## Appendix A
### Comprehensive Discussion on the Distinctions Between Synthetic and Real Adverse Weather Data  
**Structural differences in data:** In terms of physical characteristics, adverse weather conditions are typically simulated algorithmically by adding noise, texture overlays, or optical models. Weather elements such as raindrops, snowflakes, and fog particles are generated with predefined rules governing their shape, density, and motion trajectories, which may result in patterned features. For instance, synthetic rain often consists of uniformly distributed, regularly shaped elements (ellipses or line segments), lacking the size variations and angular deviations caused by factors like wind speed and air humidity in real rain. In contrast, actual rain exhibits wind-driven raindrops with irregular shapes and slanted trajectories, along with dynamically changing details such as reflections from puddles and wiper marks on vehicle windshields.  
In terms of coherence between scene elements, simulated adverse weather conditions often process different components (vehicles, roads) independently, potentially leading to semantic inconsistencies. For example, in synthetic heavy rain scenarios, vehicles may lack activated windshield wipers or headlights, and road surface reflections may fail to correspond with rainfall intensity. Additionally, realistic lighting effects such as light halos and scattering from street lamps or vehicle headlights on rainy nights could be overlooked. In contrast, real adverse weather naturally maintains logical relationships between all scene elements, following the physical laws.  
**Distributional differences in data:** In terms of feature continuity, adverse weather parameters in synthetic data, such as rainfall intensity, fog density, and illumination strength, are typically discretized into limited levels with a relatively narrow variable range. In contrast, real-world data exhibit weather characteristics that follow continuous and diversified distributions. For instance, authentic low-light conditions may encompass diverse scenarios like dawn, dusk, or tunnel interiors--each presenting distinct spectral distributions and noise patterns.  
From a temporal perspective, synthetic data predominantly consist of static single-frame images lacking sequential continuity. For instance, in synthetic snowfall simulations, snowflake trajectories are typically fixed, failing to replicate the temporal dynamics of real snowfall processes such as snow accumulation, melting, or wind-driven redistribution.  
From the perspective of long-tail distribution in data, real-world datasets may encompass various long-tail cases for the same factor. For example, in the case of rain, there could exist extreme scenarios like torrential downpours causing severe road flooding that partially submerges vehicle contours--extreme conditions that are rarely represented in synthetic data.  
**Differences in experimental methodology:** Due to the limited variety and quantity of real-world data samples, our objective was not to directly compare them with synthetic data, but rather to use them as supplementary validation for the effectiveness of the synthetic data. Therefore, identical experimental methods and metrics were applied to both datasets.  
In summary, an inevitable gap exists between real and synthetic data, which is a long-standing challenge in the field of image enhancement that remains imperfectly resolved. However, this issue is not central to our current study. At present, this limitation persists across all related research. In future work, we will investigate methods to minimize the impact of such discrepancies.