Simulation and analysis of the factors affecting the phenomenon of snowfall in the north of Iran in February 2014

Document Type : Original Article

Authors

1 Pajoohesh Blvd, Shahid Kharrazi Highway,

2 Meteorology

3 Atmospheric Science & Meteorological Research Center, Pajoohesh Blvd, Shahid Kharrazi Highway, West of Shahid Hemmat Highway, Tehran, I.R. of Iran P.O. Code: 16385-14977

Abstract

On February 1-6, 2014, a large snowstorm occurred in Iran, especially in the northern regions of the country. Snowfall in northern Iran was unprecedented in the last 30 years and caused extensive damage. Synoptic study of snowstorm phenomenon showed that there is a surface high pressure system in the north and a dynamic low pressure system in the east and southeast of Iran in the sea level pressure map and a deep through at the level of 500 hPa. The 925hpa wind map shows that the north and northwest winds throughout Iran cause cold air to enter from higher elevations. At 300hpa, the core of a western jet is located in central Iran and there is a northern jet north of Iran's borders. Maps of sea surface pressure anomalies show an unprecedented increase in pressure in northern Iran, as well as surface temperature anomalies show a sharp decrease in temperature throughout Iran, especially in the northeast. Examination of cross-sectional area of wind, temperature and relative humidity showed that in addition to large-scale and thermodynamic factors, the presence of a cold front in eastern Iran is another reason for rainfall in this region. Comparison of the output of the WRF model with the maps related to the mentioned date shows that the model simulates the synoptic pattern of the region in this sample well, although the intensity of the systems is slightly less than the real value. Examining the precipitation output of the model, it can be concluded that the model has calculated the height of snow and the extent of the areas covered by it much less than the actual values, but the main error is in determining the type of precipitation.



Materials and methods

In this study, First, the snowstorm from 1 February 1 to 6 February, 2014 is evaluated synoptically and thermodynamically by using ERA5 data with an accuracy of 0.5 degrees. The European Climate Forecast Center (ECMWF) presents its forecasts at 37 pressure levels from surface (1000 hPa) to 1 hPa. ERA-Interm re-analysis data was replaced by ERA4 data, which provides a new, higher-quality atmospheric quantity analysis (Di et al., 2011; Francis et al., 2019). In this study, the mean sea level pressure, geopotential height at the level of 500 hPa, 850 hPa and 925 hPa along with zonal and meridional winds at several levels with a spatial resolution of 0.5 degrees were used.

Also, snowfall data, maximum and minimum temperatures of several synoptic stations located in northern Iran were obtained from their SYNOP reports, which well indicate the severity of the storm and its extent. Then, in order to simulate this event, the WRF model with a horizontal accuracy of 30 km and 30 vertical levels was implemented from 00 UTC on February 1 to 00 UTC on February 6, 2014. For the initial and boundary conditions of the model, GFS analysis data were used with an accuracy of 0.5 degrees.



Conclusion



In February 2014, between 1 and 6 February, heavy snowfall affected most parts of Iran, especially the northern regions of the country. Investigation of the Earth's surface synoptic map showed that a high-pressure system is located in the north and a dynamic low-pressure system is located in the east and southeast of Iran, which over time strengthens the surface high pressure and its tabs extend to central Iran and push back the low pressure. Also, the existence of an occluded front is obvious in the north of Afghanistan, followed by a cold front in eastern Iran. At the level of 500 hPa, there is a deep trough that moved cold air from higher latitudes and high surface pressure that causes this cold air to fall to the ground. On the other hand, if the humidity has risen to the mid-levels of the atmosphere, the necessary moisture will Provide for rainfall. The 925hPa wind map shows the north and northwest winds blew throughout Iran, which cause that cold air entered from higher elevations.

The existence of a cold front in the east of Iran was confirmed in the synoptic map of 850hPa level and the intersection of geopotential and isothermal height bands, as well as the cross-sectional area of the meridional wind, temperature and relative humidity components. The change of wind direction from south to north wind, extreme temperature gradient, slope of isothermal lines and extreme relative humidity gradient are all signs of the presence of the front that has been observed in eastern Iran. Therefore, it can be concluded that in addition to large-scale and thermodynamic factors, the presence of a cold front can also be another reason for existence of precipitation.

Comparing the output of the WRF model with the maps related to the mentioned date shows that the model has simulated well the synoptic pattern of the region in this case. The output of the model in the ground map has obtained the surface high pressure in the north of the country and low pressure located in the eastern and southern half of the country and has shown the pressure gradient in the center of Iran well. The model also shows the low-altitude and high-altitude geopotential centers of different compression levels similar to the real maps, and the well has a level of 500hpa and even the tilt of its axis to the east. The comparison of the maps shows that the intensity of surface high pressure, high altitude of 850 hPa and 500 hPa is slightly less than the real value. The WRF model obtained the height of snow and the extent of the areas covered by it far less than the values reported from the stations. By examining the amount of rainfall obtained by it, it can be concluded that the main error of the model is in calculating the type of precipitation that maybe is in result of error in calculating the temperature

Keywords


  1. Khodamorad Pour, M., Irannejad, P., Akhavan, S. and Babei, K., 2018. The evaluation of snow model in NOAH-MP coupled with WRF model during the periods of heavy snow over the northern and western regions of Iran. Iranian Journal of Geophysics, 11(4), pp.146-163.
  2. Faridmojtahedi, N., Ghaffarian, P. and 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(1), pp.109-126.

