كليدواژه :
رويكرد محيطي به گردشي , تحليل خوشهاي , پرفشار اروپايي , ناوه مديترانه , تاوه قطبي , ريزش برف , كرمانشاه
چكيده لاتين :
Introduction
In this Research, the main synoptic patterns affecting snow with an environment-tocirculation
approach in Kermanshah station in the West of Iran have been studied. To
this end, the daily data of the snow station from January 1951 to December 2004
were collected from Iran Meteorological Organization. Also, to identify patterns of
climate, sea level pressure, and temperature data, daily averages of temperature and
pressure set of 500 HP data daily mean the reanalysis database by the United States
Environmental Forecasting Center on the same date was collected, clustered, and
categorized to analyze the temperature and pressure data on atmospheric patterns.
Then, by analyzing the maps of all days the number of clusters it was determined that
three of them justified the snowfall patterns in the region in the best way. Finally, to
identify the most important systems, the combined patterns of sea level pressure and
temperature and height and temperature level of 500 of each cluster were drawn
and analyzed, forming the basis of this research. The results showed that snow in the
west of Iran is affected by different systems near and far in the area, that is 1-
formation and strengthening the European high pressure in northwestern of Iran 2-
area located on the Mediterranean trough 3- the spread of Polar Vortex of the
Southern latitude 4- Strengthening the Siberian High and the Tibetan in the Northeast
and East of Iran 5- spread and penetration of low pressure Sudan to the west of Iran.
Consequently, the snowfall of these patterns can be classified in three clusters.1- low
snowfall 2- moderate snowfall 3- heavy Snow.When these systems are less severe
and extended, low and sporadic snowfall; when strengthened, moderate snowfall;
and when they are at the peak of their activities, heavy snow in this area is witnessed.
Materials and Methods
The foundation of this research was to recognize all the snow creator patterns in the
study area during the focused statistical duration. In order to know the along systems
with snowy days one conditions were considered; during snowy days the minimum
snow fall in the study station be 1 em. based on this, 188 snowy days in the study
station had the considered condition. In the second step, after recognizing the days
with snow fall, the average daily data of pressure and the temperature of the sea
level, and height and the temperature of 500 hp level from the range of reanalyzed
NCEP/NCAR data on same days in the area of 0 to 80 northern latitude degree and 0
to 100 longitude east were extracted by programming in the Grads software
environment (from the sea level temperature and the surface 500 hp temperature
were used separately in order to recognize the temperature properties of the creator
patterns of snow fall in the region). In third level, cluster analyze was used in Matlab
software on pressure and temperature data. In addition, in order to calculate the
number of the clusters the internal and external methods were used, and by
analyzing the maps of all days, three clusters were determined (figure 1). Eventually,
to identify the most important systems, the combination system of the sea level
pressure by temperature and also the combination pattern of the 500 hp surface
height with temperature were designed using the Surfer software, and based the
analyzes of this research.
Discussion
It was shown by analyzing the data that 3 clusters could identify the snow creator
patterns of the region by the best way which are including: 1. The first cluster; this
cluster by 87.2 is the most frequent snow cluster, and by 1.8 em has the less snow fall
among the three considered clusters. The study region of this cluster in the sea level
is influenced by Siberian high pressure and the black sea high pressure, and in 500 hp
is influenced by high Mediterranean trough. 2. The second cluster; the average snow
fall in in this cluster is 12.1 em and altogether, include11.2 of all occurrences. In this
cluster, on the sea level, the Black sea high pressure has been stronger over the Iran
north west and the Siberia high pressure tabs has been penetrated more severely to
the north tapes of the country. Furthermore, in 500 hp level the Mediterranean
through has been deeper and has rushed up the north Europe and Scandinavian cold
air on the study region. 3. The third cluster; this cluster is contained three events that
overall include 1.6 of all the events and the highest average snowfall pattern was 37
em during the study duration. In this pattern, the Siberia high pressure, Tibet high
pressure and Europe high pressure are in their higher activity. Polar vortex in the
direction of north east-south west has been caused falling of the polar extreme cold
air to the lower latitudes. It would be deeper in 500 hp Mediterranean through under
the polar vortex influence and the condition has been prepared for the Baroclinic
atmosphere with 500 gm height difference.
Conclusion
Investigating the snowfall in the study region, in all three patterns the systems had
influence on snowfall with different degrees although in each pattern the considered
system has improved in power and distribution which as the result, cause increasing
the snowfall in each pattern compared to the previous pattern. The heaviest
snowfalls in the region have happened at the time that the Europe high pressure is on
its highest power and distribution (table 3 and figure 8) and the Mediterranean
through is close to Iranian borders or it is over the west of Iran parallel to the polar
vortex, and elongated lengthily and falling the cold whether on that would create a
Baroclinic atmosphere condition on the east of the through so that the extremity and
the amount of snowfall would be depend on the deepness of the through (table 3)
and the position of the determined region is related to the through. As the result,
according to table 3, when the same height 5550 gm line has stablished on the study
region, snowfall would happen in that area.
