. B., & S. Belair, (2011). High horizontal and vertical resolution limited-area model: Near-surface and wind energy forecast applications, J. Appl. Meteor. Climatol, 51, 1061-1078.
  4. Carvalho, D., Rocha, A., Gomez-Gesteira, ´ M., & Silva Santos, C., (2014). WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal. Energy 117, 116–126.
  5. Charabi, Y., Al Hinai, A., Al-Yahyai, S., Al Awadhi, T., & Choudri, BS. (2019). Offshore wind potential and wind atlas over the Oman Maritime Zone. Energy, Ecology and Environment, 4, 1-14.
  6. Chauhan, H. M., Pomal, M. M., & Bhuta, N. (2013), A comparative study of wind forces on high-rise buildings as per is 875-Iii (1987) and proposed draft code (2011). Global journal for research analysis, 2 (5), 2277- 8160.
  7. Chou M.-D., & Suarez, M. J. (1994). An efficient thermal infrared radiation parameterization for use in general circulation models. NASA Tech. Memo. 104606(3), 85pp.
  8. Dee, D.P., Uppala, S.M, Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A.C.M., van den Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A.J., Haimberger, L., Healy, S.B., Hersbach, H., Holm, ´ E.V., Isaksen, L., Kållberg, P., Kohler, ¨ M., Matricardi, M., McNally, A.P., Monge-Sanz, B.M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Th´epaut, J.-N., & Vitart, F., (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. J. R. Meteorol. Soc. 137 (656), 553–597.
  9. Ghafarian, P., Pegahfar, N., & Mohammadpour Penchah, M. (2019). Simulation of the surface wind field by the WRF model in Oman Sea region with different initial and boundary conditions. Journal of the Earth and Space Physics, 45(1), 197-209. [In Persian].
  10. Gholami, S., Ghader, S., Khaleghi Zavareh, H., & Ghafarian, P. (2019). Sensitivity of the WRF model surface wind simulations to initial conditions and planetary boundary layer parameterization schemes (case study: over Persian Gulf). Iranian Journal of Geophysics, 13(1), 14-31. [In Persian].
  11. Gholami, S., Ghader, S., Khaleghi-Zavareh, H., & Ghafarian, P. (2021). Sensitivity of WRF-simulated 10 m wind over the Persian Gulf to different boundary conditions and PBL parameterization schemes. Atmospheric Research, 247, 105147.
  12. Grell, G. A., & Freitas, S. R., (2014). A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Phys., 14, 5233-5250.
  13. Hahmann, A., D. Rostkier-Edelstein, F. Vandenberghe, Y. Liu, Swerdlin, T. Warner, and R. Babarsky, 2010: A reanalysis system for the generation of mesoscale climatographies. J. Appl. Meteor. Climatol, 49, 954–972.
  14. Han, J. Y., Baik, J. J., & Lee, H. (2014). Urban impacts on precipitation. Asia-Pacific Journal of Atmospheric Sciences, 50(1), 17-30.
  15. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horanyi, ´ A., Munoz-Sabater, ˜ J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R., Holm, ´ E., Janiskova, ´ M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., & Th´epaut, J.-N., (2020). The ERA5 global reanalysis. J. R. Meteorol. Soc, 146 (730), 1999–2049.
  16. Hong, S.–Y., & H.–L. Pan, (1996). Nonlocal boundary layer vertical diffusion in a medium-range forecast model. Mon. Wea. Rev, 124, 2322–2339.
  17. Hong, S.–Y., & J.–O. J. Lim, (2006). The WRF single–moment 6–class microphysics scheme (WSM6).  Korean Meteor. Soc., 42, 129–151.
  18. Hong, Song–You., Yign, N., Jimy, D., (2006). A new vertical diffusion package with an explicit treatment of entrainment processes.  Wea. Rev., 134, 2318–2341.
  19. Janjic, Zavisa I., (1994). The Step–Mountain Eta Coordinate Model: Further developments of the convection, viscous sublayer, and turbulence closure schemes.  Wea. Rev., 122, 927–945.
  20. Layeghi, B., Ghader, S., Ali Akbari Bidokhti, A. A., & Azadi, M. (2017). Sensitivity of WRF model simulations to physical parameterization over the Persian Gulf and Oman Sea during summer monsoon. Iranian Journal of Geophysics, 11(1), 1-19. [In Persian].
