The sensor suite for assisted and automated driving functions vehicle is critical to the function of a vehicle, but also the first and most important limitation to the level of automation that the system can achieve. The advancement of 4D RADARs, providing better resolution in both azimuth and elevation compared to traditional RADAR, can assist to achieve more robust situational awareness, whilst also providing more data for perception algorithms and sensor fusion. However, like all perception sensors, 4D RADAR is also affected by numerous noise factors. To explore the sources of noise, this work identifies, classifies, and analyses automotive 4D RADAR noise factors. Overall, 22 noise factors have been considered, in combination with their effect on six 4D RADAR outputs. Finally, this work also presents and applies, for the first time, a dissimilarity metric to collected 4D RADAR data in the presence of rain with different intensities. The proposed metric is used to assess the effect of noise on the variability of the measured data, in addition it can be used to compare any 4D RADAR data. The metric, combined with other pointcloud evaluations, shows that as rainrate intensifies, the size of the pointcloud decreases, but also the variation in the measurements increases. This work presents the importance of evaluating, companding, and quantifying noise for 4D RADAR, and can pave the way for more in depth analysis of its modelling and testing of 4D RADAR for assisted and automated driving functions.