Analysis of snow microphysical process from Doppler spectra of the Ka-band millimeter-wave cloud radar
Received:July 02, 2018  Revised:March 30, 2019  download
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Author NameAffiliationE-mail
LI Yu-lian national university of defense technology/Unit 
SUN Xue-jin national university of defense technology 
Abstract:The identification of supercooled water droplets in the cloud is of great significance for the physical process of cloud-precipitation and warning aircraft icing. In this paper, the spectral peak recognition algorithm is established by the U.S. ARM-AMF2 spectral data of the 35GHz cloud radar in Finland. The spectral separation of the total spectrum is obtained, and then the supercooled water droplets are identified. Next the supercooled water droplets are identified. Next, the reflectivity, doppler velocity and spectral width of different types of particles are calculated by spectral moment. Finally, the liquid water content in the cloud is retrieved from the empirical relationship and compared with the detection results of the microwave radiometer. The results are as follows:(1)The radar reflectivity factor of mixed-phase clouds mainly depends on snow. Therefore, it is considered that the effective volume of radar is snow. The cloud liquid water content will be underestimated according to the total reflectivity;(2) The gradient of Doppler velocity(V) of ice and snow particles in the supercooled water layer(SWL) larger than that in the ice and snow layer(ISL);(3) The liquid path(LWP) obtained by the Doppler spectrum is good agreement with the microwave radiometer. It shows that the millimeter wave radar can effectively estimate the liquid water path in the cloud.
keywords:millimeter-wave radar  Doppler velocity spectrum  Supercooled water  Microwave radiometer  Liquid water path
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