  21. Li, Ji-Hang., Guo, Zhen-Hai.,. & Wang, Hui-Jun. (2014) Analysis of Wind Power Assessment Based on the WRF Model. Atmospheric and Oceanic Science Letters, 7(2), 126-131.
  22. Liu Y, Chen D, Li S, Chan PW. Discerning the spatial variations in offshore wind resources along the coast of China via dynamic downscaling. Energy, 160, 582-596.
  23. Mattar, C., Borvaran, D. (2016). Offshore wind power simulation by using WRF in the central coast of Chile. Renewable Energy, 94, 22-31.
  24. Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A. (1997). Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated‐k model for the longwave. Journal of Geophysical Research: Atmospheres, 102(D14), 16663-16682.
  25. Mughal, M.O., Lynch, M., Yu, F., McGann, B., Jeanneret, F., & Sutton, J., (2017). Wind modeling, validatio, and sensitivity study using Weather Research and Forecasting model in complex terrain. Model. Software 90, 107–125.
  26. Nakanishi, M., & Niino, H. (2006). An improved Mellor–Yamada level 3 model: its numerical stability and application to a regional prediction of advecting fog.  Layer Meteor. 119, 397–407. 
  27. Pleim, Jonathan E. (2007). A Combined Local and Nonlocal Closure Model for the Atmospheric Boundary Layer. Part I: Model Description and Testing.  Appl. Meteor. Climatol, 46, 1383–1395. 
  28. Salvação, N., & Soares, CG. (2018). Wind resource asse3ssment offshore the Atlantic Iberian coast with the WRF model. Energy, 145, 276-287.
  29. Santos-Alamillos, F. J., Pozo-Vázquez, D., Ruiz-Arias, J. A., Lara-Fanego, V., & Tovar-Pescador, J. (2013). Analysis of WRF model wind estimate sensitivity to physics parameterization choice and terrain representation in Andalusia (Southern Spain). Journal of Applied Meteorology and Climatology, 52(7), 1592-1609.
  30. Shimada, S., & Ohsawa, T. (2011). Accuracy and characteristics of offshore wind speeds simulated by WRF. SOLA, 7, 21–24.
  31. Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang, W., & Powers, J. G. (2008). A description of the Advanced Research WRF version 3. NCAR Technical note-475+ STR.
  32. Sukoriansky, S., B. Galperin, & Perov, V. (2005). Application of a new spectral model of stratified turbulence to the atmospheric boundary layer over sea ice.  –Layer Meteor., 117, 231–257. 
  33. Tewari, M., F. Chen, W. Wang, J. Dudhia, M. A. LeMone, K. Mitchell, M. Ek, G. Gayno, J. Wegiel, & Cuenca, R. H. (2004). Implementation and verification of the unified NOAH land surface model in the WRF model. 20th conference on weather analysis and forecasting/16th conference on numerical weather prediction, pp. 11–15.
  34. Tuchtenhagen, P., De Carvalho, G. G., Martins, G., Da Silva, P. E., De Oliveira, C. P., Andrade, L. D. M. B., & e Silva, C. M. S. (2020). WRF model assessment for wind intensity and power density simulation in the southern coast of Brazil. Energy, 190, 116341.
  35. Zhang, L., Xin, J., Yin, Y., Chang, W., Xue, M., Jia, D., & Ma, Y. (2021). A Major Impact of WRF Planetary Boundary Layer Schemes on Simulation Accuracy of Vertical Wind Structure by 3D Doppler Wind Lidar.
دوره 54، شماره 3
این شماره با همکاری و مشارکت «انجمن ایرانی ژئومورفولوژی» منتشر شده است، بدینوسیله از مشارکت این انجمن در «داوری مقالات» ، «معرفی داوران» و «دبیران تخصصی » و «شرکت در جلسات و نشست های مرتبط» تشکر می گردد.
آبان 1401
صفحه 293-312
  • تاریخ دریافت: 05 تیر 1400
  • تاریخ بازنگری: 10 شهریور 1400
  • تاریخ پذیرش: 05 آبان 1401