Studies On The Velocity Dealiasing, Wind Retrieval And Nowcasting Of The Doppler Radar Data

Aiming at the quality control and problems that need to be solved in practical application, using the Doppler weather radar data in current operational application, this article makes studies on the velocity dealiasing, wind retrieval and nowcasting of Doppler weather radar data.1. To solve the problems that velocity dealiasing of Doppler weather radar data usually has mistakes in current operational application, a new automated velocity dealiasing method based on zero isodop searching has been developed. Zero isodops are searched point by point from the radar origin to the maximum detection range, and the accepted sign of each region separated by the zero isodops is determined. After that, the velocity sign at each gate is compared with the accepted sign of the region wherein the gate is. If they are consistent, the velocity is considered as true; otherwise as aliased and then dealiased to the true value. Dealiasing results on real cases indicate that the new algorithm is practicable and effective, especially on aliasing lack of references affected by discontinuous echo or range folding. It improves the velocity dealiasing problems, mistakes by using traditional linear extrapolation methods, in thoughts and methods.2. To solve the ill-conditioned matrix appeared in VVP (Velocity Volume Processing) wind retrieval, an improved VVP method, SVVP (step VVP) method, is proposed. SVVP method retrieves components of the wind field through a step wise procedure, which reduces the conditional number of equations and overcome the ill-conditioned matrix problems which currently limit the application of the VVP method. Variables can be retrieved even if the analysis volume is very small. In addition, the source and order of errors are analyzed in the retrieval. The improved method is applied to real cases including convective storms and typhoons, which show that it is robust and relative capability to obtain the wind field structure of the meso-scale convective system.3. To solve the storm identification and tracking problems in nowcasting by Doppler weather radar data, a new storm identification and warning technique is proposed, and modern optimization algorithms are used in storm tracking. The new identification method assembles contiguous storm points to constitute 2D storm components and improves the vertical association of storm components to construct 3D storms, which overcomes the deficiencies existing in traditional identification methods. Modern optimization algorithms (simulated annealing algorithm, genetic algorithm and ant colony algorithm) are tested to match and track storms. Experiment results and theoretical analysis show that simulated annealing algorithm and ant colony algorithm are effective and have intuitionally adjustable parameters, whereas results from genetic algorithm is unsatisfactory for the constraint of genetic operations mode. Based on the evolution properties and characteristic distributions, storms are specified as strong storms and general storms which then can be discriminated by a support vector machine (SVM). The performance of the SVM shows that it can indicate the development and evolution of a storm, providing an important aid in severe weather warning.