Development of a surface atmosphere temperature observation instrument

Area of Science:

• Atmospheric Science
• Instrument Engineering
• Data Science

Background:

• Accurate atmospheric temperature observation is crucial for meteorological studies and climate monitoring.
• Radiation interference poses a significant challenge to the precision of traditional temperature sensors.
• Existing instruments often struggle to mitigate the impact of solar and thermal radiation.

Purpose of the Study:

• To design and validate a new natural ventilation temperature observation instrument.
• To minimize radiation errors affecting atmospheric temperature measurements.
• To develop a robust method for correcting radiation-induced inaccuracies.

Main Methods:

• Computational fluid dynamics (CFD) modeling was used to construct the instrument's thermal characteristics.
• Radiation error was quantified under various environmental conditions.
• A back propagation neural network (BPNN) algorithm was employed to create a multivariable radiation error correction equation.
• Field tests were conducted using a 076B forced ventilation device as a reference.

Main Results:

• The new natural ventilation instrument demonstrated a high degree of accuracy.
• The average error of the instrument was found to be 0.12°C.
• Excellent agreement with the reference device was observed, with a correlation coefficient of 0.999.

Conclusions:

• The developed natural ventilation instrument effectively minimizes radiation impact on temperature observations.
• The BPNN-based correction model significantly enhances measurement accuracy.
• This instrument offers a reliable solution for precise atmospheric temperature monitoring in diverse conditions.