The use of radars for remote sensing in atmospheric sciences has become increasingly popular over the past few decades. Weather radars play a crucial role in measuring, interpreting, and monitoring various atmospheric phenomena. However, accurate retrieval of vertical air velocities remains a challenging problem owing to certain deficiencies in radar data and errors in deriving numerous parameters of precipitation. This research aims to develop data processing and air motion retrieval algorithms that can improve the accuracy of this task. The project proposes a robust data processing pipeline to enhance data reliability. It also conducts the classification of rainfall types and hydrometeor classes in the different regions of atmosphere for more accurate parameter retrieval. In this project criteria for the quality of air-motion retrievals have been developed. The performance of the retrieval algorithms achieved by using exponential drop size distribution (DSD) for fall velocity calculations has been evaluated based on the proposed criteria. This research also utilizes data spanning six months for performance analysis of air velocity retrieval algorithms under various weather conditions over extensive periods of time.