تحقیقات کاربردی علوم جغرافیایی

تحقیقات کاربردی علوم جغرافیایی

حساسیت سنجی پارامترسازی‌های فیزیکی مدل WRF در شبیه سازی اقلیم شهری و کاهش جزیره گرمایی در شرایط پایدار جوی (مطالعه موردی: استان‌های تهران و البرز)

نویسندگان
1 دانشجوی دکتری اقلیم شناسی شهری، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران
2 دانشیار اقلیم شناسی، دانشکده جغرافیا و علوم محیطی، دانشگاه حکیم سبزواری، سبزوار، ایران
3 استاد گروه مهندسی عمران و محیط زیست، دانشگاه کانکوردیا،کبک، کانادا
4 استاد اقلیم شناسی، دانشکده علوم جغرافیایی، دانشگاه خوارزمی، تهران، ایران
چکیده
در پژوهش حاضر حساسیت سنجی عناصر هواشناسی(نظیر میانگین دما، رطوبت نسبی و سرعت باد) به پارامترسازی­های فیزیکی مختلف در مدل پیش بینی عددی وضع هوا (WRF) جهت شبیه سازی اقلیم شهر و تعدیل جزیره گرمایی شهری منطقه مورد مطالعه، مورد ارزیابی قرار گرفت. جهت مطالعه مسائل زیست محیطی شهری از مدل تاج پوش شهری(UCM) به صورت جفت­شده با مدل WRF استفاده شد. آزمایش­های متعددی به منظور دستیابی به پیکربندی بهینه برای انجام شبیه سازی در بازه زمانی 18-21 آگوست 2016 با شرایط جوی پایدار در فصل تابستان اجرا شد. انتخاب مناسب­ترین پیکربندی با کمترین خطا، به عنوان بستری مناسب برای شبیه سازی­های اقلیم شهری جهت مطالعه راهکارهای تعدیل جزیره گرمایی شهری (UHI) مطرح می­شود. افزایش بازتاب سطوح جهت کاهش UHI در دامنه اعمال گردید. جهت ارزیابی عملکرد پیش­بینی مدل و مقادیر مشاهداتی متناظر با آن از دو شاخص ریشه میانگین مربعات خطا (RMSE) و میانگین خطای اریب (MBE) استفاده شد. نتایج نشان داد در استان تهران، بطورکلی تمامی پیکربندی­ها دمای هوا و سرعت باد را کمتر از مقدار واقعی و رطوبت نسبی را بیشتر از مقدار واقعی برآورد می­نمایند. همچنین در استان البرز تمامی پیکربندی­ها دمای هوا و سرعت باد را بیشتر از مقدار واقعی و رطوبت نسبی را کمتر از مقدار واقعی برآورد می­­نمایند. با افزایش انعکاس سطوح شهری، میانگین دمای استان­های تهران و البرز به ترتیب به میزان 6/0 و 2/0 درجه سانتی گراد کاهش می­یابد. سرعت باد مخصوصاً در نواحی شهری، مقداری افزایش می­یابد. همچنین شاهد افزایش میانگین رطوبت نسبی (خصوصاً در نواحی شهری) در مناطق مورد مطالعه خواهیم بود.
کلیدواژه‌ها

عنوان مقاله English

Sensitivity of physical parameterization of WRF model in urban climate simulation and Heat Island mitigation in a stable atmospheric condition (case study: Tehran and Alborz provinces)

نویسندگان English

fahimeh shakeri 1
gholamabbas Fallah ghalhari 2
hashem akbari 3
zahra hejazizadeh 4
1 hakim sabzevari university
2 Faculty of Geography and Environmental Sciences, Hakim Sabzevari University,sabzevar
3 ‌Building, Civil and Environmental Engineering Department, Concordia University, Montreal, Quebec, Canada
4 Professor of Climatology, Faculty of Geography, kharazmi University
چکیده English

