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The microphysical changes in cloud formation and development are closely related to the vertical air motions. It is difficult to simultaneously detect microphysical parameters of drizzle and vertical air motions. This study proposes a method for the drizzle microphysical property and vertical air motions retrieval using Doppler lidar and radar measurements. The wavelength of lidar is 1.55 µm, and it undergoes Mie scattering or geometric scattering in drizzle. The wavelength of radar is 8.6 mm, and it undergoes Rayleigh scattering or Mie scattering in drizzle. The difference in scattering mechanisms of two wavelengths enables them to retrieve the microphysical parameters of vertical air motions and raindrops. This wavelength-dependent backscattering cross section causes differently shaped reflectivity-weighted Doppler velocity spectra leading to wavelength-dependent mean Doppler velocity, and spectra width. In this algorithm, the echo power intensity, mean Doppler velocity and spectra width of lidar and radar are used for the retrieval of microphysical parameters and vertical air motions. The feasibility of the proposed method is simulated and analyzed, which is suitable for stratiform clouds rainfall with low turbulence. Finally, an observation case is provided and analyzed.

Rotational Raman lidar is an important technique for detecting atmospheric temperature. However, in cloud regions with strong elastic scattering conditions, elastic scattering crosstalk (ESC) is prevalent due to insufficient out-of-band suppression of the optical filter, resulting significant deviations in temperature retrieval. To address this challenge, a temperature correction technique for optically-thin clouds based on the backscatter ratio is proposed. Using the least-squares method, a temperature correction function is formulated based on the relationship between the ESC and backscatter ratio of clouds. Subsequently, the backscatter ratio is used to correct the rotational Raman ratio of clouds, thereby obtaining the vertical distribution of atmospheric temperature within the cloud layer. The feasibility of this method was assessed through numerical simulations and experimentally validated using a temperature and aerosol detection lidar at the Xi'an University of Technology (XUT). The results indicate that the difference between the retrieved temperature profile under high signal-to-noise ratio conditions and radiosonde data is less than 1.5 K. This correction technique enables atmospheric temperature measurements under elastic scattering conditions with a backscatter ratio less than 115, advancing research on atmospheric structure and cloud microphysics.