Many methods for rendering weather and its lighting exist. The illumination and the appear­ance of weather are important when the weather particles function as adversarial attacks on optical flow. Optical flow methods are used for motion estimation by tracking object bound­aries. Weather can be a threat to the optical flow method by covering the edges of objects or creating an exceedingly high contrast between the image and the adverse weather. This can lead to misprediction in optical flow estimation. Hence, the adverse weather particles should be blended into the image environment and look realistic. In this bachelor thesis, we present illumination approaches to integrate the particle attacks better into the image. The basis for all the illumination methods is an environment map. The environment map is created from a single image and is used to simulate the light in a 3D scene. The mean color of the entire environment map has an impact on the particle colors. We extend this approach by using a modified version of alpha blending of the mean color to regulate the influence and the strength of the mean color. To further develop the first method with the overall mean color, we change the overall mean color to the mean color of the field of view of a particle. We then change the color of each particle with its mean color provided by the particles' field of view. Our illumination method integrates the particles better than the rendering from the existing framework. The particle color adapts to the color of the image environment. The realism of the particles depend on the image color, the transparency of the particle, and the particle type. When regulating the influence of the mean color, we can determine the appearance of the particle and preserve the original color by changing the influence of the mean color. This change in illumination helps to adapt the particles to the image environment while also improving the realism of the adverse weather.