In this research, the sensitivity of the meteorological elements (such as mean temperature, relative humidity and wind speed) to different physical parameterizations in the numerical forecast model (WRF) was evaluated to simulate the climate of the city and adjust the Urban Heat Island of the study area.To study urban environmental issues, the Urban Canopy Model (UCM) was coupled to the WRF model. Several experiments were performed to achieve optimal configuration for simulation in the period from 18-21 August 2016 in the stable atmospheric conditions in summer. Selection of the most appropriate configuration with the least error is proposed as an appropriate setting for urban climate simulations and the study of Urban Heat Island (UHI). Increasing surface reflections to reduce UHI in the range was applied. Two indices of Root Mean Square Error (RMSE), and Mean Bias Error (MBE) were used to evaluate the predictive performance of the model and its corresponding observational values. The results showed that in the province of Tehran, in general, all configurations estimate the air temperature and wind speed less than real and relative humidity more than the actual value. In Alborz province, all configurations estimate the air temperature and wind speed more than real and relative humidity less than real value. By increasing the reflection of urban levels, the mean temperature of Tehran and Alborz provinces decreases 0.6 and 0.2 ° C, respectively. Wind speed, especially in urban areas, increases somewhat. We also see an increase in relative humidity (especially in urban areas) in the studied areas.

