This thesis project is centered around the retrieval of meteorological parameters using a fast-scanning phased array radar, specifically targeting precipitation-like objects such as raindrops. The main objective is to transform radar data into variables that accurately characterize precipitation. This endeavor involves addressing various challenges, including extracting meteorological object-related variables, mapping targets from noisy radar measurements affected by phase noise, and resolving the issue of Doppler aliasing.
In this report, these challenges are tackled by introducing a range of techniques and algorithms designed to enhance radar data analysis and validate the proposed methods. The most commonly derived radar parameters for meteorological targets, known as Doppler moments, are extensively discussed, including reflectivity, mean Doppler velocity and Doppler spectrum width. An exploration of the circular calculation of Doppler spectra moments is conducted, providing valuable insights into the velocity distribution of radar targets. By analyzing higher-order moments, the characteristics and dynamics of the targets can be better understood, leading to improved target identification and tracking. Additionally, a novel circular variance-based target mapping technique is proposed to map targets from noisy radar measurements effectively. This approach proves particularly well-suited for scenarios where traditional reflectivity-based methods fall short. Apart from the circular variance-based technique, this thesis explores reflectivity-based target mapping approaches that offer enhanced methods for identifying and classifying different target parts. Two pipelines are proposed: the morphology-based pipeline and the entropy-based pipeline. Through simulation and real-world data analysis, the pros and cons of each pipeline are carefully evaluated. The second pipeline demonstrates significant benefits in classification, allowing for a more detailed and accurate representation of radar returns. It effectively distinguishes point targets, extended targets of interest, global noise, and phase noise, enabling a more comprehensive analysis of radar data and enhancing the interpretation of detected targets.
Furthermore, the critical issue of Doppler dealiasing is thoroughly investigated, addressing the problem of velocity ambiguity caused by the Nyquist limit. Three approaches are compared: gradient-based, existing advanced technique UNRAVEL, and optimizer-based. Based on the evaluation of the simulation model, the "ParticleSwarm" optimization approach is selected as the most effective in enhancing velocity measurements in severe weather conditions.
Throughout this report, experimental results are presented, findings are discussed, and recommendations and suggestions for future research are provided. The proposed techniques and algorithms have undergone rigorous evaluation and validation using both simulated data and real-world radar measurements. Overall, this research contributes
valuable insights into radar data processing for meteorological applications, offering improved accuracy and reliability for various weather-related analyses and forecasting tasks.