References：

     [1] Ma, B., Tian, J. C., He, J. Y., et al. Mechanism of dew formation in the arid zone of central Ningxia and its impact on surface soil moisture [J]. Advances in Water Science, 2022, 33(6): 955-966.
     [2] Wang, Y., Qiu, X. L., Li, Y. X., et al. Risk assessment of forest and fruit yield reduction caused by frost damage based on phenological period: a case study of Hebei Province [J]. Chinese Journal of Agrometeorology, 2022, 4(10): 821-831.
     [3] Zhang, B., Sun, S. S., Ding, L. G., et al. Hazard analysis and zoning of spring tea frost damage in Guizhou [J]. Journal of Meteorology and Environment, 2023, 39(5): 99-105.
     [4] Bao, L. L., Cheng, P., Wang, X. Y., et al. Road icing early warning in Gansu Province based on Logistic regression and neural network [J]. Journal of Arid Meteorology, 2024, 42(1): 137-145.
     [5] Zhang, H. F., Lu, S., Shen, J. J., et al. Spatiotemporal variation characteristics of road icing in Shaanxi and its risk early warning model [J]. Journal of Arid Meteorology, 2020, 38(5): 878-885.
     [6] Song, P., Che, J. H., Guo, T. T., et al. Low-temperature climatic characteristics and SVM prediction model of the highway pavement around Jiaozhou Bay [J]. Journal of Marine Meteorology, 2023, 43(3): 80-87.
     [7] Wang, K. X., Bao, Y. X., Zhu, C. Y., et al. Application of random forest regression in winter pavement temperature prediction [J]. Meteorological Monthly, 2021, 47(1): 82-93.
     [8] Zou, L. J., Liu, S., Lu, Q. J. Pavement temperature model and icing potential based on neural network [J]. Highway, 2022, 67(10): 409-414.
     [9] Zhang, Q. K., Xiang, Y., Ji, Z. M., et al. Climatic characteristics and trend analysis of icing phenomena in Anhui Province in recent 55 years [J]. Journal of Natural Disasters, 2020, 29(6): 218-226.
     [10] Hua, L. S., Wen, H. Y., Zhu, H. L., et al. Discussion on automatic observation model of frost formation based on Bayesian discrimination method [J]. Meteorological Monthly, 2015, 41(8): 964-969.
     [11] China Meteorological Administration. Specifications for Surface Meteorological Observations [M]. Beijing: China Meteorological Press, 2003: 21-27.
     [12] Wen, H. Y., Zhu, H. L., Ma, W. Z., et al. Correction of icing phenomenon data series based on Bayesian discrimination method [J]. Meteorological Monthly, 2021, 47(9): 1113-1121.
     [13] Shi, C. C. Pavement icing monitoring and early warning system based on machine vision [J]. Electronic Design Engineering, 2025, 33(6): 34-38.
     [14] Zhao, X. K., Hu, Z., Zhang, J. P., et al. Research progress on intelligent monitoring of pavement icing based on optical fiber sensing technology [J]. Journal of Jilin University (Engineering and Technology Edition), 2023, 53(6): 1566-1579.
     [15] Xie, Q. Z., Wang, L., Ge, J. Y., et al. Design of polymer optical fiber icing sensor and method for detecting pavement ice layer thickness [J]. Journal of China & Foreign Highway, 2023, 43(4): 59-67.
     [16] Ma, S. Q., Wu, K. J., Chen, D. D., et al. Design of automatic observation system for weather phenomena [J]. Meteorological Monthly, 2011, 37(9): 1166-1172.
     [17] Ran, B., Zhang, Z. Y., Yang, J. B., et al. Formation law of condensed water on Artemisia ordosica in the Mu Us Sandy Land and its impact on water balance [J]. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(8): 111-119.