شبیه‌سازی توفان تندری با استفاده از مدل WRF در استان کرمانشاه مطالعه موردی: 31 مارس 2014

نوع مقاله : مقاله پژوهشی

نویسندگان

1 عضو هیات علمی گروه اقلیم شناسی دانشکده جغرافیای دانشگاه تبریز

2 دانشگاه حکیم سبزواری

چکیده

به‌منظور بررسی ساختار جو در زمان رخداد توفان تندری در استان کرمانشاه از کدهای مخابره شده از ایستگاه‌های سینوپتیک استان و پارامترهای فشار تراز دریا، ارتفاع ژئوپتانسیل، نم ویژه، مؤلفه باد مداری و مؤلفه نصف‌النهاری باد با مراجعه به تارنمای متعلق به NCEP/NCAR اخذ و برای مطالعات میان‌مقیاس از داده‌های رادیوسوند استفاده گردید. همچنین برای بارزسازی این پدیده از مدل WRF با توان تفکیک 15 کیلومتر استفاده گردید. در نهایت برای ارزیابی بین برونداد مدل در بخش بارش از مقادیر بارش برآورد شده سنجنده TRMM نیز بهره‌گیری شد. نتایج نشان داد ریزش هوای سرد و تشکیل یک ناوه عمیق بر روی غرب ایران، بوجود آمدن گرادیان شدید فشار در شمال غرب و غرب کشور و تشکیل یک جبهه سرد در منطقه، از عوامل عمده سینوپتیکی در زمان توفان بوده است. همچنین مشخص گردید که منابع رطوبتی بارش‌ها نیز از دریای عرب، سرخ و خلیج‌فارس تامین شده است. از طرف دیگر صعود شدید هوا در تشکیل ابرهای جوششی و هسته‌های تگرگ نقش عمده‌ای داشته‌اند. بررسی و ارزیابی برونداد مدل­ WRF (مقدار برآورد شده بارش 18 میلی­متر) و سنجنده TRMM (مقدار برآورد شده بارش 19 میلی­متر) در مقایسه با بارش گزارش شده از ایستگاه ها (مقدار برآورد شده بارش  17 میلی­متر) نشان داد که مدل WRF با توان تفکیک مذکور و RMSE برابر با 1 میلی­متر در بررسی ساختار جو در منطقه برای پویانمایی پدیده‌های میان‌مقیاسی همچون توفان تندری مناسب است.

کلیدواژه‌ها


عنوان مقاله [English]

Thunderstorm simulation using WRF model in Kermanshah Case Study: March 31, 2014

نویسندگان [English]

  • Ali mohammad Khorshiddoust 1
  • Mehdi Asadi 2
  • Hassan Hajmohammadi 2
1 Professor of Climatology- Department of Tabriz University
2 Hakim Sabzevari University
چکیده [English]

To study the atmospheric structure at the time of Thunder storm occurrence in Kermanshah province, were used the transmitted codes from synoptic stations of the province. To study the atmospheric structure, the sea level pressure parameters, geopotential heights, specific humidity, Uwnd and Vwnd were obtained from the NCEP / NCAR Web site, and Radio sonde data was used for intermediate studies. To illustrate this phenomenon, the WRF model was used with a resolution of 15 km. Finally, in order to evaluate the model output in the rainfall section, the estimated rainfall values of the TRMM sensor. The results showed that the cold weather and the formation of a deep cave on the west of Iran, the emergence of severe pressure gradient in the northwest and west of the country and the formation of a cold front in the region, were the main synoptic factors during the storm. Also, the role of the Arabian Sea, Red Sea and Persian Gulf can be mentioned in providing rain water resources. On the other hand, extreme climbs of the air have played a major role in the formation of bubble clouds and hailstones. The review and evaluation of the WRF model output (Estimated precipitation is 18 mm) and TRMM sensor (Estimated precipitation is 19 mm) compared to reported rainfall from stations (Estimated precipitation is 17 mm) showed that this model with RMSE=1 mm is suitable for exploring the structure of atmosphere in the region to dynamically visualize intermediate scale phenomena such as thunder storms.
Keywords: Thunderstorm, Synoptic, WRF model, TRMM sensor, Kermanshah province.
Introduction
Thunder storm is one of the most important, most abundant and most severe atmospheric hazards that due to its combination with rainstorms, hail and lightning, its effective role in creating sudden floods, both in terms of agriculture and in terms of financial and life damage, this phenomenon has always been of interest to researchers. Hail from grains or pieces of ice with a diameter of 5 to 50 mm and sometimes more. Hail intensive growth is the result of intense and frequent vertical movements of the air in the clouds of the cumulonimbus, which causes hailstones to absorb water droplets around them and freeze them. Creating and hailing, the presence of hot and humid air at the bottom of the atmosphere and climbing clouds accumulated with high altitude and high temperature combined with the continuation of the conditions of maximum instability of the air. Because of the importance of the hail event, studies have been conducted on its formation and growth in different countries in order to anticipate them. This can be cited by Sterling (2003) Prosenjit et al. (2008) by Litta et al. (2012).

