A multi-physics field analysis of sounding temperature sensors based on computational fluid dynamics

Purpose Precise temperature measurements are crucial for understanding Earth’s energy balance and for accurately predicting future climate change. Therefore, atmospheric temperature observations using radiosonde sensors require enhanced accuracy, targeting measurements with a precision of 0.1 K or better. Design/methodology/approach First, temperature errors of radiosonde sensors were simulated using computational fluid dynamics (CFD) from sea level up to an altitude of 32 km. These simulations accounted for a range of environmental factors, including solar radiation intensity, solar radiation angle, air velocity and altitude (air density). A neural network algorithm was then applied to learn and model the CFD-derived temperature errors. Based on this, a temperature error correction algorithm for radiosonde sensors was developed. Findings Experimental results demonstrated that the average absolute error between the measured temperature errors and the values corrected using the algorithm was 0.019 K, with a root mean square error of 0.018 K and a correlation coefficient of 0.99. These findings suggest that the temperature error correction algorithm effectively reduces measurement errors to approximately 0.05 K. Social implications The widespread adoption of this technology can impact various aspects of society, including enhancing the overall quality of meteorological observation networks and providing more accurate meteorological data support for multiple fields, such as agriculture, disaster early warning, and public health. Originality/value This study focuses on developing a correction algorithm for radiation-induced errors in sounding temperature sensors by integrating CFD with neural network algorithm. This approach aims to enhance the accuracy of temperature observations from sounding sensors, minimizing biases caused by solar radiation. The improved precision in temperature measurements will contribute to more reliable historical temperature data, thereby supporting research in climate change by providing accurate datasets for long-term climate analysis.