LiDAR sensors are now a main component in perception systems of vehicles. With the advancements in technology of ADAS and autonomous vehicle systems that rely on LiDAR sensors, it is necessary to thoroughly test and validate these systems before deployment into the real world. Among the method is via the use simulation that allows the system to traverse millions, if not billions, of kilometres with thorough testing and validation including scenarios that cover a larger scope of situations and cases. However, despite the accuracy of LiDAR for mapping the environment and estimating precise distances, it is known to be affected by adverse weather conditions, thus reducing the operational design domain of vehicles equipped with this type of sensor. This paper aims to introduce a methodology of acquiring real world data, and through data analysing and processing be able to create and improve the current LiDAR models in simulation by implementing the effects of perturbation, whether it be hardware or weather caused.