Cloud radar observations and spectral polarimetry are crucial for understanding cloud microphysics. The overall purpose of this study is two-fold: (a) to describe the methodology for simulating polarimetric spectral variables including white and stochastic noise of a real radar spectrum, as well as the impact of atmospheric turbulence and (b) to compare simulations with observed spectra for rain observations. Rain electromagnetic scattering properties have been historically computed by assuming spheroidal shapes via the T-matrix method (Mishchenko et al., 2000). Such models have been found satisfactory to explain radar and radiometeric measurements. However, raindrops generally change due to oscillations, which cause departure from rotationally symmetric shape, and make T-matrix tools impractical since they hinge upon the assumption of rotationally symmetric particles. This work focuses on generating simulations of a 94 GHz cloud radar observations in rain conditions, pointing at 45 degrees and comparing with real observations. The spectral differential reflectivity (sZDR) and spectral differential phase (sδhv) are the variables of interest. They are produced with the T-matrix method, by computing the electromagnetic scattering properties and simulating the radar response. The simulation tool is described in section 2 and explores diverse conditions, allowing for the modification of rain rate, white and spectral noise, and turbulence parameters. The effect of atmospheric turbulence introduces an increased spread of velocities within the radar volume and contributes to the blurring of the spectral features, such as smearing out the distinct features (Mie scattering notches) in the Doppler spectrum. Incorporating the impact of turbulence in the simulations for spectral polarimetric variables is a complex task and the attempt is discussed in this study.