文献类型：期刊文献

英文题名：Development of an Air Temperature Observation System Using a Radiation Shield and Neural Network Correction

作者：Li, Lin[1];Yuan, Keya[2];Chen, Yuan[3]

第一作者：李琳

通讯作者：Yuan, KY[1]

机构：[1]Beijing Union Univ, Coll Appl Sci & Technol, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[3]Nanjing Univ Informat Sci & Technol, Jiangsu Ind Technol Engn Ctr Environm & Meteorol I, Nanjing 210044, Peoples R China

第一机构：北京联合大学应用科技学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2026

卷号：26

期号：12

外文期刊名：SENSORS

收录：;Scopus(收录号：2-s2.0-105043396395);WOS:【SCI-EXPANDED(收录号:WOS:001803754800001)】；

基金：This research was funded by the National Key Research and Development Program of China, grant number 2022YFB4601100. The APC was funded by Keya Yuan.

语种：英文

外文关键词：radiation shield; air temperature observation; radiation-induced temperature deviation; computational fluid dynamics; neural network correction

摘要：Highlights What are the main findings? The proposed correction method significantly improved the accuracy of air temperature measurements by reducing radiation-induced errors. The proposed system achieves high-accuracy air temperature measurements under actual atmospheric conditions. What are the implications of the main findings? The proposed approach improves air temperature observation accuracy under strong solar radiation and weak ventilation conditions. Integrating radiation shielding and data-driven correction provides a scalable strategy for reliable meteorological measurements.Highlights What are the main findings? The proposed correction method significantly improved the accuracy of air temperature measurements by reducing radiation-induced errors. The proposed system achieves high-accuracy air temperature measurements under actual atmospheric conditions. What are the implications of the main findings? The proposed approach improves air temperature observation accuracy under strong solar radiation and weak ventilation conditions. Integrating radiation shielding and data-driven correction provides a scalable strategy for reliable meteorological measurements.Abstract Accurate air temperature observation requires minimizing solar radiation-induced deviations, which are strongly influenced by radiation shield performance. However, conventional shields often produce significant errors under strong solar radiation or weak ventilation. In this study, an air temperature observation system integrating a radiation shield and a backpropagation (BP) neural network-based correction method is proposed. Computational fluid dynamics (CFD) simulations were conducted to quantify radiation-induced temperature deviations under representative meteorological conditions, and the simulated dataset was used to train and test the neural network model. Initial field comparison experiments were performed using a 076B forced-ventilation system as a reference, where measured differences were treated as experimental deviations and model outputs as predicted deviations. The results show that, before correction, the proposed system exhibited a maximum deviation of 1.05 degrees C and a mean deviation of 0.26 degrees C, while the root mean square error and mean absolute error between experimental and predicted deviations were 0.30 degrees C and 0.23 degrees C, respectively. The correction significantly reduced temperature deviations, demonstrating the effectiveness of the proposed system in improving measurement accuracy.