Cgaracteristics Of Velocity Ambiguity And Dealiasing Algorithms For CUBRAD-SA Doppler Weather Radars

Radial velocity fields from CINRAD weather radars are faced by the problem of velocity ambiguity. Ambiguity which inhibits the application of radar velocity fields will make subjective analysis difficult and make data assimilation and wind field retrieval unreliable. One effective solution is software-based dealiasing algorithm. In this paper, observations from CINRAD-SA operational radars were studied, and in response to the problems in velocity dealiasing (velocity ambiguity characteristics, contaminated errors, continuous noise inhibition ability, and algorithm validation), studies were conducted from four aspects.First, operational radar observations were used to pre-characterize velocity ambiguity, including the occurrence rate of ambiguity, and the inter-station, inter-type, temporal, and spatial distributions of ambiguity. The results showed that:velocity ambiguity was ubiquitous in CINRAD-SA; seashore radars were slightly more ambiguous than inland radars; echoes of weak convections had the highest occurrence rate of ambiguity; ambiguity occurred more frequently in winter half year than in summer half year; ambiguity occurred mainly at an elevation angle of6.0°, azimuth of70°or250°, radial distance of50-60km, or height of5-6km. Knowledge on these characteristics provides a foundation for design and validation of dealiasing algorithms.Second, the types and causes of dealiasing errors were analyzed by using the basic principles of velocity dealiasing. Errors were divided into two types:general error and contamination error. The two error types had different causes and harms. General error and contamination error were caused by missing data and continuous noise respectively. Contamination error showed negative effect on dealiasing algorithms and made data quality improvement unclear after dealiasing, so contamination error is a major problem for the application of dealiasing algorithms. Therefore, an important pathway to improve dealiasing performance is to reduce the impact from continuous noise.Third, based on the above results, a new anti-noise velocity dealiasing algorithm (AND algorithm) was proposed. The primary objectives of AND were to reduce contamination error and to improve algorithm performance by eliminating the impact of "continuous noise". AND was finished in three steps:noise separation, curve dealiasing, and noise restoration; it involved a lossless "separation-restoration" repressing scheme. This scheme could avoid the mistaken deletion of non-noisy data during noise suppression, effectively repress "continuous noise", and avoid the loss of any wind field information. During dealiasing, three fitted curves were weighted to further repress residual noise and dynamically match wind fields at varying scales.Fourth, all ambiguous files from4stations×3years of operational radar data were used to validate the actual performance of AND based on file precision; AND was also directly and indirectly compared with an existing dealiasing algorithm, with illustration by special cases. The results showed that:total dealiasing precision of AND was close to90%; precisions of typhoons and strong convections were>94%; precision of discontinuity stratus was70%. AND (with a dealiasing precision30%higher) was obviously superior over WSR-88D algorithm and greatly reduced the rate of contamination error.