Temperature sensors may produce a measurement error of up to 1 °C because of the influence of solar radiation. In order to obtain a relatively minimal temperature error, a new temperature observation system was proposed in this paper for measuring surface air temperatures. Firstly, a radiation shield was designed with two aluminum plates, eight vents, and a multi-layer structure which is able to resist direct solar radiation, reflected radiation, and upwelling long-ware radiation, as well as ensuring the temperature sensor probe could work effectively. Then, the effect of different solar radiation intensities, wind speeds, scattered radiation intensities, long-wave radiation intensities, and underlying surface reflectivity levels on radiation error was calculated through a computational fluid dynamics (CFD) method. The mapping relationship was established between the various influencing factors and the solar radiation error. A back-propagation (BP) network algorithm was used to fit the discrete data obtained from the simulation to obtain the solar radiation error correction equation. Finally, the solar radiation error correction equation was verified. Outdoor experiments were conducted to confirm this system’s measurement accuracy. According to the experimental findings, the root-mean-square error was only 0.095 °C, which is a relatively high degree by which to reduce the temperature error. In addition, the average difference between the corrected value of the temperature observation system and the reference value was barely 0.084 °C.

Air temperature measurements in atmospheric environmental monitoring are susceptible to radiation-induced bias under natural ventilation. This study develops a low-power naturally ventilated air temperature sensor and a correction method combining computational fluid dynamics (CFD) with machine learning. The sensor integrates a Pt100 thin-film platinum resistance probe (Heraeus Holding GmbH, Hanau, Germany), symmetric guide plates, and a dual aluminum-plate radiation shield to reduce radiative heating while improving airflow around the probe. A three-dimensional fluid–solid coupled heat-transfer model was established in ANSYS FLUENT 15.0 to optimize guide-plate spacing and inclination angle and quantify the effects of solar radiation, long-wave radiation, scattered radiation, air density, wind speed, solar elevation angle, and surface albedo on radiation error. CFD results identified a guide-plate spacing of 24 mm and an inclination angle of 45° as the preferred parameters. A multilayer perceptron (MLP) model trained with CFD-derived data was validated in field experiments using a Model 076B aspirated radiation shield (Met One Instruments, Inc., Grants Pass, OR, USA) as the reference. The model predicted radiation error with a root mean square error (RMSE) of 0.052 °C, a mean absolute error (MAE) of 0.042 °C, and a correlation coefficient of 0.92. The proposed sensor and correction method provide a low-power and easy-to-maintain approach for reducing radiation-induced bias in naturally ventilated air-temperature measurements, with potential applications in meteorological observation, air-quality monitoring, and agricultural microclimate assessment.