To characterize atmospheric turbulence, the Doppler moments are estimated by weather radars. However, moment accuracy is highly sensitive to radar transmission parameters such as pulse repetition time (T_s) and number of pulses (N_p), which affect Doppler ambiguity and estimation variance. Traditional fixed-parameter radars face trade-offs between aliasing and measurement precision. This paper proposes an adaptive radar framework that dynamically adjusts T_s and N_p on a per-scan basis to improve Doppler moment estimation at a single resolution cell level. Inspired by the Fully Adaptive Radar (FAR) concept, the method also includes a novel multi-lag Doppler width estimation scheme. Results demonstrate enhanced estimation accuracy, enabling better responsiveness to localized and non-stationary weather conditions.

The problem of enabling adaptive capabilities in the context of weather radar is considered in this paper. Inspired by the cognitive radar framework, an approach based on Reinforcement Learning (RL) is formulated to deal with the monitoring of multiple storm cells moving near a potential area of interest. The approach aims to dynamically adjust the radar waveform bandwidth, and consequently maximum measurable range and range resolution, in order to provide the best monitoring based on a purposely-defined reward function. The approach is successfully validated with a simulator developed in Python & StoneSoup. Results demonstrate that the proposed method outperforms traditional fixed-scan ('sit and spin') strategies commonly used in weather radar operations.