مروری بر کارایی مدل عددی WRF-ARW به‌عنوان ابزاری در شبیه‌سازی‌های بارش ایران‌زمین

نوع مقاله : مقاله مروری

نویسندگان

1 استادیار، گروه هیدرولوژی و منابع آب، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران.

2 دانشجوی کارشناسی، دانشکده مهندسی آب و محیط زیست، دانشگاه شهید چمران اهواز، اهواز، ایران.

چکیده

کسب اطلاع از نحوه توزیع و شدت بارش محتمل باعث بهبود دقت در اتخاذ تصمیمات مدیریتی در حین و پس از بارش در شرایط رخداد سیل می‌شود. امروزه با رشد علوم به‌خصوص در زمینه محاسبات کامپیوتری و حل معادلات پیشرفته جوی، مبتنی بر قوانین معتبر فیزیکی-دینامیکی این امکان در اختیار همگان به‌ویژه مدیران، بهره‌برداران و برنامه‌ریزان منابع آب قرار داده شده تا به کمک شبیه‌سازی، نحوه تغییرات شرایط جوی در آینده نزدیک را با عدم‌قطعیت کمتر از گذشته پیش‌بینی نمود. مدل عددی هواشناسی میان‌مقیاس WRF اخیراً مورد توجه محققان قرار گرفته و رفته‌رفته به مهم‌ترین ابزار برای مطالعات جو و پیش‌بینی تبدیل شده است، چراکه با اعمال به‌روزترین یافته‌های علوم جوی در قالب مجموعه‌ای از پارامتری‌سازی‌های فیزیکی (خردفیزیک ابر، تابش، همرفت، تلاطم لایه‌مرزی، انتقال دمای‌سطح و رطوبت در مقیاس زیرشبکه) یک روش ریزمقیاس‌نمایی دینامیک در شبیه‌سازی فرایند‌های جوی فراهم می‌کند. بدین‌منظور، در مطالعه حاضر جهت آشنایی مخاطبان با مدل WRF با هسته ARW و همچنین برای دراختیار قرار دادن مجموعه‌ای از نقطه‌نظرات سازنده و پیشنهادات حاصله در رابطه با کاربست مدل WRF-ARW در شبیه‌سازی بارش، سعی شد با گردآوری، مرور و جمع بندی، نتایج چندی از پژوهش‌های انجام پذیرفته (در داخل کشور) ارائه گردند. از این‌رو طبیعتا مقالات بیشتری نه‌تنها در داخل کشور بلکه در خارج از کشور توسط محققان کشورمان منتشر گشته‌اند که در اینجا مجال بررسی همه آن‌ها در قالب یک مقاله مروری نبود و مشخصا یکی از اهداف پژوهشی نویسندگان این مقاله درآینده خواهد بود.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

A review of the WRF-ARW numerical model's performance as a tool for precipitation simulations over Iran

نویسندگان [English]

  • Mohammad Amin Maddah 1
  • Fatemeh Parhizkar 2
1 Assistant Professor, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
2 Undergraduate student, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
چکیده [English]

Knowledge of the spatial distribution and intensity of an impending heavy rainstorm improves the accuracy of management decisions made before, during, and after the storm. Thanks to advances in science, particularly in the field of computer calculations and solving advanced atmospheric equations based on valid physical and dynamic equations, everyone, especially managers and planners of water resources, now can predict how the weather will change in the near future with less uncertainty than in the past. Researchers have recently regarded the Weather Research and Forecasting (WRF) mesoscale model as an essential tool for atmospheric studies and forecasting, because it combines the most recent advances in atmospheric sciences with a set of physical parameterization options (cloud microphysics, radiation, convection, boundary layer turbulence, surface temperature, and moisture treatment at a sub-grid scale) to produce a dynamic downscaling model in simulating atmospheric processes. Therefore, in the current study, we attempted to collect, review, and summarize the results of several studies conducted (within the country) in order to familiarize the audience with the WRF model with ARW core, as well as to provide a set of constructive points of view and suggestions regarding the application of the WRF-ARW model in precipitation simulation. Naturally, domestic researchers have published so many investigations both within the country and overseas that there was no way to examine them all in the form of a review article; however, this will undoubtedly be one of the authors' future study goals.

