Calibration of Adaptive Digital Beamforming for Polarimetric Phased Array Weather Radars

This study investigates the implementation of adaptive digital beamforming (DBF) for polarimetric phased array radar (PAR), focusing on the calibration challenges and performance benefits for weather observations. Traditional Fourier-based beamforming methods, while widely used, suffer from fixed and limited spatial resolution and susceptibility to contamination, particularly in the presence of strong reflectivity gradients or clutter. On the other hand, adaptive DBF using the Capon method offers improved data quality by minimizing contamination but requires careful calibration, particularly for maintaining the integrity of polarimetric measurements. This work presents a framework that includes beam pattern calibration and noise estimation to enable accurate polarimetric variable estimation. Simulation results demonstrate the advantages of the Capon method over Fourier processing in terms of resolution and clutter mitigation, while NEXRAD data serve as a basis for more realistic simulations highlighting the method’s practical applicability. These findings provide a critical step toward the application of adaptive DBF in polarimetric phased array weather radar.