کلیدواژه‌ها English

WRF model
Urban Canopy Model (UCM)
albedo
Physical Parameterization
Adinna E, Christian E.I., Okolie A.T. 2009. Assessment of urban heat island and possible adaptations in Enugu urban using landsat-ETM, Journal of Geography and Regional Planning, 2(2): 30-36.
Akbari H, Touchaei A.G. 2014. Modeling and labeling heterogeneous directional reflective roofing materials. Solar Energy Materials & Solar Cells, 124: 192–210.
Akbari H, Kolokotsa D. 2016. three decades of urban heat islands and mitigation technologies research, Energy Build,133: 834–842.
Akbari H, Pomerantz M, Taha H. 2001. Cool surfaces and shade trees to reduce energy use and improve air quality in urban areas. Solar Energy, 70(3): 295–310.
Akbari H, Bretz S, Kurn D.M, Hanford J. 1997. Peak power and cooling energy savings of high-albedo roofs. Energy and Buildings, 25 (2): 117-126.
Akbari H, Menon S, Rosenfeld A. 2009. Global cooling: increasing world-wide urban albedos to offset CO2, Climatic Change, 94 (3-4): 275-286.
Arnfield A.J. 2003. Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island, International Journal of Climatology, 23 (1): 1-26.
Baldinelli G, Bonafoni S, Anniballe R, Presciutti A, Gioli B, Magliulo V. 2015. Spaceborne detection of roof and impervious surface albedo: Potentialities and comparison with airborne thermography measurements, Solar Energy, 113: 281–294.
Balsamo G, Albergel C, Beljaars A, Boussetta S, Brun E, Cloke H.L, Dee D.P, Dutra E, Pappenberger F, DeRosnay P, Sabater J.M, Stockdale T, Vitart F. 2012. ERA-Interim/Land: A Global Land-Surface Reanalysis Based on ERA-Interim Meteorological Forcing. ECMWF: Reading, UK.
Bouyer J, Musy M, Huang Y, Athamena K. 2009. mitigating urban heat island effect by urban design: forms And materials. Paper presented at the Proceedings of the 5th urban research symposium, cities and climate change: responding to an urgent agenda, Marseille.
Das M.K, Chowdhury Md .A.M, Das S. 2015. Sensitivity Study with Physical Parameterization Schemes for Simulation of MCS Associated with Squall Events, International Journal of Earth and Atmospheric Science, 2(2): 20-36.
Fanni Z. 2006. Cities and urbanization in Iran after the Islamic revolution, Cities, 23(6): 407–411.
Greene J.S, Kalkstein L.S, Kim K.R, Choi Y.J, Lee D.G. 2016. The application of the European heat wave Of 2003 to Korean cities to analyze impacts on heat-related mortality, International Journal of. Biometeorology, 60: 231–243.
Heaviside C, Tsangari H, Paschalidou A, Vardoulakis S, Kassomenos P, Georgiou K.E, Yamasaki E.N. 2016. Heat-related mortality in Cyprus for current and future climate scenarios, Science of the Total Environment. 569–570: 627–633.
Hooshangi H, Akbari H, Touchaei A.G. 2016. measuring solar reflectance of variegated flat roofing materials using quasi-Monte Carlo method. Energy and Buildings, 114: 234-240.
Howard L. 1833. the climate of London, deduced from meteorological observations, made in the metropolis, and at various places around it.” 2nd ed., Harvey and Darton, vols. 1–3 London, UK
Jandaghian Z, Akbari H. 2018. the Effect of Increasing Surface Albedo on Urban Climate and Air Quality: A Detailed Study for Sacramento, Houston, and Chicago, Climate, 6(19):1-21, Doi: 10.3390/cli6020019
Jandaghian Z, Touchaei G.A, Akbari H. 2018. Sensitivity analysis of physical parameterizations in WRF for urban climate simulations and heat island mitigation in Montreal. Urban Climate, 24: 577–599.
Kolusu S., Seshagirirao K, Prasanna V, Preethi B. 2014. Simulation of Indian summer monsoon intraseasonal oscillations using WRF regional atmospheric model. International Journal of Earth and Atmospheric Science, 1: 35-53.
Krieger J.R, Zhang J, Atkinson D. E, Zhang X, Shulski M. D. 2009. Sensitivity of WRF model forecasts to different physical parameterizations in the Beaufort sea region. 8th Conference on Coastal Atmospheric and Oceanic Prediction and Processes. P.1- 2.
Kusaka H, Kimura F. 2004. coupling a single-layer urban canopy model with a simple atmospheric model: Impact on urban heat island simulation for an idealized case. Journal of Meteorological Society of Japan, 82(1): 67–80.
Kusaka H, Kondo H, Kikegawa Y, Kimura F. 2001 a simple single-layer urban canopy model for atmospheric models: Comparison with multi-layer and slab models. Boundary Layer Meteorology, 101 (3): 329–358.
Liao J, Wang T, Wang X, Xie M, Jiang Z, Huang X, Zhu J. 2014. Impacts of different urban canopy schemes In WRF/Chem on regional climate and air quality in Yangtze River Delta, China. Atmospheric Research,146: 226 -243.
Madanipour A. 1998. Tehran: The Making of a Metropolis, John Wiley & Sons, London, UK.
Martilli A, Clappier A, Rotach M. 2002. an urban surface exchange parameterisation for mesoscale models. Boundary-Layer Meteorology. 104: 261–304.
Morini E, Touchaei A. Castellani G. B, Rossi F, Cotana F. 2016. The Impact of Albedo Increase to Mitigate the Urban Heat Island in Terni (Italy) Using the WRF Model. Sustainability, 8 (999): 1-14, doi: 10.3390/su8100999
Nuruzzaman M.d. 2015. Urban Heat Island: Causes, Effects and Mitigation Measures - A Review. International Journal of Environmental Monitoring and Analysis, 3(2): 67-73. doi: 10.11648/j.ijema.20150302.15
Oke T.R. 1987. Boundary Layer Climates, 2nd ed.; Methuen: London, UK; New York, NY, USA, p. 452.
Pomerantz M, Akbari H. 1998. Cooler Paving Materials for Heat Island Mitigation, Proceedings of the 1998 ACEEE Summer Study on Energy Efficiency in Buildings, United States, 9: 135.
Rossi F, Bonamente E., Nicolini A, Anderini E, Cotana F. 2015. A carbon footprint and energy consumption Assessment methodology for UHI-affected lighting systems in built areas. Energy and Buildings, 114: 96–103.
Semsar M. 1986. Tehran Eine Stadtgeographische Studie, Springer, Vienna, Austria.
Sharan M, Gopalakrishnan S.G. 1997. Comparative evaluation of eddy exchange coefficients for strong and weak wind stable boundary layer modeling. Journal of applied meteorology, 36: 545-559.
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. National Center for Atmospheric Research: Boulder, CO, USA.
Stensrud D. J. 2007. Parameterization Schemes: Keys to understanding numerical weather prediction models. Cambridge University Press, pp.459
Taha H. 1997a. modeling the impacts of large-scale albedo changes on ozone air quality in the South Coast Air Basin. Atmospheric Environment, 31(11): 1667-1676.
Taha H. 1997b. urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat. Energy and Buildings, 25(2): 99-103.
Touchaei A.G. 2015. Characterizing the Effect of Increasing Albedo on Urban Meteorology and Air Quality in Cold Climates, a Case Study for Montreal, PhD. Dissertation, the Department of Building, Civil and Environmental Engineering, Concordia University Montreal, Quebec, Canada.
U.S. Geological Survey. USGS 2006. The National Land Cover Database. Accessed at: http://landcover.usgs.gov/usgslandcover.php; 05/25/2014.
Wang W, Seaman N.L. 1997. A comparison study of convective parameterization schemes in a mesoscale Model. Monthly Weather Review.125: 252-278.
Xu X, Gregory J, Kirchain R. August 1st, 2015. the Impacts of Surface Albedo on Climate and Building Energy Consumption: Review and Comparative Analysis. Transportation Research Board 95th Annual Meeting,