Particle Spectral Characterization of a Rain-to-Snow Event and Identification Process of DSG5 Precipitation Weather Phenomenometer
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Abstract:
In this paper, the relationship between the data at all levels formed by DSG5 in a rain-snow transition weather process and its identification process of weather phenomena are analyzed. The variation characteristics of the precipitation particle spectrum and meteorological elements such as air temperature, ground temperature and grass surface temperature in the process of rain-snow transition are studied. The ECMWF model data and dual-polarization Doppler radar data are used to explore the weather background of rain-snow transition and the spatial distribution of precipitation particles in different phases. It is found that: Before the start of precipitation, a cold air layer formed at the bottom of the warm air layer below 2 km above the station due to the influence of the backflow cold air, and the snowflakes in the upper layer experienced different degrees of melting after falling into the warm air layer. With the enhancement of the backflow cold air, the cold air layer near the ground gradually became thicker, the temperature became colder, and the warm layer gradually became thinner. The station experienced three main precipitation stages: raindrops, raindrops plus ice particles, and ice particles plus snowflakes. The phase change of precipitation particles over the station was clearly reflected in the spatial distribution of the correlation coefficient of the dual-polarisation Doppler radar. In the process of changing from rain to snow, the scale spectrum of surface precipitation particles obviously widened, and the velocity spectrum obviously narrowed. The raindrop scale time spectrum and velocity time spectrum better reflected the changes in the phase state of precipitation particles. The distribution of parameters such as the correlation coefficient of dual-polarisation Doppler radar and the changes of air temperature, ground temperature, and grass surface temperature assisted DSG5 in judging the precipitation weather phenomenon. In general, the identification results of DSG5 precipitation weather phenomenon were consistent with the analysis results of dual-polarisation Doppler radar and other observation data. To avoid interference, the particle number threshold was adopted by DSG5, which made the start time of precipitation judged by DSG5 lag by 5 minutes, the end time of precipitation advance by 6 minutes, and the total duration of precipitation judged by DSG5 was obviously shorter. According to the continuity of the precipitation process and radar echo, the time periods before and after the precipitation process identified by DSG5 were included in the corresponding weather process, and the identification results of DSG5 were optimised to overcome the problem that the total precipitation time of DSG5 was too short.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.