Materials and methods
WRF model
The Advanced Modeling System (WRF) is an interdisciplinary and flexible model with many capabilities that can be used to simulate different atmospheric conditions. This model has been developing over the past few years, the model is available on a variety of computer systems, including a computer cluster, its range is very wide and covering a range between several meters to thousands of kilometers and by varying the different modes of physical parameters in the model, a wide range of performances can be achieved. In the course of this investigation and to simulate real thunderstorm was used from version 3.8 Medium scale WRF model. For this purpose, the network was considered with a horizontal resolution of 15 km with a moderate resolution in a network with dimensions of = 316x, = 451y. Also, FNL-type data were used with a one-degree separation as three hours.

Discussion and results
In Fig. 2, the arrangement of pressure patterns is such that a high pressure masses with a central pressure of 1016 hPa in the south of the Black Sea causes an hourly flow to occur in this region. On the other hand, in central Iran, a low-pressure cell with a central pressure of 1004 hPa has caused pressure gradient on the country. With an increase in the pressure gradient of about 12 hPa, a fracture in the pressure lines in the south of the Caspian region has occurred, which continues to the west of Iran. The formation of the fronts with extreme high currents has made it possible to provide conditions for incidental precipitation and thunderstorms in the region. On the other hand, in the middle of the atmosphere and on the west of Iran, the cold weather of the troposphere with the loss of geopotential has led to an ever-increasing upturn in air and instability. With 5450 geopotential meters placed on the area, a very powerful rotating core has affected all of the western and northwest regions of the country. This cold weather has caused the formation of a cold front in the region resulting in heavy rains and heavy storms along with hail in the area.

Conclusion
The results showed a high pressure masses with a central pressure of 1016 hpa in the south of the Black Sea, causing an hourly flow in this area. . On the other hand, in central Iran, a low-pressure cell with a central pressure of 1004 hPa has caused pressure gradient on the country. The model output also showed the highest positive vortices in the mid-range in the Iraq-Turkey region, which includes another core of the south-west. The existence of instabilities at all levels in the western regions has caused the conditions for storms in the region. The results of the total precipitation also showed that by eliminating the initial 6 hours of the output of the model, the maximum changes and the region's potentiality from the atmospheric systems referred to the east of Kermanshah province. Therefore, it is needed to demonstrate this phenomenon from the previous day and use more hours to explain the behavior and structure of the Thunder storm in the WRF model. On the other hand, it can be pointed out that simultaneous use of satellite data and dynamic model can be a good way to monitor and predicting thunderstorms with hail.

کلیدواژه‌ها [English]

  • Thunderstorm
  • Synoptic
  • WRF model
  • TRMM sensor
  • Kermanshah province
  1.  