کلیدواژه‌ها [English]

  • Dynamic downscaling
  • Numerical model
  • Precipitation simulation
  • WRF

مراجع

Akbari, Z., & Davoodi, R. (2019). 24 and 48 hour rainfall forecast verification of WRF and GFS regional model with observational data in Lorestan province, In: Proceeding of 4th International Congress of Developing Agriculture, Natural Resources, Environment and Tourism of Iran, 13-15 Feb., Tabriz Islamic Art University, Tabriz, Iran, 1-13. (In Persian).
Azadi, M., Ghazi Mir Saeed, M., & Jafari, S. (2009), Performance evaluation of WRF model for forecasting rainfall in Iran for one month, In: Proceeding of 12th Conference on Fluid Dynamics, 28-30 April, Babol Noshirvani university of technology, Babol, Iran, 1-6. (In Persian).
Azadi, M., Shirgholami, M. R., Hajjam, S., & Sahraian, F. (2012b). WRF Model Output Postprocessing for Daily Precipitation over Iran, Iran Water Resources Research, 7(4), 71. (In Persian).
Azadi, M., Soufiyani, M., Vakili, G., & Ghaemi, H. (2016). 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), 110-119. (In Persian).
Azadi, M., Taghizadeh, E., & Memarian, M. H. (2012a). Verification of WRF Precipitation Forecast over Iran Country during Nov. 2008-Jun. 2009, Iran Water Resources Research, 8(2), 48. (In Persian).
Basha, C. Z., Bhavana, N., Bhavya, P., & Sowmya, V. (2020). Rainfall Prediction using Machine Learning & Deep Learning Techniques. In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 92-97). IEEE.
Bijandi, M. (2019). Flood Prediction Using HEC-1 Model and WRF Postprocessed Model Based on Neural and Fuzzy Particle Swarm Algorithm and GPM Satellite Data: A Case Study of April 1398 Flood in Northeast of Iran, In: Proceeding of 7th comprehensive conference on flood engineering and management, 5-7 Aug, Tehran, Iran. (In Persian).
Cloke, H. L., & Pappenberger, F. (2009). Ensemble flood forecasting: A review. Journal of hydrology, 375(3-4), 613-626.
Duzenli, E., Yucel, I., Pilatin, H., & Yilmaz, M. T. (2021). Evaluating the performance of a WRF initial and physics ensemble over Eastern Black Sea and Mediterranean regions in Turkey. Atmospheric Research, 248, 105184.
Emmanouil, G., Vlachogiannis, D., & Sfetsos, A. (2021). Exploring the ability of the WRF-ARW atmospheric model to simulate different meteorological conditions in Greece. Atmospheric Research, 247, 105226.
Faridmojtahedi, N., Ghaffarian, P., & Negah, S. (2017). Analyzing the Spatial Distribution of Heavy Snow Fall Depth in Gilan Plain (February 2005, January 2008, and February 2014) Using WRF Model, Journal of Geography and Environmental Hazards, 6(21), 109-126. (In Persian).
Ghafarian, P., & Barekati, S. M. (2013). Verification of the Weather Research and Forecasting Model (WRF) for the Heavy Precipitation Forecasting in the Karun basin. A case study (8-9 February 2006), Journal of Climate Research, 4(15), 129-140. (In Persian).
Ghamariadyan, M., & Imteaz, M. A. (2021). A Wavelet Artificial Neural Network method for medium‐term rainfall prediction in Queensland (Australia) and the comparisons with conventional methods. International Journal of Climatology, 41, E1396-E1416.
Goodarzi, L., Banihabib, M. E., & Ghafarian, P. (2018). Evaluation of the WRF Model Performance for Heavy Rainfall Simulation A Case Study of the Kan Basin in Iran, Water and Soil Conservation, 25(1), 229-242. (In Persian).
Hong, S. Y., Dudhia, J., & Chen, S. H. (2004). A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Monthly weather review, 132(1), 103-120.
Imani Amirabad, S., Farrokhnia, A., Dehban, H., Hassanli, A.M., Javadi, F., & Najafi, M.S. (2019), WRF and GFS forecast performance model in forecasting heavy rainfall in the last country, In: Proceeding of 7th comprehensive conference on flood engineering and management, 5-6 Aug., Tehran, 1-19. (In Persian).
Jaberi, P., Sabetghadam, S., & Ghader, S. (2021). Visibility prediction during fog and precipitation using the WRF model over Tehran, Journal of Spatial Analysis Environmental Hazarts, 7(3), 107-124. (In Persian).
Karimkhani, M., Jamshidi, T., Azadi, M., & Fattahi, E. (2018). Resolution impacts for accuracy of forecasting precipitation using the WRF model Area of Study: Karkhe and Karoon basin, Journal of Wetland Ecobiology, 9(4), 55-74. (In Persian).
Khansalari, S., & Ranjbar Saadatabadi, A. (2020). Investigating the causes of different performance of WRF model in forecasting rainfall of different weather systems from a Dynamic Perspective: A case study, Nivar, 108, 1-10. (In Persian).
