A Velocity Dealiasing Scheme for C-band Weather Radar Systems

来源 :Advances in Atmospheric Sciences | 被引量 : 0次 | 上传用户:xiaofyk
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A dealiasing algorithm for radar radial velocity observed by C-band Doppler radars is presented as an extension of an existing S-band dealiasing algorithm. This has operational significance in that many portable and many commercial broadcast radars, as well as approximately one half of the Chinese weather radar network(CINRAD), are C-band radars. With a wavelength of about 5 cm, the Nyquist interval of C-band radars is just about one half that of S-band radars(wavelength of about 10 cm) and thus has more velocity folding. The proposed algorithm includes seven modules to remove noisy data, find the starting radials, dealias velocities, and apply least squares error checking in both the radial and azimuth directions. The proposed velocity dealiasing method was applied to one widespread rain case and three strong convective cases from radars operating in China. It was found that, on average, 92.95% of the aliased radial velocity data could be correctly de-aliased by the algorithm, resulting in 96.65% of the data being valid. A dealiasing algorithm for radar radial velocity observed by C-band Doppler radars is presented as an extension of an existing S-band dealiasing algorithm. This has operational significance in that many portable and many commercial broadcast radars, as well as approximately one half of the With a wavelength of about 5 cm, the Nyquist interval of C-band radars is just about one half that of S-band radars (wavelength of about 10 cm) and thus thus has more velocity folding. The proposed algorithm includes seven modules to remove noisy data, find the starting radials, dealias velocities, and apply least squares error checking in both the radial and azimuth directions. The proposed velocity dealiasing method was applied to one widespread rain case and three strong convective cases from radars operating in China. It was found that, on average, 92.95% of the aliased radial velocity data could be correctly de-aliased by the algorithm, resulti ng in 96.65% of the data being valid.
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