Accurate modeling of radio wave propagation in mountainous environments is essential for wireless communication systems in applications such as emergency rescue and public safety. Based on propagation measurements from VHF to microwave bands, this paper proposes a terrain-augmented close-in (TA-CI) model. The model incorporates two terrain features extracted from high-resolution digital elevation models (DEMs): the maximum terrain clearance height (h_c) and the maximum diffraction angle (θ_max). By comparing the model predictions at 83 MHz, 235 MHz, 632 MHz, 1.2 GHz, and 5.8 GHz, the proposed TA-CI model achieves higher prediction accuracy than both the close-in (CI) model and the floating-intercept (FI) model in the coastal mountainous area of northern China and the plateau mountainous area of southern China. The root-mean-square prediction error is up to 33.9% lower than that of the CI model. The results of this work provide a propagation model for path loss prediction from VHF to microwave bands in mountainous environments.

Radio noise at mountainous observatories affects the deployment of radio observation equipment. Existing mountainous-station measurements have mainly focused on spectrum occupancy and interference amplitude, while the statistical properties of the noise remain insufficiently addressed. To fill this gap, this study analyzes both the Gaussian and impulsive components of the noise. Radio noise in the 30–500 MHz band was measured at a high-altitude mountainous station in southwestern China using a USRP X310 software-defined radio. For the Gaussian component, the measured noise figure exceeds the ITU-R P.372-17 model and decreases with frequency. A smoothing spline model achieved an RMSE of 0.8687, an MAE of 0.3843, and an R² of 0.9929, outperforming the ITU-R P.372-17 model. For the impulsive component, pulse events were concentrated in the 30–140 MHz and 200–270 MHz bands. An XGBoost regression model was trained to predict IN level density and pulse length from statistical features and was benchmarked against linear, polynomial, and random forest baselines under leave-one-time-block-out cross-validation. These results provide empirical data and references for radio service deployment at mountainous observation stations.