High-precision detection of the optical and microphysical properties of water clouds is essential for understanding climate change processes. Effective retrieval of the extinction coefficient and effective radius of water clouds can be achieved by utilizing the multiple scattering effect in water cloud signals detected by lidar. In this work, two water cloud retrieval methods based on polarized Mie-scattering lidar (ML) and dual-field-of-view high spectral resolution lidar (HSRL), respectively, are introduced. The performances of these methods are compared through the retrieval results from four representative water cloud cases. The results indicate that while both methods exhibit comparable retrieval accuracies for water cloud extinction coefficients, the dual-field-of-view HSRL method demonstrates superior performance in retrieving the effective radius retrieval. Enhancing the retrieval accuracy of the polarized ML method is possible by increasing the resolution of the lookup table points, though this comes at the cost of some algorithmic efficiency. Due to the weaker signal intensity at the HSRL molecular channel, the retrieval stability of the dual-field-of-view HSRL method is more sensitive to the signal noise from the molecular channel. The evaluation presented in our study provides an important reference for the future development of instruments and algorithms for observing water clouds based on lidar.

This paper presents and compares two water cloud retrieval methods based on polarized ML and dual-field-of-view HSRL, respectively. The modified gamma distribution is adopted to parameterize the droplet size distribution of water clouds, while the adiabatic model is used to characterize the vertical distribution of water cloud properties. The Monte Carlo model and analytical model simulate multiple scattering lidar signals from water clouds during the retrieval process. Lastly, a detailed description of the polarized ML method and the dual-field-of-view HSRL method is provided, along with their respective flowcharts illustrated in Figs. 1 and 2.

A series of Monte Carlo simulations involving various water clouds is conducted to investigate the multiple scattering effect on the depolarization ratio of signals and signal variations at different field-of-views (Fig. 3). Subsequently, four representative water cloud cases are defined, and their signals are simulated using the Monte Carlo model as input for two retrieval methods (Fig. 4). The retrieved values of water cloud properties (extinction coefficient and effective radius) at a reference height by polarized ML method are illustrated in Fig. 5. For the dual-field-of-view HSRL method, the dual-field-of-view molecular signals reconstructed by retrieved water cloud properties are compared with the input signals (Fig. 6). Comparing the water cloud properties retrieved from the two methods to the true input values is depicted in Fig. 7. The results reveal that both methods accurately retrieve the extinction coefficient, while the dual-field-of-view HSRL method showing higher retrieval accuracy for the effective radius.

We introduce the fundamental principles of two water cloud retrieval methods based on polarized ML and dual-field-of-view HSRL. The methods utilize signals simulated by the Monte Carlo model as the input for retrieval, and the accuracy of their retrieval results is compared. The findings demonstrate that both methods accurately retrieve the extinction coefficient of water clouds. However, the polarized ML method encounters limitations in retrieving the effective radius due to the point resolution of the lookup table, resulting in a larger retrieval error. In contrast, the dual-field-of-view HSRL method, unrestricted by this limitation, achieves higher retrieval accuracy. Specifically, the root-mean-square error of the retrieved effective radius in the HSRL method is approximately 22% to 89% of that obtained by the polarized ML method. The lookup table-based polarized ML method is constrained by the computational speed of the Monte Carlo model, necessitating a reduction in the point number of the lookup table (100×100 in our study) to enhance algorithm efficiency. On the other hand, the dual-field-of-view HSRL method faces challenges with weaker molecular channel signals compared to the ML signals, leading to increased susceptibility to signal noise and greater fluctuations in retrieval results, especially at lower signal-to-noise ratios near cloud tops. Overall, while the dual-field-of-view HSRL method offers higher accuracy in retrieving water cloud properties without lookup table resolution constraints, the higher signal intensity of polarized ML signals ensures more stable retrievals in the presence of larger signal noise. Future research could enhance the retrieval performance of both the polarized ML and dual-field-of-view HSRL methods by improving the lookup table resolution or the signal-to-noise ratio, respectively, to advance lidar-based water cloud research.