 

  1. Balk, B., and K. Elder, 2000, Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed. Water Resour. Res., 36, 13–26.
  2. Blo¨schl, G., 1999, Scaling issues in snow hydrology. Hydrol. Processes, 13, 2149–2175.
  3. Chou, M. D. and Suarez, M. J., 1994, An efficient thermal infrared radiation parameterization for use in general circulation models, NASA Tech. Memo.104606, 3, 85p.
  4. Elder, K., Dozier, J., & Michaelsen, J, 1991, Snow accumulation and distribution in an alpine watershed. Water Resources Research, 27(7), 1541-1552.
  5. Essery, R., & Pomeroy, J., 2004, Vegetation and topographic control of wind-blown snow distributions in distributed and aggregated simulations for an Arctic tundra basin. Journal of Hydrometeorology, 5(5), 735-744.
  6. Faranda, D., 2020. An attempt to explain recent changes in European snowfall extremes. Weather and Climate Dynamics, 1(2), pp.445-458.
  7. Gauer, P., 1998, Blowing and drifting snow in Alpine terrain: numerical simulation and related field measurements. Ann. Glaciol., 26, 174 – 178.
  8. Ikeda, K., Rasmussen, R., Liu, C., Gochis, D., Yates, D., Chen, F., Tewari, M., Barlage, M., Dudhia, J., Miller, K. and Arsenault, K., 2010. Simulation of seasonal snowfall over Colorado. Atmospheric Research, 97(4), pp.462-477.
  9. Kain, J. S. and Fritsch, J. M., 1993, Convective parameterization for mesoscale models: The Kain- Fritcsh scheme. The representation of cumulus convection in numerical models, K. A. Emanuel and D.J. Raymond, Eds., Amer. Meteor. Soc., 246 p.
  10. Karami, S., Hamzeh, N.H., Alam, K. and Ranjbar, A., 2020. The study of a rare frontal dust storm with snow and rain fall: Model results and ground measurements. Journal of Atmospheric and Solar-Terrestrial Physics, 197, p.105149.
  11. Khodamorad Pour, M., Irannejad, P., Akhavan, S. and Babei, K., 2018. The evaluation of snow model in NOAH-MP coupled with WRF model during the periods of heavy snow over the northern and western regions of Iran. Iranian Journal of Geophysics, 11, pp.146-163.
  12. Lin, Y. L., Farley, R. D. and Orville, H. D., 1983, Bulk parameterization of the snow field in a cloud model, J.Climate Appl. Meteor., 22, pp. 1065- 1092.
  13. Liston, G.E., and M. Sturm, 1998, A snow-transport model for complex terrain. J. Glaciol., 44, 498 – 516. Essery, R.L.H., L. Li and J.W. Pomeroy, 1999, A distributed model of blowing snow fluxes over complex terrain. Hydrol. Processes, 13, 2423 – 2438.
  14. Liston, G.E., and M. Sturm, 2002, Winter precipitation patterns in arctic Alaska determined from a blowing-snow model and snow-depth observations. J.Hydrometeor., 3, 646 – 659.
  15. Liston, G.E., J.P. McFadden, M. Sturm and R.A. Pielke, 2002, Modelled changes in arctic tundra snow, energy and moisture fluxes due to increased shrubs. Global Change Biology, 8, 17 – 32.
  16. Liu, L., Ma, Y., Menenti, M., Zhang, X. and Ma, W., 2019. Evaluation of WRF modeling in relation to different land surface schemes and initial and boundary conditions: a snow event simulation over the Tibetan plateau. Journal of Geophysical Research: Atmospheres, 124(1), pp.209-226.
  17. Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., & Clough, S. A., 1997, Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlatedā€k model for the longwave. Journal of Geophysical Research: Atmospheres (1984–2012), 102(D14), 16663-16682.
  18. Negah, S., Momenpoor, F., Ghaffarian, P., Farid Mojtahedi, N. and Asadi Oskooiee, E., 2014. 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, 19, pp.113-125.
  19. Noh Y, Cheon WG, Raasch S. 2000, The improvement of the K-profile model for the PBL using LES. In Preprints of the International Workshop of Next Generation NWP Model. Laboratory for Atmospheric Modeling Research: Seoul, South Korea; 65–66.
  20. O’Gorman, P.A., 2014. Contrasting responses of mean and extreme snowfall to climate change. Nature, 512(7515), pp.416-418.
  21. Pomeroy, J.W., T. Brown, G. Kite, D.M. Gray, R.J. Granger and A. Pietroniro, 1998, PBS-SLURP Model. National Hydrology Research Institute Contribution Series No.CS-98003. Report to Saskatchewan Water Corporation, Moose Jaw and the Upper Assiniboine River Basin Study, Environment Canada, Regina, Saskatchewan. 24 pp. plus appendices.
  22. Purves, R.S., J.S. Barton, W.A. Mackaness and D.E. Sugden, 1998, The development of a rule-based spatial model of wind transport and deposition of snow. Ann. Glaciol, 26, 197 – 202
  23. Yu, E., 2013. High-resolution seasonal snowfall simulation over Northeast China. Chinese Science Bulletin, 58(12), pp.1412-1419.