Khodamoradpour, M., & Irannejad, P. (2020). Evaluation of the calibrated snow model of the NOAH-MP land surface scheme coupled in the WRF using MODIS images in areas with different land-surface features, Journal of Agricultural Meteorology, 7(2), 15-25. (In Persian).
Mahala, B. K., Mohanty, P. K., Xalxo, K. L., Routray, A., & Misra, S. K. (2021). Impact of WRF Parameterization Schemes on Track and Intensity of Extremely Severe Cyclonic Storm “Fani”. Pure and Applied Geophysics, 178(1), 245-268.
Mehdizadeh, S. (2020). Using AR, MA, and ARMA time series models to improve the performance of MARS and KNN approaches in monthly precipitation modeling under limited climatic data. Water Resources Management, 34(1), 263-282.
Mehralipour, M. A., Fathian, H., Nikbakht Shahbazi, A., Zohrabi, N., & Mobarak Hassan, E. (2020). Parameter uncertainty analysis by Monte-Carlo method for flood forecasting using WRF Prediction of Precipitation and Air Temperature in Dez Basin, Iran Water Resources Research, 16(2), 115-131. (In Persian).
Memarian, M. H., & Daman Afshan, M. (2016). Evaluation of cloudiness prediction resulting from WRF model, Journal of the Earth and Space Physics, 42(1), 183-196. (In Persian).
Negah, S., Momenpoor, F., Ghaffarian, P., Faridmojtahedi, N., & Asadi Oskooiee, E. (2015). Identification and formation mechanism analysis of spatial pattern snowfall in central plain of guilan (delta snow) by using weather and research forecast (WRF) model, Journal of Climate Research, 5(19), 113-125. (In Persian).
Nikfal, A., Kashi, M., Khodam, N., Hashemi, M., & Karami, J., (2019). Meteorological analysis and numerical simulation of Lorestan flood in April 2017 using WRF atmospheric model, In: Proceeding of 7th comprehensive conference on flood engineering and management, 5-6 Aug., Tehran, 1-11. (In Persian).
Pourghasemi Ardakani, M. A., & Memarian, M.H. (2018). Investigation of the effect of some microphysical parameters on precipitation, using the WRF model, In: Proceeding of 7th National Conference on Water Resources Management, 25-26 April, Yazd University, Yazd, Iran. (In Persian).
Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J.L., Gochis, D.J., Ahmadov, R., Peckham, S.E., Grell, G.A., & Duda, M. G. (2017). The weather research and forecasting model: Overview, system efforts, and future directions. Bulletin of the American Meteorological Society, 98(8), 1717-1737.
Rahmanian, M., Ahmadi Hojjat, M., Kalateh Seifri, Z., & Malahi, S. (2012). Performance evaluation of WRF model for predicting atmospheric hazards of a strong rainfall system: a case study, In: Proceeding of 1st national conference on sustainable development strategies (agriculture, natural resources) And Environment), Tehran, Iran, 1-7. (In Persian).
Salahi, A., Ashrafzadeh, A., & Vazefehdoost, M., (2020). Comparison of estimated precipitation from GPM satellite, Doppler meteorological radar and WRF precipitation forecast model with ground station data in Gilan province, In: Proceeding of 18th Iranian Hydraulics Conference, 5-6 Feb., Tehran University, Tehran, Iran, 1-6. (In Persian).
Sasanian, S., Azadi, M., & Ghorban Fallah, R., (2015). Evaluation of WRF model performance with different physical options for predicting winter rainfall in southwestern Iran, In: Proceeding of 1st scientific congress on the development and promotion of agricultural sciences, natural resources and environment, Tehran, Iran, 1-12. (In Persian).
Shirali, E., Nikbakht Shahbazi, A., Fathian, H., Zohrabi, N., & Mobarak Hassan, E. (2021). Evaluation of WRF Model for Simulation of Precipitation and Flood Forecasting in Karun 4 Basin, Iranian Journal of Soil and Water Research, 51(8), 1907-1920. (In Persian).
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J.G., Duda, M.G., Barker, D.M., & Huang, X. Y. (2019). A description of the advanced research WRF model version 4. National Center for Atmospheric Research: Boulder, CO, USA, 145.
Torabian, M. J., & Heidari, M. (2019). Simulation of Cumulative Flood in Qom with WRF Numerical Model, In: Proceeding of 6th Regional Conference of Climate change, 18-19 Nov., Tehran, Iran, 1-7. (In Persian).
Yang, Q., Yu, Z., Wei, J., Yang, C., Gu, H., Xiao, M., Laux, P., Arnault, J., Gao, L., Dong, N., Shang, S., & Kunstmann, H. (2021). Performance of the WRF model in simulating intense precipitation events over the Hanjiang River Basin, China–A multi-physics ensemble approach. Atmospheric Research, 248, 105206.
مدیریت آب و آبیاری
دوره 11، شماره 3
آبان 1400
صفحه 561-573

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