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پیش‌بینی پتانسیل سقوط بهمن با استفاده از یک مدل پیش‌بینی عددی (مطالعه موردی: منطقه شهرستانک، 26-28 دسامبر 2016) | ||
| فیزیک زمین و فضا | ||
| مقاله 11، دوره 44، شماره 3، آبان 1397، صفحه 641-658 اصل مقاله (1.23 M) | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2018.246101.1006945 | ||
| نویسندگان | ||
| سحر تاجبخش* 1؛ امیرحسین نیک‌فال2 | ||
| 1استادیار، گروه کاوشهای جوی، پژوهشکده هواشناسی، تهران، ایران | ||
| 2کارشناس هواشناسی، پژوهشکده هواشناسی، تهران، ایران | ||
| چکیده | ||
| بهمن یکی از مهم‌ترین مخاطرات وضع هوا در مناطق کوهستانی و برف‌گیر است و پیش‌بینی صحیح آن در افزایش ایمنی جاده‌های کوهستانی نقش مؤثری دارد. از این‌رو در این تحقیق کوشش شده است ضمن بررسی شرایط همدیدی رخداد بهمن در یک مطالعه موردی، مهم‌ترین شاخص‌های پیش‌بینی پتانسیل رخداد بهمن با استفاده از یک مدل پیش‌بینی عددی برآورد و احتمال رخداد بهمن بر اساس یکی روش‌های کاربردی موجود بررسی شود. مطالعه موردی به‌گونه‌ای انتخاب شده که شرایط جوی قابل‌توجهی برای رخداد بهمن در منطقه دیده نمی‌شود و تنها یک مورد سقوط بهمن در طول 24 ساعت در منطقه شهرستانک جاده چالوس گزارش شده است. مهم‌ترین نتایج این بررسی نشان می‌دهد که الگوهای پیش‌بینی همدیدی وضع هوا در این مطالعه، با شرایط جوی واقعی همخوانی دارد و ویژگی مشخصی برای وقوع بهمن در این الگوها دیده نمی‌شود. آستانه‌های معرفی شده برای پتانسیل وقوع بهمن با رخداد واقعی در این مطالعه همخوانی خوبی دارد. وزش باد کمتر از m/s 9، بارش برف کمتر از 30 سانتی‌متر در 24 ساعت، افزایش دما کمتر از C° 8 در 12 ساعت، عدم ماندگاری دما در محدوده 4- تا C° 10- و آسمان نیمه‌ابری در طول روز بدون بار باران، شرایطی است که بر احتمال رخداد بهمن و نه قطعیت آن (پتانسیل نامشخص وقوع بهمن) تأکید دارد و برای پیش‌بینی پتانسیل بالای وقوع بهمن، آستانه‌های جوی باید بالاتر از مقادیر یاد شده باشند. | ||
| کلیدواژه‌ها | ||
| بسته برف؛ ضخامت برف؛ آب معادل برف؛ بهمن و مدل پیش‌بینی عددی WRF | ||
| عنوان مقاله [English] | ||
| Avalanche potential forecast using a numerical weather predicton model,(Case study: Shahrestanak zone, 26-28 December 2016) | ||
| نویسندگان [English] | ||
| sahar Tajbakhsh1؛ Amirhosein Nikfal2 | ||
| 1Assistant Professor, Atmospheric Survey Research Group, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran | ||
| 2Meteorology expert, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran | ||
| چکیده [English] | ||
| Avalanches are likely to happen in winter time mountainous regions of Iran, and its timely prediction can help to improve the road traffic safety. The aim of this study is to provide the avalanche first guess using Numerical Weather Prediction (NWP) model outputs. Since the meteorological observations in mountainous areas are very scarce, access to snow data in ground measurements is not feasible; it seems that making use of numerical models to simulate the associated data and predicting the avalanche first guess may be a reliable method. For this purpose, three avalanche events which occurred in Chalous Road (Kanduan-Gachsar) were investigated synoptically as the case studies. Then the precipitation (water equivalent to snow, snow thickness), temperature and wind outputs of Weather Research and Forecasting (WRF) model were analysed based on the Spangler classification tables in order to determine the potential of avalanche events. Snow density, snow water equivalent and snow depth are the most important factors of snow cover that have fundamental applications in predicting avalanche and flood events. The predictions in this study are based on the WRF model numerical simulations. Spangler et al. (2009) presented a model for estimating the avalanche potential based on the three components of the region’s climatic conditions, present weather survey and forecasting avalanche indices. For verification of the model, the threshold values for precipitation, temperature and wind were calculated in Colorado. In the present study, only the third part of the Spengler method (prediction of avalanche occurrence potential) was applied to make the first guess of avalanche potential occurrence in the mountainous regions of the country. The period 26 -29 December 2016 for heavy snow conditions with multiple avalanche was considered as a case study. The area under study is the Shahrestanak (36, 10 °N and 51.31° E) on the Chalous road, which