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Fluid dynamics analysis and experimental study for solar radiation error correction of sounding humidity sensor
Jiahong Zhang1, Xiaolu Xie1, Qingquan Liu1
1Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China.
The Review of Scientific Instruments
|July 10, 2021
Summary
This study addresses solar radiation errors in capacitive humidity sensors like the HC103M2. Computational fluid dynamics (CFD) and a back propagation neural network model accurately predict and correct these errors, improving humidity measurement accuracy.
Area of Science:
- Atmospheric Science
- Instrument Engineering
- Sensor Technology
Background:
- Solar radiation significantly impacts the accuracy of capacitive sounding humidity sensors, leading to errors in relative humidity measurements.
- The HC103M2 is a mainstream capacitive sounding humidity sensor susceptible to solar radiation errors.
- Accurate humidity measurements are critical for various applications, including meteorology and climate monitoring.
Purpose of the Study:
- To investigate and correct the solar radiation error of the HC103M2 capacitive sounding humidity sensor.
- To develop and validate computational models for predicting solar radiation heating effects on humidity sensors.
- To improve the accuracy of relative humidity measurements by mitigating solar radiation-induced errors.
Main Methods:
- Utilized computational fluid dynamics (CFD) to simulate the dry error caused by solar radiation heating.
- Developed an experimental platform to verify CFD simulation accuracy by measuring solar radiation heating.
- Proposed a back propagation (BP) neural network fusion algorithm for predicting solar radiation heating based on key environmental parameters.
Main Results:
- CFD simulations showed good agreement with experimental data, with a maximum deviation of 3.30% and an average error of 1.94% in relative humidity.
- The BP neural network model achieved high accuracy in predicting solar radiation heating, with a maximum absolute error of approximately 0.2 K and a relative humidity error of ±1.30%.
- Corrected humidity profiles using the developed models showed closer agreement with reference instruments (RS92 and cryogenic frost point hygrometer).
Conclusions:
- The CFD method is accurate and feasible for investigating solar radiation errors in humidity sensors.
- The BP neural network fusion algorithm provides an operational method for predicting and correcting solar radiation heating effects.
- The developed models and correction strategies significantly improve the accuracy of humidity measurements, particularly in vertical profiles.
