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Precipitation Phase Transformation Mechanism and Prediction Technology of Snow Depth
WANG Yijie, ZHAO Guixiang, MA Yanzhi
Journal of Arid Meteorology    2019, 37 (6): 964-971.  
Abstract326)      PDF(pc) (2007KB)(1370)       Save
Based on the surface and radiosonde observation data, Doppler weather radar data of Shanxi Province from 2014 to 2017 and reanalysis data of NCEP/NCAR, the rain-to-snow transformation processes were classified and analyzed in winter half year in Shanxi Province, and the effect of surface air temperature on transformation of precipitation phases was discussed, then the precursor information of precipitation phase transition was extracted. The relationship between snowfall and snow depth increment for snowfall processes was statistically analyzed, and the prediction indicators of snow depth were refined. Finally, two rain-to-snow processes from 23 to 24 November 2015 and 21 to 22 November 2016 were analyzed comparatively in order to reveal physical mechanism of precipitation phase transition. The results are as follows: (1) The rain-to-snow transformation process was the most in November in Shanxi Province, and followed by February. As an indicator of precipitation phase change, the surface air temperature was related to climatic and weather (such as the intensity and moving path of cold air) characteristics, geographic location, etc. (2) The ratio of snow depth increment and snowfall was about 0.68 cm·mm-1 in winter half year in Shanxi, and it increased with the decrease of temperature, so it had obvious temporal and spatial differences with the change of month, latitude and altitude. (3) During the change of rain to snow, the proportion of snow-ice layer thickness to total cloud layer increased with the cooling in lower layer of troposphere and the falling path of solid condensate in cloud shortened, which reduced the melting probability of solid condensate, and resulting in the change of phases.
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Numerical Simulation and Diagnostic Analysis About a Heavy Rainstorm Process in North China Based on WRF Model
MA Yanzhi, ZHAO Guixiang, HAO Jingyu,YAN Hui, ZHANG Lei
Journal of Arid Meteorology    DOI: 10.11755/j.issn.1006-7639(2016)-03-0511