Based on AIRS remote sensing datasets of air temperature and total column water vapor and the method of HYSPLIT backward trajectory analysis, the sources and transporting paths of various meteorological elements of two heavy snow processes in Urumqi during 10-12 December 2015 and 2-3 March 2016 were monitored and analyzed, which including cold air, warm air and water vapor, the differences between conditions and intensities of snowfall in winter and spring were also studied. The results show that:(1) Snowstorm in northern Xinjiang is an outcome of interaction of cold air, warm air and water vapor. (2) During two snowstorm processes, the transfer of water vapor has five sources of the Black Sea, the Caspian Sea, the Aral Sea, the Balkhash Lake and the Arabia Sea, the cold air mainly comes from the northern Europe, the Norwegian Sea, the Barents Sea and the Iranian Plateau, the sources and transporting paths of warm air and water vapor are generally same or closely related. (3) The snowstorm during 10-12 December 2015 is a record strong snowstorm process, the cold air of this process mainly comes from the northern Europe, the Norwegian Sea and the Iranian Plateau, the warm air and water vapor both come from the Black Sea and the Caspian Sea. The transportation of water vapor from the Arabia Sea is an important cause of torrential storm. (4) The snowfall during 2-3 March 2016 is a weak snowfall process in spring, the cold air of this process mainly comes from the regions of Nordic and West Siberia, the warm air mass and water vapor comes from the Northern Xinjiang and the Balkhash Lake respectively. Due to the lack of continuous water vapor supplement, the snowfall is short and weak. (5) By comparing the two different snowfall processes, the abundant and continuous water vapor is a main factor that determines the duration and intensity of snowfall.
LIU Song
,
HUANG Fuxiang
,
YANG Lianmei
,
HAN Shuangshuang
. Monitoring of Conditions for Heavy Snowstorm in Northern Xinjiang Using Satellite Remote Sensing Data[J]. Plateau Meteorology, 2018
, 37(4)
: 994
-1001
.
DOI: 10.7522/j.issn.1000-0534.2018.00055
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