Abstract—In the development of automated driving andadvanced driver assistance systems, it is essential that theunderlying algorithms are thoroughly tested. Simulation isa valuable tool for evaluation of such algorithms, especiallyin safety-critical scenarios. Simulations can also provide theopportunity to generate and repeat rare, yet important, testcases and can be applied at various stages of development.For simulation to serve as a reliable substitute for real-worldtesting, it must offer high fidelity. Therefore, advanced modelsfor both sensors, and the sensed environment are required. Athorough understanding of adverse weather conditions withinthe operational design domain is consequently crucial to maintainfidelity. This work develops statistical models of radarbackscatter from rain for the perception by automotive radarswhich can be implemented in a simulated environment. Todemonstrate the method’s validity, both simulation and realworlddata are used to assess the fidelity of rain radar signalpredicted or generated using the model. The work presentedhere offers a state-of-the-art understanding of how rain (basedon drop size distributions) affects the radar model and thebackground signal to be expected across the radar field of view,including the velocity dimension. With the use of such models insimulation it is possible to anticipate how the radar will react inthe real-world under similar conditions. Future work includesintegrating these findings into a radar-in-the-loop environmentand developing additional adverse weather models for radar.Index Terms—RADAR model, Adverse weather model, Simulation,Measurements, 4D radar, Automated Driving, ADAS.