As the Weather Surveillance Radar 1988 Doppler network reaches the end of its expected life, a network of multifunction phased-array radars (MPAR) supporting both aircraft and weather surveillance missions has been proposed. A phased-array system should match the sensitivity, spatial resolution, and data quality of the WSR-88D while having a update time of 60 seconds for weather surveillance. Since an MPAR system must complete both weather and aircraft surveillance missions, the update time reduction provided by having multiple faces is insufficient to achieve the desired 60 second update time for weather surveillance. Therefore, it is likely that multiple simultaneous beams would be needed per face to meet the timeline requirements. An approach to achieve multiple receive beams is to use a spoiled transmit beam and to form a cluster of simultaneous receive beams. However, a significant challenge for this approach is the potential of high sidelobe levels in the two-way radiation pattern, which can result in significantly biased estimates of the radar variables in situations where the signal power has large spatial variation. This dissertation proposes an adaptive beamspace algorithm designed for phased-array weather radar that utilizes a spoiled transmit beam and a cluster of simultaneous receive beams to achieve the desired timeline. Taking advantage of the adaptive algorithm's ability to automatically adjust sidelobe levels to match the scene, the high-sidelobe problem associated with a spoiled transmit beam is mitigated. Through extensive simulations, it is shown that adaptive beamspace processing can produce accurate and calibrated estimates of weather radar variables. Furthermore, it is demonstrated that the adaptive beamspace algorithm can automatically reject interference signals and reduce their impact on the radar-variable estimates. Additionally, it is shown that, despite higher sidelobe levels, the adaptive beamspace algorithm can perform similarly to a conventional system based on a dish antenna in terms of biases when reflectivity gradients are present. Finally, the adaptive beamspace algorithm is shown to compare favorably to some alternative solutions that can also achieve the desired MPAR timeline requirement while preserving data quality
