Effect of Scattered Solar Radiation on the Informativeness of Polarization Lidar Studies of High-Level Clouds

During daylight laser polarization sensing of high-level clouds (HLCs), the lidar receiving system generates a signal caused by not only backscattered laser radiation, but also scattered solar radiation, the intensity and polarization of which depends on the Sun’s location. If a cloud contains spatially oriented ice particles, then it becomes anisotropic, that is, the coefficients of directional light scattering of such a cloud depend on the Sun’s zenith and azimuth angles. In this work, the possibility of using the effect of anisotropic scattering of solar radiation on the predictive ability of machine learning algorithms in solving the problem of predicting the HLC backscattering phase matrix (BSPM) was evaluated. The hypothesis that solar radiation scattered on HLCs has no effect on the BSPM elements of such clouds determined with a polarization lidar was tested. The operation of two algorithms for predicting the BSPM elements is evaluated. To train the first one, meteorological data were used as input parameters; for the second algorithm, the azimuthal and zenith angles of the Sun's position were added to the meteorological parameters. It is shown that there is no significant improvement in the predictive ability of the algorithm.