## 1 Introduction  
In the rapidly growing field of automated mobility, optical sensors, particularly light detection and ranging (LiDAR), play a crucial role in advanced driving assistance systems (ADASs). Especially, solid-state LiDARs have emerged as a cost-effective and robust alternative to traditional mechanically scanning LiDARs, making them suitable for mass market adoption, not only with respect to the ADAS market, but also for smart city applications in the form of intelligent infrastructure units. However, LiDAR sensors are highly sensitive to precipitation and fog due to scattering in the near-infrared wavelength. Opposed to sparsely investigated longterm degradation over the sensor product lifecycle [1; 2; 3; 4], previous works have extensively studied and modeled the instantaneous impact of environmental effects using lab simulations [5; 6; 7; 8; 9; 10; 11; 12], field measurements [13; 14], or both [15; 16]. While lab simulations still fail to replicate natural precipitation types like snow and hail, field measurements provide more realistic data at the cost of longer measurement periods to accumulate a balanced dataset with respect to weather condition coverage [9; 16]. The primary focus of previous works has been to understand and evaluate LiDAR sensor performance and model data degradation caused by environmental effects for virtual validation of ADAS functionalities. In this contribution, a different approach is taken by investigating whether these degradation effects can be used to predict the current weather conditions. Such an inverse model could serve as a cost-effective solution for precise precipitation measurements and as a lightweight software feature in vehicles and infrastructure units, enabling advanced driver warnings and adaptive driving dynamics. Additionally, it could be utilized as a data quality indicator for weighted heterogeneous sensor data fusion or adaptive filtering for improved object detection [17]. Hence, in a previous work by the authors [18], a physics-informed deep learning (PIDL) method was developed to predict the comprehensive precipitation spectrum, specifically the particle size and velocity distribution (PSVD) from precipitation-induced degradation effects. The authors achieved a mean absolute error (MAE) as low as 2.4 particles per size and velocity bin, considering 440 bins. Based on that, the precipitation type could be classified, and characteristic quantities like the precipitation rate, attenuation coefficient, or meteorological visibility distance could be derived using known physical relations. The model was trained using data from a one-year in-field measurement campaign conducted with a low-cost automotive Flash LiDAR and correlated, extensive weather measurements [13]. However, the model was still lacking generalizability, since the PSVD prediction was relying on information from the background scenery, which makes the deployment in the vehicle or infrastructure challenging. Consequently, within this work, a novel pipeline is introduced which extracts the measurement noise from the first LiDAR return pulse in a preprocessing step in order to predict the occurring weather condition solely based on measurement noise (see Figure 1). The current weather state is, for the sake of generalizability, directly predicted from the extracted noise in an end-to-end approach, consisting of  
1. 15 standardized precipitation classes, corresponding to the World Meteorological Organization (WMO) Surface Synoptic Observations (SYNOP) standard [19]. 2. The respective precipitation rate (in mm/h) as quantitative, interpretable metric on the respective precipitation intensity, also correlating with the meteorologic visibility as demonstrated earlier [13]. For the corresponding classification and regression task, multiple deep neural network models are developed based on varying state-of-the-art architectures as well as model sizes, and benchmarked with respect to accuracy and computational efficiency.