A self-calibrating retrieval algorithm for three-phase water Raman lidar incorporating polarization and temperature-dependent spectral characteristics

The phase distribution of water within clouds governs their macro- and micro-physical structure. This paper presents a three-phase water Raman lidar system developed for simultaneous measurement of water vapor, liquid water, and ice, and proposes a novel retrieval algorithm. By utilizing the characteristic Raman spectra of three-phase water and employing ultra-narrowband interference filters, the system separates backscatter signals at different wavelengths. The significant spectral overlap among the three phases prevents complete physical separation of their signals, and the temperature-dependent variation of liquid water Raman spectra further complicates this separation. We propose a self-calibrating three-phase water retrieval algorithm that incorporates temperature and polarization profiles measured by a single-line-extracted Raman lidar and an elastic polarization lidar. This method calculates overlap factors using temperature-dependent Raman spectra of three-phase water and analyzes the signal composition in each channel under various meteorological conditions to accurately retrieve the distribution of three-phase water. Preliminary measurements successfully obtained three-phase water profiles with spatiotemporal resolutions of 1 h and 90 m, respectively. Coordinated observations with single-line-extracted branch Raman lidar and polarization lidar systems are conducted to verify the algorithm's performance. Continuous measurements under cloudy conditions precisely captured phase transition processes in both water and ice clouds, enabling detailed measurements of the small-scale variations in three-phase water distribution within clouds and demonstrating the system's capability for advanced cloud microphysical research.