Spectral dispersion of cloud droplet size distributions and radar threshold reflectivity for drizzle

In order to meet the urgent requirement for accurate retrieval of liquid cloud microphysical properties, integrating the detecting advantages of active and passive sensors and combining radar reflectivity and optical depth information from CloudSat and Aqua, a new retrieval algorithm of liquid cloud microphysical parameters is proposed according to the optimal estimation theory. By assuming the lognormal size distribution of cloud droplets and establishing functional relationships between measurement and retrieval variables based on forward physical model, with the prior information about spectral distribution parameters, the optimal solutions of spectral parameters are obtained after iteratively calculating, then the microphysical parameters of liquid cloud could be retrieved based on forward physical model, and the uncertainty can be calculated according to error propagation theory. By designing retrieval scheme and using measured case data, the retrieval results are compared with the data published by CloudSat official institutions and those retrieved using empirical algorithms, showing that retrievals of liqiud cloud microphysical parameters based on optimal estimation theory by using combined active and passive sensor data are well consistent with official released data, which makes up for the disadvantages of empirical algorithms that have large error and poor expansibility and gives some important references for retrieval research of liquid cloud microphysical parameters based on domestic spaceborne and airborne W-band millimeter-wave radar data.

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Recognition of hail and rainstorm based on the radar reflectivity image features

Zhi-Ying¹,

Liu²,

Jia³

et al. 2014

Acta Phys. Sin.

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Based on radar reflectivity image features, an automatic recognition method is proposed to identify the hail and rainstorm. We extract the image features of hail echo areas and rainstorm echo areas from radar reflectivity images. By analyzing both the differences in single feature between hail and rainstorm and the classified complementarity among different features, we determine the effective image features, including the intensity and texture features, to identify the hail and rainstorm. The hail and rainstorm objective recognition model can be established through the data mining of the extracted sample features and sounding data by using the Rough Set Theory. Through the test and identification of the 362 test samples, the hit ratio of hail reaches 93.29% and the hit ratio of rainstorm reaches 89.07%. The false alarm ratios of hail and rainstorms can be also at a low level. Compared with those from the PUP system, the experimental results from the present system show that it has a good effect to identify and classify hail and rainstorm by using the radar reflectivity image features.

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Spectral dispersion of cloud droplet size distributions and radar threshold reflectivity for drizzle

Cited by 3 publications

References 15 publications

Analytical studies of the cloud droplet spectral dispersion influence on the first indirect aerosol effect

Analytical studies of the cloud droplet spectral dispersion influence on the first indirect aerosol effect

Retrieval of liquid cloud microphysical properties from spaceborne active and passive sensor data based on optimal estimation theory

Recognition of hail and rainstorm based on the radar reflectivity image features

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