    1. Banacos, P. G., & Schulta, D. M., 2004. Moisture Flux Convective Initiation Forcasting, www.ams.confex.com.
    2. Brown, D., & Reuter, G. 2018. WRF model simulations of the influence of the Athabasca oil sands development on convective storms. Earth Interactions, 22: 1-25.‏
    3. Case, J. L., Gatlin, P. N., Srikishen, J., Knickerbocker, J., Bell, J. R., Allen, R. E., & Petersen, W. A. 2018. Forecasting and Monitoring Intense Thunderstorms in the Hindu Kush Himalayan Region: Spring 2018 Forecasting Experiment [STUB].‏
    4. Chawla, I., Osuri, K. K., Mujumdar, P. P., & Niyogi, D. 2018. Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin. Hydrology & Earth System Sciences, 22.‏
    5. Donyadoost, G., Arkian, F., Ranjbar, A., Mirazaei, E. 2015. The Numerical Investigation of Thunderstorms using WRF-ARW Model in Tehran (ten Case studies). Journal of Climate Research, 1394(23), 49-80.
    6. Gevorgyan, A. 2018. Convection‐permitting simulation of a heavy rainfall event in Armenia using the WRF model. Journal of Geophysical Research: Atmospheres.‏
    7. Ghavidel Rahimi, Y, Farajzadeh Asl, M, Baghbanan, P. 2015. Changes in Temporal trend of Thunderstorms in Iran. MJSP, 19 (2):185-210.
    8. Ghavidel Rahimi, Y. 2010. Synoptic Mapping and Interpretation of Climate Using GRADS Software. Suha Danesh Publications Tehran.
    9. Iranpour, F. - al- din, Yazdanpanah, H., & Hanafi, A. (2014). Synoptic and Thermodynamic analysis of thunderstorm in Hamedan meteorology stations. GEOGRAPHY AND ENVIRONMENTAL HAZARDS, 4(1), 115-131.
      1. Jafarpour, I. 2006. The Basics of Climatology. Fourth edition. Tehran University Press. Tehran.
      2. Jahanbakhsh asll S, Mirhashemi H, Belvasi M. 2015. Studying the Trend of changes in the Frequency of Days with Thunderstorms in Iran Using Nonparametric Methods. Geores, 30 (2):33-46.
      3. Jalali, M., Kamariyan, V. 2017. Spectral Analysis of Time Series of Thunderstorms in Northwest of Iran. Geography and Planning, 21(61), 165-182.
      4. Karki, R., ul Hasson, S., Gerlitz, L., Talchabhadel, R., Schenk, E., Schickhoff, U., & Böhner, J. 2018. WRF-based simulation of an extreme precipitation event over the Central Himalayas: Atmospheric mechanisms and their representation by microphysics parameterization schemes. Atmospheric Research, 214: 21-35.‏
      5. Kaviani, M; Behlol A. 2008. The foundation of climatology. Publication Samt. Tehran.
      6. Khorshiddoust A M, Asadi M, Hajimohammadi H. 2017. The Study of regional structure of atmosphere during the thunderstorm hail event on16 to 18 July 2016 Case study: North West of Iran. Jsaeh. 4 (2): 37-50.
      7. Khorshiddoust, Kh., Rasuly, A. K., & Fakhari, M. 2017. Spatio-temporal Distribution of Lightning Phenomenon in Iran Using TRMM Lightning Image Sensor (LIS) Data. Geography and Enviromental Hazards, 6(1), 89-107.
      8. Litta, A., Mohanty, J, Sumam Mary, U and Idicula, C.  2012.  The diagnosis of severe thunderstorms with high-resolution WRF model. J. Earth Syst.  Sci.  121: 297-316.
      9. Mohan, P. R., Srinivas, C. V., Yesubabu, V., Baskaran, R., & Venkatraman, B. 2018. Simulation of a heavy rainfall event over Chennai in Southeast India using WRF: Sensitivity to microphysics parameterization. Atmospheric Research, 210: 83-99.‏
      10. Mojarrad F, Koshki S, Masompour J, Miri M. 2018. Analysis of Thunderstorm Instability Indexes in Iran using Reanalysis Data. Jsaeh, 4 (4) :33-48.
      11. Moosavi S M, Zarrin A, Mofidi A, Hosseini S F. 2017. Investigating the relationship between the frequency of thunderstorms and temperature trend in Mashhad. Geores, 32 (3) :74-87.
      12. Murthy, B. S., Latha, R., & Madhuparna, H. 2018. WRF simulation of a severe hailstorm over Baramati: a study into the space–time evolution. Meteorology and Atmospheric Physics, 130: 153-167.‏
      13. Nugraha, A. A. A., & Trilaksono, N. J. 2018. Simulation of wind gust–Producing thunderstorm outflow over Mahakam blocks using WRF. In AIP Conference Proceedings. 1987.
      14. Osuri, K. K., Mohanty, U. C., Routray, A., Mohapatra, M., and Niyogi, D., 2013, Real time track prediction of tropical cyclones over the North Indian Ocean using the ARW model: J. Appl. Meteor. Climatol., 52, 2476–2492.
      15. Prosenjit Chatterjee, D and Pradhan, De. U. K. 2008. Simulation of local sever storm mesoscale model MM5. Indian Journal of Radio & Space Physics, 37: 419-433.
      16. Rasuly, A. k., Khorshiddoust, A. M., & Fakhari, M. 2015. Investigation of Synoptic and Thermodynamic Conditions of the Thunderstorm Leading to Severe Flooding in Central Alborz on July19, Geography and Enviromental Hazards, 5(2), 127-142.
      17. Salehi, B., Aalijahan, M. 2015. Extraction and Analysis of Synoptic Patterns Resulting in Thunderstorm in Ardabil Plain. Physical Geography Research Quarterly, 47(3), 399-419.
      18. Singh, J., Gairola, A., & Das, S. 2015. Numerical Simulation of a Severe Thunderstorm over Delhi using WRF model. International Journal of Scientific and Research Publications.‏ 5: 1-6.
      19. Srinivas, C. V., Yesubabu, V., Prasad, D. H., Prasad, K. H., Greeshma, M. M., Baskaran, R., & Venkatraman, B. 2018. Simulation of an extreme heavy rainfall event over Chennai, India using WRF: Sensitivity to grid resolution and boundary layer physics. Atmospheric Research, 210: 66-82.
      20. Sterling, R. 2003. Trend in U.S. climate during the twentieth century; consequences, 2.
      21. Tavousi T, HamidianPour M, Dahani R. 2017. A Study of Temporal Variations and Spatial Analysis of Thunderstorms and their relation with ENSO. Jsaeh, 4 (3):17-34.
      22. Warner, T. T., 2011, Numerical Weather and Climate Prediction: Cambridge University Press, Cambridge, 550 pp.
      23. Zoljoodi M, Ghazi Mirsaeed M, Seifari Z. 2013. Verification of WRF model On Accuracy and Precision of Various Schemes and Evaluation of Precipitation Forecast in Iran. Geores; 28 (2) :187-194.