ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 40 Issue 5 > 2014,40(5):612-618. DOI:10.7519/j.issn.1000-0526.2014.05.012 Prev Next
Droplet Spectrum Characteristics and Depth Forecast Method of Two Snowstorms in Northwest of Hubei
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Abstract:
The droplet spectrum variation characteristics and snow depth forecast of the two snowstorms in 14-15 December 2010 and 20-22 January 2012 are studied by using Thies Clima laser precipitation monitor (TCLPM) and artificial observation data from Danjiangkou Station. The results show that: (1) the TCLPM can automatically identify the precipitation phase state. Combined with surface artificial dense observations, the temperature higher than 0.7℃ is for precipitation phase state, less than 0.7℃ is for sleet, below -0.5℃ is for pure snow. At the same time, when the surface temperature is below 0.5℃, snow begins to accumulate. The surface wind speeds in the two processes are relatively slow, conducive to snow accumulation on the ground; (2) the TCLPM can monitor the variation of droplet spectrum characteristics of heavy snowfall weather, the echo intensity (Z), average diameter (Dm), water content (W), snowfall particle number concentration (N) which increases with the enhancement of snowfall intensity. During the two snowstorm events, there are different levels of positive correlations among Dm, Z, N, W and VSD, better correlation between W and VSD, respectively reaching 0.844 and 0.926; (3) Selecting the first order fitting snow particles in water content W and surface snow rate, the snow rate forecast equation is derived compare surface snow rate forecast (VSDF) in the pure snow stage and the surface snow depth forecasts (SDF) to VSD, SD retrieved with the observation data by TCLPM it is found that they are very close, indicating this method can be used to estimate VSD and SD on the surface snow rate and snow depth, which can reflect the main time period with quick increase of surface snow.
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