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Volume 8, Issue 3 (10-2018)
Disaster Prev. Manag. Know. 2018, 8(3): 300-310 |
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Nakhaei O, Haji Mohammadi H, Feyzi Koshki F. Investigating the atmospheric synoptic of heavy snowfalls Case study: Hamadan province. Disaster Prev. Manag. Know. 2018; 8 (3) :300-310
URL: http://dpmk.ir/article-1-231-en.html
URL: http://dpmk.ir/article-1-231-en.html
1- Master of desert and arid areas Management, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran
2- Master student of Synoptic Weather, Faculty of Literature, Ferdowsi University of Mashhad, Mashhad, Iran
3- Master student of desert and arid areas Management, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran
2- Master student of Synoptic Weather, Faculty of Literature, Ferdowsi University of Mashhad, Mashhad, Iran
3- Master student of desert and arid areas Management, Faculty of Natural Resources and Environment, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract: (3043 Views)
Backgrounds and objective: Snowfall has a decisive role in supplying water resources, agricultural economics, and outbreak of drought crisis. On the other hand, snowfall during the cold season causes various problems and damages in various sectors such as buildings, utilities, transportation and energy.
Method: In this research, in order to identify the atmospheric patterns associated with heavy snowfall in Hamadan province, four meteorological stations were used in the period of 20 years; after identification of snowy days, with the aim of identifying dominant patterns, hierarchical clustering method was applied to data of geo-potential height of 500 hPa. In the following, one day was selected as a sample and analyzed for a closer look. For this purpose, ground surface pressure data (SLP), omega, geo-potential heights (HGT), Zonal wind (U) and meridian wind (V) at various levels were collected from website of the National Center of Environmental Prediction (NCEP / NCAR), to draw the synoptic maps in GrADS software.
Findings and Results: The results showed that in the upper levels of the atmosphere, four dominant patterns were distinguishable which play a major role in the occurrence of heavy snowfalls. In these patterns, the dominant constituent is deep trough. Considering the representative day maps showed that the performance of the omega and vorticity indexes, the atmosphere's thickness conditions, and jet stream situation is such that atmospheric changes from the ground to the upper level of the atmosphere are completely related and provide the appropriate conditions for heavy snowfall incredible and heavy snowfall.
Method: In this research, in order to identify the atmospheric patterns associated with heavy snowfall in Hamadan province, four meteorological stations were used in the period of 20 years; after identification of snowy days, with the aim of identifying dominant patterns, hierarchical clustering method was applied to data of geo-potential height of 500 hPa. In the following, one day was selected as a sample and analyzed for a closer look. For this purpose, ground surface pressure data (SLP), omega, geo-potential heights (HGT), Zonal wind (U) and meridian wind (V) at various levels were collected from website of the National Center of Environmental Prediction (NCEP / NCAR), to draw the synoptic maps in GrADS software.
Findings and Results: The results showed that in the upper levels of the atmosphere, four dominant patterns were distinguishable which play a major role in the occurrence of heavy snowfalls. In these patterns, the dominant constituent is deep trough. Considering the representative day maps showed that the performance of the omega and vorticity indexes, the atmosphere's thickness conditions, and jet stream situation is such that atmospheric changes from the ground to the upper level of the atmosphere are completely related and provide the appropriate conditions for heavy snowfall incredible and heavy snowfall.
Type of Study: Research |
Subject:
Special
Received: 2018/04/9 | Accepted: 2018/05/31 | ePublished: 2018/08/26
Received: 2018/04/9 | Accepted: 2018/05/31 | ePublished: 2018/08/26
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