experienced more than 10 avalanche events during winter 2016. Its elevation is about 2230 to 2240 meters and is located in Alborz Province. There are similar climatic conditions in the two regions of Colorado and Alborz based on the Gutten climate classification but their temperature and type of snowpack are different according to the Sturm snowpack classification. The type of snowpack in the Colorado area is prairie (thin and moderate cold snow covering with substantial wind drifting, with the maximum depth of about 1 meter),while the type of snowpack in Alborz area is ephemeral (thin and warm snow that melts down soon and its depth is between zero to 50 cm). Hence, it seems that the thresholds of the meteorological indicators related to avalanche potential in Alborz region could be slightly lower than its thresholds in Colorado area. The synoptic study was done using the mean daily ERA-Intrim data. In the present study, the patterns of sea level pressure, thickness of 500/1000 hPa, geopotential heights and temperature with relative vorticity of 500 hPa, vertical velocity at 700 hPa, as well as relative humidity and winds of 850 hPa were studied in order to identify the weather conditions during the avalanche period. Also, using the WRF meso-scale model (ver. 3.9), the simulation of atmospheric condition is conducted for 4 days (26-29 December 2016). Temperature, wind, cloudiness, snow thickness, snow water equivalent and snow cover (the most important indicators of avalanche) were determined using the WRF numerical prediction model. Then the potential of avalanches’ occurrence was investigated according to the Spengler model. Here, we attempt to investigate the potential of avalanche event in a case study using a NWP model and determine the probability of occurrence of avalanche based on one of the existing methods. The case study was selected in such a way that no significant atmospheric conditions were observed in the area. There was only one case of avalanche over 24 hours in the Chalous Road, Shahrestanak position. The results showed that the patterns of weather forecasting in this study are in agreement with actual weather conditions and there is no specific feature for avalanche occurrence in these patterns. The presented thresholds for the avalanche potential have good match in this case study. Winds of less than 9 m/s, snow depth of less than 30 cm in 24 hours, temperature of less than 8 °C in 12 hours, unstable temperature in the range of -4 to -10°C, and cloudy sky during the day without rain emphasize the probability of avalanche and not its certainty. To predict the high potential for avalanche, the atmospheric thresholds should be higher than these values. | ||
| کلیدواژه‌ها [English] | ||
| Snowpack, Snow depth, Snow water equivalent, Avalanche, WRF numerical weather prediction | ||
| مراجع | ||
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تاجبخش، س.، رهنما، م. و نیک‌فال، الف. ح.، 1397، مقایسه پوشش برف بین برونداد یک مدل پیش‌بینی عددی و داده‌های سنجندهMODIS در ایران، نشریه تحقیقات منابع آب (زیر چاپ).
Armstrong, R. L. and Brun, E. (Eds.), 2010, Snow and Climate. Cambridge University Press, 222 pp. Bartelt, P. B. and Lehning, M., 2002, A physical SNOWPACK model for avalanche warning. Part I: Numerical model. Cold Reg. Sci. Technol., 35, 123–145 Brun, E., David, P., Sudul, M. and Brunot, G., 1992, A numerical model to simulate snow-cover stratigraphy for operational avalanche forecasting. J. Glaciol., 38, 13–22. Brun, E., Martin, E., Gendre, V. S. C. and Coleou, C., 1989, An energy and mass model of snow cover suitable for operational avalanche forecasting. J. Glaciol., 35, 333–342. Comiso, J., Cavalieri, D. and Markus, T., 2003, Sea ice concentration, ice temperature, and snow depth using AMSR-E data. IEEE Trans. Geosci. Remote Sens., 41, 243–252. Cox, S. and Fulsaas, K., 2003, The freedom of the hills Mountaineers Books Mountaineers (Society) Edition 7, illustrated The Mountaineers Books, ISBN 978-0-89886-827-2, pages 346, 347. Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S. and Vitart, F., 2011, The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. http://doi.org/10.1002/qj.828 Ducharm, P., Houdayer, A. Y., Choquette, Y., and Kapfer, B., 2015, Numerical Simulation of Terrestrial Radiation over a Snow Cover, Journal of Atmospheric and Oceanic Technology, 32, 1478, 1485. Etchevers, P. and Martine, E., Brown, R., Fierz, Ch., Lejeune, Y. and Bazili, E., 2004, Validation of the energy budget of an alpine snowpack simulated by several snow models (SnowMIP project). Ann. Glaciol., 38, 150–158. Frei, A., Brown, R., Miller, J. A. and Robinson, D. A., 2005, Snow mass over North America: Observations and results from the second phase of the atmospheric model intercomparison project. J. Hydrometeor., 6, 681–695. Gustafsson, D., Stahli, M. and Jansson, P. E., 2001, The surface energy balance of a snow cover: Comparing measurements to two different simulation models. Theor. Appl. Climatol., 70, 81–96. Iacono, M., Mlawer, E., Collins, W., 2008, RTMIP Forcing for the AER Models and Status of the RRTMG Applications, Oral presentation at the ARM Radiative Process Working Group Meeting, Princeton, New Jersey. Janjic Zavisa, I., 1994, The Step–Mountain Eta Coordinate Model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927–945. Janjic, Z. I., 2002, Nonsingular implementation of the Mellor-Yamada Level 2.5 Scheme in the NCEP Meso model. NCEP Office Note No. 437, 61 pp. Kottek, M., 2011, Comments on: 'The thermal zones of the Earth' by Wladimir Köppen (1884) . Meteorologische Zeitschrift. 20(3): 361–365. Lehning, M., Bartelt, P. Brown, B. and Fierz, C., 2002, A physical SNOWPACK model for the Swiss avalanche warning: Part III: Meteorological forcing, thin layer formulation and evaluation. Cold Reg. Sci. Technol., 35, 169–184. Male, D. H., and Granger, R. J. 1981, Snow surface energy exchange. Water Resour. Res., 17, 609–627. Marty, C. and Meister, R., 2012, Long-term snow and weather observations at Weissfluhjoch and its relation to other high-altitude observatories in the Alps. Theoretical and Applied Climatology: 1-11. doi:10.1007/s00704-012-0584-3. Mlawer, E. J., Taubman, S., J. Brown, P. D. Iacono, M. J. and Clough, S. A., 1997, Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated–k model for the longwave. J. Geophys. Res., 102, 16663–16682. Pulliainen, J., 2006, Mapping of snow water equivalent and snow depth in boreal and subarctic zones by assimilating spaceborne microwave radiometer data and ground-based observations. Remote Sens. Environ. 101, 257–269. Rinen, O., Eerola, K., Siljamo, N. and Koskinen, 2008, Comparison of Snow Cover from Satellite and Numerical Weather Prediction Models in the Northern Hemisphere and Northern Europe, Journal of applied meteorology and climatology, 49, 1199-1216. Roeger, C., McClung, D. Stull, R. Hacker, J. and Modzolewski, H., 2001, A verification of numerical weather forecasts for avalanche prediction. Cold Reg. Sci. Technol., 33, 189–205. Roeger, C., McClung, D. Stull, R. Hacker, J. and Modzolewski, H., 2003, Weather and Forecasting, 18(6), 1140-1160. Schirmer, M., Wirz, V., Clifton, A. and Lehning, M., 2011, Persistence in intra-annual snow depth distribution:Measurements and topographic control., Water Resources Research, Vol. 47, W09516,doi:10.1029/2010WR009426. Serquet, G., Marty, C. and Rebetez, M., 2013, Monthly trends and the corresponding altitudinal shift in the snowfall/precipitation day ratio, Theoretical and Applied Climatology, doi: 10.1007/s00704- 013-0847-7. Spangler, T., 2015, Snowpack assessment, http://www.meted.ucar.edu/afwa/snowpack/contrib.htm. Sturm, M., Holmgren, J. and Liston, G., 1995, A Seasonal Snow Cover Classification System for Local to Global Applications, Journal of Climate, 8, 1261-1283. Sturm, M., Taras, B., Liston, G., Derksen, C., Jonats, T. and Lea, J., 2010, Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes, Journal of Hydrometeorology, 11, 1380-1394. Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek, M., Gayno, G., Wegiel, J. and 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. Thompson, G., Field, P., Rasmussen, R. and Hall, W., 2008, Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization. Mon. Wea. Rev., 136, 5095–5115. Tiedtke, M., 1989, A comprehensive mass flux scheme for cumulus parameterization in large–scale models. Mon. Wea. Rev., 117, 1779–1800. | ||
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