Perception in adverse weather conditions is one of the most prominent challenges for automated driving features. The sensors used for mid-to-long range perception most impacted by weather (i.e., camera and LiDAR) are susceptible to data degradation, causing potential system failures. This research series aims to better understand sensor data degradation characteristics in real-world, dynamic environmental conditions, focusing on adverse weather. To achieve this, a dataset containing LiDAR (Velodyne VLP-16) and camera (Mako G-507) data was gathered under static scenarios using a single vehicle target to quantify the sensor detection performance. The relative position between the sensors and the target vehicle varied longitudinally and laterally. The longitudinal position was varied from 10m to 175m at 25m increments and the lateral position was adjusted by moving the sensor set angle between 0 degrees (left position), 4.5 degrees (center position), and 9 degrees (right position). The tests were conducted on three days, one day representing the following weather conditions: clear, rain, and snow. The LiDAR performance was evaluated by comparing the return point count and return point power intensity from the target vehicle. The camera performance was quantified using a YOLOv5 model to perform object detection inference, tracking the detection confidence, inaccurate classification count (type I error), and misclassification count (type II error) of the target vehicle. Overall, LiDAR showed power intensity reduction by 22.42% and 29.30% in rain and snow, respectively, while camera confidence results were not impacted by the mild weather conditions.