Design of a hemispherical shell shaped natural ventilation temperature observation instrument

Area of Science:

• Meteorology and Atmospheric Science
• Instrument Design and Engineering

Background:

• Accurate atmospheric temperature measurement is crucial for various industries and research.
• Solar radiation introduces significant temperature errors (≥1°C) in conventional instruments.
• Existing instruments often suffer from heat pollution and inadequate air velocity.

Purpose of the Study:

• To design and optimize a novel hemispherical shell-shaped instrument for enhanced atmospheric temperature measurement.
• To develop a robust temperature error correction model using neural networks.
• To validate the instrument's performance and the correction model through field experiments.

Main Methods:

• Computational Fluid Dynamics (CFD) for instrument optimization and error quantification.
• Development of a multi-variable neural network model for temperature error correction.
• Field experiments to collect real-world performance data.

Main Results:

• The instrument design was optimized using CFD simulations.
• The neural network model effectively corrected temperature errors under varying conditions.
• Field experiments showed a mean error of 0.08°C and maximum error of 0.25°C before correction.
• The correction model reduced measurement errors to within -0.05°C to 0.04°C, achieving an accuracy of ~0.1°C.

Conclusions:

• The designed hemispherical instrument effectively mitigates solar-induced temperature errors.
• The neural network-based correction model significantly enhances measurement accuracy.
• The optimized instrument and correction system provide reliable and precise atmospheric temperature data.