Retrieval of water cloud microphysical properties with polarized LiDAR based on optimization method: a polarimetric Monte Carlo simulation

Sensing the microphysical properties of water clouds using LiDAR relies on two key parameters: the cloud droplet effective radius and extinction coefficient. Retrieving these two parameters from LiDAR data is challenging due to the ill-posed nature of the LiDAR equation, which often requires assumptions like the LiDAR ratio (extinction-to-backscatter ratio). While various techniques have been developed to bypass these limitations, this study proposes what we believe to be a novel optimization approach as a robust alternative for retrieving water cloud microphysical properties near the cloud bottom (100-300 meters) from LiDAR data. Based on polarimetric Monte Carlo simulations with a pre-computed look-up table under multiple-scattering conditions, a grid search optimization method was applied on homogeneous and sub-adiabatic water cloud models to find the optimal solution. Result showed that the global minimum of the cost function applied to the look-up table is affiliated with the inversion result of the LiDAR equation. Error analysis, supported by statistical metrics, demonstrated the accuracy of the optimization method, with regression results closely matching the 1:1 line. This approach offers a robust alternative for retrieving the water cloud microphysical properties, enhancing the capability of LiDAR-based atmospheric research.