从单偏振雷达数据中联合反演风速、雨滴下落速度和直径，提升降水参数精度。

The inverse problem of precipitation retrieval from Doppler power spectral density measurements of a fast-scanning single-polarization X-band weather radar is addressed. The formulation is developed around quantities that remain interpretable and stable across short coherent processing intervals: radial-wind mean, radial-wind spectral width, reflectivity-weighted terminal fall velocity, and median volume diameter. The gamma drop-size distribution parameters are retained as latent variables within the spectral forward model rather than reported as primary retrieval products. This choice is motivated by likelihood analyses showing that the inverse problem is weakly identifiable with respect to the latent distribution parameters, whereas the derived quantities remain substantially more stable. Simulated-data experiments compare the proposed approach with a moment-based retrieval using externally supplied wind information in the short-coherent-processing-interval regime. Real-data experiments demonstrate same-scan spatial likelihood pooling through a one-scan plan-position-indicator retrieval and 33-scan time-series comparisons against two disdrometers within the radar coverage region. The results show that the latent distribution parameters should be treated as intermediate fitting variables rather than primary end products. The proposed approach shows its clearest practical benefit for median volume diameter and reflectivity-weighted terminal fall velocity under incoherent short-record operation. Spatial pooling allows efficient full-scan mapping of both quantities when each Doppler spectrum contains only a few slow-time samples. Radar-disdrometer discrepancies remain physically meaningful even when the spectral fit and temporal evolution are reasonable.